



import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     MegatronBertForCausalLM,
     MegatronBertForMaskedLM,
     MegatronBertForMultipleChoice,
     MegatronBertForNextSentencePrediction,
     MegatronBertForPreTraining,
     MegatronBertForQuestionAnswering,
     MegatronBertForSequenceClassification,
     MegatronBertForTokenClassification,
     MegatronBertModel,
 )

class    _A	:



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=13					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=7					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					str=True					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=True					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=99					,		__SCREAMING_SNAKE_CASE							:					Dict=64					,		__SCREAMING_SNAKE_CASE							:					str=32					,		__SCREAMING_SNAKE_CASE							:					str=5					,		__SCREAMING_SNAKE_CASE							:					int=4					,		__SCREAMING_SNAKE_CASE							:					str=37					,		__SCREAMING_SNAKE_CASE							:					str="gelu"					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.1					,		__SCREAMING_SNAKE_CASE							:					str=0.1					,		__SCREAMING_SNAKE_CASE							:					Any=512					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=16					,		__SCREAMING_SNAKE_CASE							:					List[Any]=2					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.02					,		__SCREAMING_SNAKE_CASE							:					Tuple=3					,		__SCREAMING_SNAKE_CASE							:					int=4					,		__SCREAMING_SNAKE_CASE							:					Dict=None					,		):


           '''simple docstring'''

           __a							=    parent
           __a							=    batch_size
           __a							=    seq_length
           __a							=    is_training
           __a							=    use_input_mask
           __a							=    use_token_type_ids
           __a							=    use_labels
           __a							=    vocab_size
           __a							=    hidden_size
           __a							=    embedding_size
           __a							=    num_hidden_layers
           __a							=    num_attention_heads
           __a							=    intermediate_size
           __a							=    hidden_act
           __a							=    hidden_dropout_prob
           __a							=    attention_probs_dropout_prob
           __a							=    max_position_embeddings
           __a							=    type_vocab_size
           __a							=    type_sequence_label_size
           __a							=    initializer_range
           __a							=    num_labels
           __a							=    num_choices
           __a							=    scope



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size)

           __a							=    None
           if self.use_input_mask:
               __a							=    random_attention_mask([self.batch_size, self.seq_length])

           __a							=    None
           if self.use_token_type_ids:
               __a							=    ids_tensor([self.batch_size, self.seq_length]					,		self.type_vocab_size)

           __a							=    None
           __a							=    None
           __a							=    None
           if self.use_labels:
               __a							=    ids_tensor([self.batch_size]					,		self.type_sequence_label_size)
               __a							=    ids_tensor([self.batch_size, self.seq_length]					,		self.num_labels)
               __a							=    ids_tensor([self.batch_size]					,		self.num_choices)

           __a							=    self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return MegatronBertConfig(
               vocab_size=self.vocab_size					,		hidden_size=self.hidden_size					,		num_hidden_layers=self.num_hidden_layers					,		num_attention_heads=self.num_attention_heads					,		intermediate_size=self.intermediate_size					,		embedding_size=self.embedding_size					,		hidden_act=self.hidden_act					,		hidden_dropout_prob=self.hidden_dropout_prob					,		attention_probs_dropout_prob=self.attention_probs_dropout_prob					,		max_position_embeddings=self.max_position_embeddings					,		type_vocab_size=self.type_vocab_size					,		is_decoder=__SCREAMING_SNAKE_CASE					,		initializer_range=self.initializer_range					,		)



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    MegatronBertModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE)
           __a							=    model(__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE)
           __a							=    model(__SCREAMING_SNAKE_CASE)

           self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, self.seq_length, self.hidden_size))
           self.parent.assertEqual(result.pooler_output.shape					,		(self.batch_size, self.hidden_size))



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    MegatronBertForMaskedLM(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.vocab_size))



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    MegatronBertForCausalLM(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.vocab_size))



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    MegatronBertForNextSentencePrediction(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(
               __SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE					,		)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, 2))



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    MegatronBertForPreTraining(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(
               __SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE					,		next_sentence_label=__SCREAMING_SNAKE_CASE					,		)
           self.parent.assertEqual(result.prediction_logits.shape					,		(self.batch_size, self.seq_length, self.vocab_size))
           self.parent.assertEqual(result.seq_relationship_logits.shape					,		(self.batch_size, 2))



