



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 __snake_case						:Dict						=					'''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class    _A	:
       UpperCamelCase__  :   List[str]						 =						PegasusConfig
       UpperCamelCase__  :   Dict						 =						{}
       UpperCamelCase__  :   Optional[Any]						 =						'''gelu'''



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict=13					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=7					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=False					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=99					,		__SCREAMING_SNAKE_CASE							:					List[str]=32					,		__SCREAMING_SNAKE_CASE							:					Any=5					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=4					,		__SCREAMING_SNAKE_CASE							:					List[str]=37					,		__SCREAMING_SNAKE_CASE							:					int=0.1					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=20					,		__SCREAMING_SNAKE_CASE							:					int=2					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0					,		):


           '''simple docstring'''

           __a							=    parent
           __a							=    batch_size
           __a							=    seq_length
           __a							=    is_training
           __a							=    use_labels
           __a							=    vocab_size
           __a							=    hidden_size
           __a							=    num_hidden_layers
           __a							=    num_attention_heads
           __a							=    intermediate_size

           __a							=    hidden_dropout_prob
           __a							=    attention_probs_dropout_prob
           __a							=    max_position_embeddings
           __a							=    eos_token_id
           __a							=    pad_token_id
           __a							=    bos_token_id



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    ids_tensor([self.batch_size, self.seq_length - 1]					,		self.vocab_size).clip(3					,		self.vocab_size)
           __a							=    np.expand_dims(np.array([self.eos_token_id] * self.batch_size)					,		1)
           __a							=    np.concatenate([input_ids, eos_tensor]					,		axis=1)

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

           __a							=    self.config_cls(
               vocab_size=self.vocab_size					,		d_model=self.hidden_size					,		encoder_layers=self.num_hidden_layers					,		decoder_layers=self.num_hidden_layers					,		encoder_attention_heads=self.num_attention_heads					,		decoder_attention_heads=self.num_attention_heads					,		encoder_ffn_dim=self.intermediate_size					,		decoder_ffn_dim=self.intermediate_size					,		dropout=self.hidden_dropout_prob					,		attention_dropout=self.attention_probs_dropout_prob					,		max_position_embeddings=self.max_position_embeddings					,		eos_token_ids=[2]					,		bos_token_id=self.bos_token_id					,		pad_token_id=self.pad_token_id					,		decoder_start_token_id=self.pad_token_id					,		**self.config_updates					,		)
           __a							=    prepare_pegasus_inputs_dict(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return config, inputs_dict



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


           '''simple docstring'''

           __a							=    20
           __a							=    model_class_name(__SCREAMING_SNAKE_CASE)

           __a							=    model.encode(inputs_dict['''input_ids'''])

           __a	,       __a							=    (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           __a							=    model.init_cache(decoder_input_ids.shape[0]					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,		dtype='''i4''')

           __a							=    jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,		)
           __a							=    model.decode(
               decoder_input_ids[:, :-1]					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		past_key_values=__SCREAMING_SNAKE_CASE					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)

           __a							=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,		dtype='''i4''')
           __a							=    model.decode(
               decoder_input_ids[:, -1:]					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		past_key_values=outputs_cache.past_key_values					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)

           __a							=    model.decode(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           __a							=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1E-3					,		msg=F'Max diff is {diff}')





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


           '''simple docstring'''

           __a							=    20
           __a							=    model_class_name(__SCREAMING_SNAKE_CASE)

           __a							=    model.encode(inputs_dict['''input_ids'''])

           __a	,       __a							=    (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           __a							=    jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
               ]					,		axis=-1					,		)

           __a							=    model.init_cache(decoder_input_ids.shape[0]					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,		)

           __a							=    model.decode(
               decoder_input_ids[:, :-1]					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		past_key_values=__SCREAMING_SNAKE_CASE					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)
           __a							=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,		dtype='''i4''')
           __a							=    model.decode(
               decoder_input_ids[:, -1:]					,		__SCREAMING_SNAKE_CASE					,		past_key_values=outputs_cache.past_key_values					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		decoder_position_ids=__SCREAMING_SNAKE_CASE					,		)

           __a							=    model.decode(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE)

           __a							=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1E-3					,		msg=F'Max diff is {diff}')





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			):
    if attention_mask is None:
        __a							=    np.not_equal(_UpperCAmelCase       ,			config.pad_token_id    ).astype(np.inta    )
    if decoder_attention_mask is None:
        __a							=    np.concatenate(
            [
                np.ones(decoder_input_ids[:, :1].shape       ,			dtype=np.inta    ),
                np.not_equal(decoder_input_ids[:, 1:]       ,			config.pad_token_id    ).astype(np.inta    ),
            ]       ,			axis=-1       ,			)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }

@require_flax
class    _A	(     __UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Dict						 =						(
           (
               FlaxPegasusForConditionalGeneration,
               FlaxPegasusModel,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase__  :   Any						 =						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
       UpperCamelCase__  :   str						 =						True
       UpperCamelCase__  :   Dict						 =						False
       UpperCamelCase__  :   Dict						 =						False
       UpperCamelCase__  :   Tuple						 =						False



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    FlaxPegasusModelTester(self)
           __a							=    ConfigTester(self					,		config_class=__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   __a							=    self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    model_class(__SCREAMING_SNAKE_CASE)

                   @jax.jit
                   def encode_jitted(__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple=None					,		**__SCREAMING_SNAKE_CASE							:					int):
                       return model.encode(input_ids=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

                   with self.subTest('''JIT Enabled'''):
                       __a							=    encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   with self.subTest('''JIT Disabled'''):
                       with jax.disable_jit():
                           __a							=    encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		len(__SCREAMING_SNAKE_CASE))
                   for jitted_output, output in zip(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                       self.assertEqual(jitted_output.shape					,		output.shape)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   __a							=    model_class(__SCREAMING_SNAKE_CASE)
                   __a							=    model.encode(inputs_dict['''input_ids''']					,		inputs_dict['''attention_mask'''])

                   __a							=    {
                       '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                       '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                       '''encoder_outputs''': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					int):
                       return model.decode(
                           decoder_input_ids=__SCREAMING_SNAKE_CASE					,		decoder_attention_mask=__SCREAMING_SNAKE_CASE					,		encoder_outputs=__SCREAMING_SNAKE_CASE					,		)

                   with self.subTest('''JIT Enabled'''):
                       __a							=    decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   with self.subTest('''JIT Disabled'''):
                       with jax.disable_jit():
                           __a							=    decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		len(__SCREAMING_SNAKE_CASE))
                   for jitted_output, output in zip(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                       self.assertEqual(jitted_output.shape					,		output.shape)



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


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               __a							=    model_class_name.from_pretrained('''google/pegasus-large'''					,		from_pt=__SCREAMING_SNAKE_CASE)
               __a							=    np.ones((1, 1))
               __a							=    model(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
           __a							=    PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')

           __a							=    [
               ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
               ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
           ]

           __a							=    [
               '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
               '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
           ]

           __a							=    tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors='''np'''					,		truncation=__SCREAMING_SNAKE_CASE					,		max_length=512					,		padding=__SCREAMING_SNAKE_CASE)
           __a							=    model.generate(**__SCREAMING_SNAKE_CASE					,		num_beams=2).sequences
           __a							=    tokenizer.batch_decode(__SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE)
           assert tgt_text == decoded










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))





