

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	if isinstance(lowerCamelCase__     ,			collections.abc.Iterable      ):
		return x
	return (x, x)






@require_tf
class 	A_		:
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int):
								pass
							def    lowerCAmelCase					(		self		:      Any):
								pass
							def    lowerCAmelCase					(		self		:      Dict):
								pass
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[Any]=None       ,**SCREAMING_SNAKE_CASE__		:      Any):
								__lowerCamelCase    :    Optional[Any] 	=			VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Tuple 	=			TFVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[Any] 	=			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		:      str       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any=None       ,**SCREAMING_SNAKE_CASE__		:      List[str]):
								__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			self.get_vision_text_model(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    int 	=			TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__       ,text_model=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			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		:      Any       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      int=None       ,**SCREAMING_SNAKE_CASE__		:      Dict):
								__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			self.get_vision_text_model(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			{'vision_model': vision_model, 'text_model': text_model}
								__lowerCamelCase    :    Optional[int] 	=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[Any] 	=			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[Any]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]=None       ,**SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.get_vision_text_model(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__       ,text_model=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			model(input_ids=SCREAMING_SNAKE_CASE__       ,pixel_values=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			output[0].numpy()

								with tempfile.TemporaryDirectory() as tmpdirname:
									model.save_pretrained(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    List[Any] 	=			TFVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[Any] 	=			model(input_ids=SCREAMING_SNAKE_CASE__       ,pixel_values=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[Any] 	=			after_output[0].numpy()
									__lowerCamelCase    :    List[str] 	=			np.amax(np.abs(out_a - out_a))
									self.assertLessEqual(SCREAMING_SNAKE_CASE__       ,1E-5)
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      str=None       ,**SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.get_vision_text_model(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__       ,text_model=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			model(
								    input_ids=SCREAMING_SNAKE_CASE__       ,pixel_values=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__       ,output_attentions=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[Any] 	=			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)
								__lowerCamelCase    :    str 	=			to_atuple(vision_model.config.image_size)
								__lowerCamelCase    :    Optional[Any] 	=			to_atuple(vision_model.config.patch_size)
								__lowerCamelCase    :    Union[str, Any] 	=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								__lowerCamelCase    :    Tuple 	=			num_patches + 1
								self.assertEqual(vision_attentions[0].shape[-3:]       ,(vision_config.num_attention_heads, seq_len, seq_len))

								__lowerCamelCase    :    Optional[int] 	=			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		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      float):
								__lowerCamelCase    :    Union[str, Any] 	=			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		:      List[str]):
								__lowerCamelCase    :    str 	=			self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_model(**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Tuple 	=			self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Dict 	=			self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    List[str] 	=			self.prepare_config_and_inputs()
								self.check_save_load(**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    List[str] 	=			self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**SCREAMING_SNAKE_CASE__)


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			self.get_pretrained_model_and_inputs()

								__lowerCamelCase    :    Tuple 	=			model_a(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			outputs[0].numpy()

								with tempfile.TemporaryDirectory() as tmp_dirname:
									model_a.save_pretrained(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			TFVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Tuple 	=			model_a(**SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			after_outputs[0].numpy()
									__lowerCamelCase    :    Tuple 	=			np.amax(np.abs(out_a - out_a))
									self.assertLessEqual(SCREAMING_SNAKE_CASE__       ,1E-5)






@require_tf
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
								    'hf-internal-testing/tiny-random-vit'       ,'hf-internal-testing/tiny-random-bert')
								__lowerCamelCase    :    List[Any] 	=			1_3
								__lowerCamelCase    :    int 	=			floats_tensor(
								    [
								        batch_size,
								        model.vision_model.config.num_channels,
								        model.vision_model.config.image_size,
								        model.vision_model.config.image_size,
								    ])
								__lowerCamelCase    :    Tuple 	=			ids_tensor([batch_size, 4]       ,model.text_model.config.vocab_size)
								__lowerCamelCase    :    Optional[int] 	=			random_attention_mask([batch_size, 4])
								__lowerCamelCase    :    List[Any] 	=			{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

								return model, inputs
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[str]):
								__lowerCamelCase    :    List[str] 	=			TFViTModel(SCREAMING_SNAKE_CASE__       ,name='vision_model')
								__lowerCamelCase    :    Any 	=			TFBertModel(SCREAMING_SNAKE_CASE__       ,name='text_model')
								return vision_model, text_model


							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Optional[Any] 	=			TFViTModelTester(self)
								__lowerCamelCase    :    str 	=			TFBertModelTester(self)
								__lowerCamelCase    :    Tuple 	=			vit_model_tester.prepare_config_and_inputs()
								__lowerCamelCase    :    Any 	=			bert_model_tester.prepare_config_and_inputs()