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           __a							=    MegatronBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(
               __SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		start_positions=__SCREAMING_SNAKE_CASE					,		end_positions=__SCREAMING_SNAKE_CASE					,		)
           self.parent.assertEqual(result.start_logits.shape					,		(self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape					,		(self.batch_size, self.seq_length))



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    self.num_labels
           __a							=    MegatronBertForSequenceClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_labels))



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.num_labels
           __a							=    MegatronBertForTokenClassification(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.num_labels))



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    self.num_choices
           __a							=    MegatronBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    input_ids.unsqueeze(1).expand(-1					,		self.num_choices					,		-1).contiguous()
           __a							=    token_type_ids.unsqueeze(1).expand(-1					,		self.num_choices					,		-1).contiguous()
           __a							=    input_mask.unsqueeze(1).expand(-1					,		self.num_choices					,		-1).contiguous()
           __a							=    model(
               __SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		token_type_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE					,		)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_choices))





       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           (
               (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       
           )							=    config_and_inputs
           __a							=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict


@require_torch
class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   List[str]						 =						(
           (
               MegatronBertModel,
               MegatronBertForMaskedLM,
               MegatronBertForCausalLM,
               MegatronBertForMultipleChoice,
               MegatronBertForNextSentencePrediction,
               MegatronBertForPreTraining,
               MegatronBertForQuestionAnswering,
               MegatronBertForSequenceClassification,
               MegatronBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase__  :   str						 =						(
           {
               '''feature-extraction''': MegatronBertModel,
               '''fill-mask''': MegatronBertForMaskedLM,
               '''question-answering''': MegatronBertForQuestionAnswering,
               '''text-classification''': MegatronBertForSequenceClassification,
               '''text-generation''': MegatronBertForCausalLM,
               '''token-classification''': MegatronBertForTokenClassification,
               '''zero-shot''': MegatronBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase__  :   Dict						 =						True
       # test_resize_embeddings = False
       UpperCamelCase__  :   Tuple						 =						False



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					int=False):


           '''simple docstring'''

           __a							=    super()._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		return_labels=__SCREAMING_SNAKE_CASE)

           if return_labels:
               if model_class in get_values(__SCREAMING_SNAKE_CASE):
                   __a							=    torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)					,		dtype=torch.long					,		device=__SCREAMING_SNAKE_CASE)
                   __a							=    torch.zeros(
                       self.model_tester.batch_size					,		dtype=torch.long					,		device=__SCREAMING_SNAKE_CASE)
           return inputs_dict



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    MegatronBertModelTester(self)
           __a							=    ConfigTester(self					,		config_class=__SCREAMING_SNAKE_CASE					,		hidden_size=37)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_model(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_pretraining(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_question_answering(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_token_classification(*__SCREAMING_SNAKE_CASE)





def 					__snake_case     (      _UpperCAmelCase    ):
    return torch.tensor(
        _UpperCAmelCase       ,			dtype=torch.long       ,			device=_UpperCAmelCase       ,			)


__snake_case						:Any						=					1E-4

@require_torch
@require_sentencepiece
@require_tokenizers
class    _A	(     unittest.TestCase   ):



       @slow
       @unittest.skip('''Model is not available.''')
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    '''nvidia/megatron-bert-uncased-345m'''
           if "MYDIR" in os.environ:
               __a							=    os.path.join(os.environ['''MYDIR''']					,		__SCREAMING_SNAKE_CASE)
           __a							=    MegatronBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.half()
           __a							=    _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]])
           with torch.no_grad():
               __a							=    model(__SCREAMING_SNAKE_CASE)[0]
           __a							=    torch.Size((1, 9, 1_024))
           self.assertEqual(output.shape					,		__SCREAMING_SNAKE_CASE)

