





'''simple docstring'''







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
			    XLMForMultipleChoice,
			    XLMForQuestionAnswering,
			    XLMForQuestionAnsweringSimple,
			    XLMForSequenceClassification,
			    XLMForTokenClassification,
			    XLMModel,
			    XLMWithLMHeadModel,
			)
			from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class 			_lowercase						:







					def __init__( self    ,   A__    ,   A__=13    ,   A__=7    ,   A__=True    ,   A__=True    ,   A__=True    ,   A__=True    ,   A__=True    ,   A__=False    ,   A__=False    ,   A__=False    ,   A__=2    ,   A__=99    ,   A__=0    ,   A__=32    ,   A__=5    ,   A__=4    ,   A__=0.1    ,   A__=0.1    ,   A__=5_12    ,   A__=2    ,   A__=0.0_2    ,   A__=2    ,   A__=4    ,   A__="last"    ,   A__=True    ,   A__=None    ,   A__=0    ,   ) ->	Optional[Any]:
						snake_case				=			parent
						snake_case				=			batch_size
						snake_case				=			seq_length
						snake_case				=			is_training
						snake_case				=			use_input_lengths
						snake_case				=			use_token_type_ids
						snake_case				=			use_labels
						snake_case				=			gelu_activation
						snake_case				=			sinusoidal_embeddings
						snake_case				=			causal
						snake_case				=			asm
						snake_case				=			n_langs
						snake_case				=			vocab_size
						snake_case				=			n_special
						snake_case				=			hidden_size
						snake_case				=			num_hidden_layers
						snake_case				=			num_attention_heads
						snake_case				=			hidden_dropout_prob
						snake_case				=			attention_probs_dropout_prob
						snake_case				=			max_position_embeddings
						snake_case				=			type_sequence_label_size
						snake_case				=			initializer_range
						snake_case				=			num_labels
						snake_case				=			num_choices
						snake_case				=			summary_type
						snake_case				=			use_proj
						snake_case				=			scope
						snake_case				=			bos_token_id







					def     UpperCamelCase			( self    ) ->	Dict:
						snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size    )
						snake_case				=			random_attention_mask([self.batch_size, self.seq_length]    )

						snake_case				=			None
						if self.use_input_lengths:
							snake_case				=			(
							    ids_tensor([self.batch_size]    ,   vocab_size=2    ) + self.seq_length - 2
							)  # small variation of seq_length

						snake_case				=			None
						if self.use_token_type_ids:
							snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.n_langs    )

						snake_case				=			None
						snake_case				=			None
						snake_case				=			None
						if self.use_labels:
							snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )
							snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels    )
							snake_case				=			ids_tensor([self.batch_size]    ,   2    ).float()
							snake_case				=			ids_tensor([self.batch_size]    ,   self.num_choices    )

						snake_case				=			self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)







					def     UpperCamelCase			( self    ) ->	Any:
						return XLMConfig(
						    vocab_size=self.vocab_size    ,   n_special=self.n_special    ,   emb_dim=self.hidden_size    ,   n_layers=self.num_hidden_layers    ,   n_heads=self.num_attention_heads    ,   dropout=self.hidden_dropout_prob    ,   attention_dropout=self.attention_probs_dropout_prob    ,   gelu_activation=self.gelu_activation    ,   sinusoidal_embeddings=self.sinusoidal_embeddings    ,   asm=self.asm    ,   causal=self.causal    ,   n_langs=self.n_langs    ,   max_position_embeddings=self.max_position_embeddings    ,   initializer_range=self.initializer_range    ,   summary_type=self.summary_type    ,   use_proj=self.use_proj    ,   num_labels=self.num_labels    ,   bos_token_id=self.bos_token_id    ,   )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	str:
						snake_case				=			XLMModel(config=A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			model(A__    ,   lengths=A__    ,   langs=A__    )
						snake_case				=			model(A__    ,   langs=A__    )
						snake_case				=			model(A__    )
						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Union[str, Any]:
						snake_case				=			XLMWithLMHeadModel(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			model(A__    ,   token_type_ids=A__    ,   labels=A__    )
						self.parent.assertEqual(result.loss.shape    ,   ()    )
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	List[str]:
						snake_case				=			XLMForQuestionAnsweringSimple(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			model(A__    )

						snake_case				=			model(A__    ,   start_positions=A__    ,   end_positions=A__    )
						snake_case				=			outputs
						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     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Union[str, Any]:
						snake_case				=			XLMForQuestionAnswering(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			model(A__    )

						snake_case				=			model(
						    A__    ,   start_positions=A__    ,   end_positions=A__    ,   cls_index=A__    ,   is_impossible=A__    ,   p_mask=A__    ,   )

						snake_case				=			model(
						    A__    ,   start_positions=A__    ,   end_positions=A__    ,   cls_index=A__    ,   is_impossible=A__    ,   )