								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			vision_config_and_inputs

								(
								    (
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			
								)    :    str 	=			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_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      int):
								# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
								# just reinitialize it.
								__lowerCamelCase    :    Union[str, Any] 	=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
								    'Rocketknight1/tiny-random-deit-tf'       ,'hf-internal-testing/tiny-random-roberta')
								__lowerCamelCase    :    Union[str, Any] 	=			1_3
								__lowerCamelCase    :    Tuple 	=			floats_tensor(
								    [
								        batch_size,
								        model.vision_model.config.num_channels,
								        model.vision_model.config.image_size,
								        model.vision_model.config.image_size,
								    ])
								__lowerCamelCase    :    Optional[Any] 	=			ids_tensor([batch_size, 4]       ,model.text_model.config.vocab_size)
								__lowerCamelCase    :    Any 	=			random_attention_mask([batch_size, 4])
								__lowerCamelCase    :    Optional[int] 	=			{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

								return model, inputs
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Dict=None       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    str 	=			self.get_vision_text_model(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__       ,text_model=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			model(
								    input_ids=SCREAMING_SNAKE_CASE__       ,pixel_values=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__       ,output_attentions=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[Any] 	=			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)
								__lowerCamelCase    :    Optional[Any] 	=			to_atuple(vision_model.config.image_size)
								__lowerCamelCase    :    int 	=			to_atuple(vision_model.config.patch_size)
								__lowerCamelCase    :    List[str] 	=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								__lowerCamelCase    :    Tuple 	=			num_patches + 2
								self.assertEqual(vision_attentions[0].shape[-3:]       ,(vision_config.num_attention_heads, seq_len, seq_len))

								__lowerCamelCase    :    Optional[int] 	=			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		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								__lowerCamelCase    :    List[str] 	=			TFDeiTModel(SCREAMING_SNAKE_CASE__       ,name='vision_model')
								__lowerCamelCase    :    Optional[Any] 	=			TFRobertaModel(SCREAMING_SNAKE_CASE__       ,name='text_model')
								return vision_model, text_model


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    str 	=			TFDeiTModelTester(self)
								__lowerCamelCase    :    Dict 	=			TFRobertaModelTester(self)
								__lowerCamelCase    :    List[Any] 	=			vit_model_tester.prepare_config_and_inputs()
								__lowerCamelCase    :    List[str] 	=			bert_model_tester.prepare_config_and_inputs()

								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			vision_config_and_inputs

								(
								    (
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			
								)    :    str 	=			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_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Tuple 	=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
								    'Rocketknight1/tiny-random-clip-tf'       ,'hf-internal-testing/tiny-random-bert')
								__lowerCamelCase    :    str 	=			1_3
								__lowerCamelCase    :    Dict 	=			floats_tensor(
								    [
								        batch_size,
								        model.vision_model.config.num_channels,
								        model.vision_model.config.image_size,
								        model.vision_model.config.image_size,
								    ])
								__lowerCamelCase    :    Tuple 	=			ids_tensor([batch_size, 4]       ,model.text_model.config.vocab_size)
								__lowerCamelCase    :    List[Any] 	=			random_attention_mask([batch_size, 4])
								__lowerCamelCase    :    str 	=			{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

								return model, inputs
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								__lowerCamelCase    :    str 	=			TFCLIPVisionModel(SCREAMING_SNAKE_CASE__       ,name='vision_model')
								__lowerCamelCase    :    Union[str, Any] 	=			TFBertModel(SCREAMING_SNAKE_CASE__       ,name='text_model')
								return vision_model, text_model


							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Any 	=			TFCLIPVisionModelTester(self)
								__lowerCamelCase    :    List[str] 	=			TFBertModelTester(self)
								__lowerCamelCase    :    int 	=			clip_model_tester.prepare_config_and_inputs()
								__lowerCamelCase    :    List[Any] 	=			bert_model_tester.prepare_config_and_inputs()

								__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			vision_config_and_inputs

								(
								    (
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			(
								    __lowerCamelCase
								)	,			
								)    :    Tuple 	=			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):
								__lowerCamelCase    :    Union[str, Any] 	=			TFVisionTextDualEncoderModel.from_pretrained(
								    'clip-italian/clip-italian'       ,logit_scale_init_value=1.0       ,from_pt=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian')

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

								__lowerCamelCase    :    List[Any] 	=			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])       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			np.array([[1.2284727, 0.3104122]])

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







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


a		=["""gpt2"""]
a		="""gpt2"""

if is_tf_available():






					class 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

													__lowerCamelCase    :    List[Any] 	=			tf.cast(input_ids_dense > 0       ,tf.intaa)
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__lowerCamelCase    :    Optional[int] 	=			[
								    'This is a straightforward English test sentence.',
								    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
								    'Now we\'re going to add some Chinese: 一 二 三 一二三',
								    'And some much more rare Chinese: 齉 堃 齉堃',
								    'Je vais aussi écrire en français pour tester les accents',
								    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
								]
								__lowerCamelCase    :    List[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											__lowerCamelCase    :    List[str] 	=			python_outputs[key].numpy()
											__lowerCamelCase    :    Optional[Any] 	=			tf_outputs[key].numpy()

											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
											self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE__       ,tf.intaa) == tf_outputs_values))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Any 	=			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    str 	=			model.serving(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__lowerCamelCase    :    Optional[Any] 	=			Path(SCREAMING_SNAKE_CASE__) / 'saved.model'
										tf.saved_model.save(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,signatures={'serving_default': model.serving})
										__lowerCamelCase    :    Tuple 	=			tf.saved_model.load(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			loaded_model.signatures['serving_default'](SCREAMING_SNAKE_CASE__)['output_0']
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output))
							@slow
							def    lowerCAmelCase					(		self		:      int):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Optional[int] 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    Optional[Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			model_from_config(SCREAMING_SNAKE_CASE__)

									for key in from_config_output.keys():
										self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    List[Any] 	=			out['input_ids'].numpy().shape[1]

										assert out_length == max_length