           __a							=    [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
           for ii in range(3):
               for jj in range(3):
                   __a							=    output[0, ii, jj]
                   __a							=    expected[3 * ii + jj]
                   __a							=    '''ii={} jj={} a={} b={}'''.format(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   self.assertTrue(math.isclose(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		rel_tol=__SCREAMING_SNAKE_CASE					,		abs_tol=__SCREAMING_SNAKE_CASE)					,		msg=__SCREAMING_SNAKE_CASE)










from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
 from transformers import (
     TFBertModel,
     TFCLIPVisionModel,
     TFDeiTModel,
     TFRobertaModel,
     TFVisionTextDualEncoderModel,
     TFViTModel,
     VisionTextDualEncoderConfig,
 )

if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor





def 					__snake_case     (      _UpperCAmelCase    ):
    if isinstance(_UpperCAmelCase       ,			collections.abc.Iterable    ):
        return x
    return (x, x)

@require_tf
class    _A	:



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a							=    VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           __a							=    TFVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

           self.assertEqual(output['''text_embeds'''].shape					,		(input_ids.shape[0], config.projection_dim))
           self.assertEqual(output['''image_embeds'''].shape					,		(pixel_values.shape[0], config.projection_dim))



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]=None					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

           self.assertEqual(output['''text_embeds'''].shape					,		(input_ids.shape[0], model.config.projection_dim))
           self.assertEqual(output['''image_embeds'''].shape					,		(pixel_values.shape[0], model.config.projection_dim))



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    {'''vision_model''': vision_model, '''text_model''': text_model}
           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

           self.assertEqual(output['''text_embeds'''].shape					,		(input_ids.shape[0], model.config.projection_dim))
           self.assertEqual(output['''image_embeds'''].shape					,		(pixel_values.shape[0], model.config.projection_dim))



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Dict=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)
           __a							=    output[0].numpy()

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a							=    TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)
               __a							=    after_output[0].numpy()
               __a							=    np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-5)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(
               input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		output_attentions=__SCREAMING_SNAKE_CASE)

           __a							=    output.vision_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		vision_config.num_hidden_layers)

           # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
           __a							=    to_atuple(vision_model.config.image_size)
           __a							=    to_atuple(vision_model.config.patch_size)
           __a							=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
           __a							=    num_patches + 1
           self.assertEqual(vision_attentions[0].shape[-3:]					,		(vision_config.num_attention_heads, seq_len, seq_len))

           __a							=    output.text_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		text_config.num_hidden_layers)

           self.assertEqual(
               text_attentions[0].shape[-3:]					,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,		)



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					np.ndarray					,		__SCREAMING_SNAKE_CASE							:					np.ndarray					,		__SCREAMING_SNAKE_CASE							:					float):


           '''simple docstring'''

           __a							=    np.abs((a - b)).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		F'Difference between torch and flax is {diff} (>= {tol}).')



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_model(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_save_load(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a	,       __a							=    self.get_pretrained_model_and_inputs()

           __a							=    model_a(**__SCREAMING_SNAKE_CASE)
           __a							=    outputs[0].numpy()

           with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a							=    TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a							=    model_a(**__SCREAMING_SNAKE_CASE)
               __a							=    after_outputs[0].numpy()
               __a							=    np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-5)


@require_tf
class    _A	(     __UpperCAmelCase				,unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               '''hf-internal-testing/tiny-random-vit'''					,		'''hf-internal-testing/tiny-random-bert''')
           __a							=    13
           __a							=    floats_tensor(
               [
                   batch_size,
                   model.vision_model.config.num_channels,
                   model.vision_model.config.image_size,
                   model.vision_model.config.image_size,
               ])
           __a							=    ids_tensor([batch_size, 4]					,		model.text_model.config.vocab_size)
           __a							=    random_attention_mask([batch_size, 4])
           __a							=    {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

           return model, inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    TFViTModel(__SCREAMING_SNAKE_CASE					,		name='''vision_model''')
           __a							=    TFBertModel(__SCREAMING_SNAKE_CASE					,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    TFViTModelTester(self)
           __a							=    TFBertModelTester(self)
           __a							=    vit_model_tester.prepare_config_and_inputs()
           __a							=    bert_model_tester.prepare_config_and_inputs()

           __a	,       __a	,       __a							=    vision_config_and_inputs

           (
               (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       
           )							=    text_config_and_inputs

           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": input_mask,
               "input_ids": input_ids,
               "text_token_type_ids": token_type_ids,
               "text_sequence_labels": sequence_labels,
               "text_token_labels": token_labels,
               "text_choice_labels": choice_labels,
           }


@require_tf
class    _A	(     __UpperCAmelCase				,unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               '''Rocketknight1/tiny-random-deit-tf'''					,		'''hf-internal-testing/tiny-random-roberta''')
           __a							=    13
           __a							=    floats_tensor(
               [
                   batch_size,
                   model.vision_model.config.num_channels,
                   model.vision_model.config.image_size,
                   model.vision_model.config.image_size,
               ])
           __a							=    ids_tensor([batch_size, 4]					,		model.text_model.config.vocab_size)
           __a							=    random_attention_mask([batch_size, 4])
           __a							=    {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

           return model, inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None					,		**__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(
               input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		output_attentions=__SCREAMING_SNAKE_CASE)

           __a							=    output.vision_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		vision_config.num_hidden_layers)

           # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
           __a							=    to_atuple(vision_model.config.image_size)
           __a							=    to_atuple(vision_model.config.patch_size)
           __a							=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
           __a							=    num_patches + 2
           self.assertEqual(vision_attentions[0].shape[-3:]					,		(vision_config.num_attention_heads, seq_len, seq_len))

           __a							=    output.text_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		text_config.num_hidden_layers)

           self.assertEqual(
               text_attentions[0].shape[-3:]					,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,		)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    TFDeiTModel(__SCREAMING_SNAKE_CASE					,		name='''vision_model''')
           __a							=    TFRobertaModel(__SCREAMING_SNAKE_CASE					,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    TFDeiTModelTester(self)
           __a							=    TFRobertaModelTester(self)
           __a							=    vit_model_tester.prepare_config_and_inputs()
           __a							=    bert_model_tester.prepare_config_and_inputs()

           __a	,       __a	,       __a							=    vision_config_and_inputs

           (
               (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       
           )							=    text_config_and_inputs

           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": input_mask,
               "input_ids": input_ids,
               "text_token_type_ids": token_type_ids,
               "text_sequence_labels": sequence_labels,
               "text_token_labels": token_labels,
               "text_choice_labels": choice_labels,
           }


@require_tf
class    _A	(     __UpperCAmelCase				,unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               '''Rocketknight1/tiny-random-clip-tf'''					,		'''hf-internal-testing/tiny-random-bert''')
           __a							=    13
           __a							=    floats_tensor(
               [
                   batch_size,
                   model.vision_model.config.num_channels,
                   model.vision_model.config.image_size,
                   model.vision_model.config.image_size,
               ])
           __a							=    ids_tensor([batch_size, 4]					,		model.text_model.config.vocab_size)
           __a							=    random_attention_mask([batch_size, 4])
           __a							=    {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

           return model, inputs



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    TFCLIPVisionModel(__SCREAMING_SNAKE_CASE					,		name='''vision_model''')
           __a							=    TFBertModel(__SCREAMING_SNAKE_CASE					,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    TFCLIPVisionModelTester(self)
           __a							=    TFBertModelTester(self)
           __a							=    clip_model_tester.prepare_config_and_inputs()
           __a							=    bert_model_tester.prepare_config_and_inputs()

           __a	,       __a							=    vision_config_and_inputs

           (
               (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       (
               __a
           )	,       
           )							=    text_config_and_inputs

           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": input_mask,
               "input_ids": input_ids,
               "text_token_type_ids": token_type_ids,
               "text_sequence_labels": sequence_labels,
               "text_token_labels": token_labels,
               "text_choice_labels": choice_labels,
           }


@require_vision
@require_tf
class    _A	(     unittest.TestCase   ):



       @slow
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_pretrained(
               '''clip-italian/clip-italian'''					,		logit_scale_init_value=1.0					,		from_pt=__SCREAMING_SNAKE_CASE)
           __a							=    VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''')

           __a							=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           __a							=    processor(
               text=['''una foto di un gatto''', '''una foto di un cane''']					,		images=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')

           __a							=    model(**__SCREAMING_SNAKE_CASE)

           # verify the logits
           self.assertEqual(outputs.logits_per_image.shape					,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
           self.assertEqual(
               outputs.logits_per_text.shape					,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])					,		)

           __a							=    np.array([[1.2_28_47_27, 0.3_10_41_22]])

           self.assertTrue(np.allclose(outputs.logits_per_image.numpy()					,		__SCREAMING_SNAKE_CASE					,		atol=1E-3))