						((snake_case		)				,				)				=			result_with_labels.to_tuple()

						snake_case				=			model(A__    ,   start_positions=A__    ,   end_positions=A__    )

						((snake_case		)				,				)				=			result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape    ,   ()    )
						self.parent.assertEqual(result.start_top_log_probs.shape    ,   (self.batch_size, model.config.start_n_top)    )
						self.parent.assertEqual(result.start_top_index.shape    ,   (self.batch_size, model.config.start_n_top)    )
						self.parent.assertEqual(
						    result.end_top_log_probs.shape    ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)    )
						self.parent.assertEqual(
						    result.end_top_index.shape    ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)    )
						self.parent.assertEqual(result.cls_logits.shape    ,   (self.batch_size,)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	int:
						snake_case				=			XLMForSequenceClassification(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			model(A__    )
						snake_case				=			model(A__    ,   labels=A__    )
						self.parent.assertEqual(result.loss.shape    ,   ()    )
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Dict:
						snake_case				=			self.num_labels
						snake_case				=			XLMForTokenClassification(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			model(A__    ,   attention_mask=A__    ,   labels=A__    )
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Optional[Any]:
						snake_case				=			self.num_choices
						snake_case				=			XLMForMultipleChoice(config=A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			input_ids.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
						snake_case				=			token_type_ids.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
						snake_case				=			input_mask.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
						snake_case				=			model(
						    A__    ,   attention_mask=A__    ,   token_type_ids=A__    ,   labels=A__    ,   )
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)    )







					def     UpperCamelCase			( self    ) ->	Union[str, Any]:
						snake_case				=			self.prepare_config_and_inputs()
						(
						    (
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,		(
						    snake_case		
						)				,				
						)				=			config_and_inputs
						snake_case				=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
						return config, inputs_dict




@require_torch
class 			_lowercase						(       __a				,		__a				,		__a				,		unittest.TestCase					):
					_UpperCAmelCase										=					(
					    (
					        XLMModel,
					        XLMWithLMHeadModel,
					        XLMForQuestionAnswering,
					        XLMForSequenceClassification,
					        XLMForQuestionAnsweringSimple,
					        XLMForTokenClassification,
					        XLMForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					_UpperCAmelCase										=					(
					    (XLMWithLMHeadModel,) if is_torch_available() else ()
					)  # TODO (PVP): Check other models whether language generation is also applicable
					_UpperCAmelCase										=					(
					    {
					        '''feature-extraction''': XLMModel,
					        '''fill-mask''': XLMWithLMHeadModel,
					        '''question-answering''': XLMForQuestionAnsweringSimple,
					        '''text-classification''': XLMForSequenceClassification,
					        '''text-generation''': XLMWithLMHeadModel,
					        '''token-classification''': XLMForTokenClassification,
					        '''zero-shot''': XLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Any:
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith('''Fast'''    )
						):
							# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
							# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
							# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
							return True

						return False







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__=False    ) ->	Optional[int]:
						snake_case				=			super()._prepare_for_class(A__    ,   A__    ,   return_labels=A__    )

						if return_labels:
							if model_class.__name__ == "XLMForQuestionAnswering":
								snake_case				=			torch.zeros(
								    self.model_tester.batch_size    ,   dtype=torch.long    ,   device=A__    )
								snake_case				=			torch.zeros(
								    self.model_tester.batch_size    ,   dtype=torch.long    ,   device=A__    )

						return inputs_dict







					def     UpperCamelCase			( self    ) ->	Optional[int]:
						snake_case				=			XLMModelTester(self    )
						snake_case				=			ConfigTester(self    ,   config_class=A__    ,   emb_dim=37    )







					def     UpperCamelCase			( self    ) ->	List[Any]:
						self.config_tester.run_common_tests()







					def     UpperCamelCase			( self    ) ->	Tuple:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*A__    )







					def     UpperCamelCase			( self    ) ->	str:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*A__    )







					def     UpperCamelCase			( self    ) ->	Tuple:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*A__    )







					def     UpperCamelCase			( self    ) ->	Union[str, Any]:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*A__    )







					def     UpperCamelCase			( self    ) ->	Dict:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*A__    )







					def     UpperCamelCase			( self    ) ->	List[str]:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*A__    )







					def     UpperCamelCase			( self    ) ->	Union[str, Any]:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*A__    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__=False    ,   A__=1    ) ->	Any:
						self.assertIsInstance(A__    ,   A__    )
						self.assertListEqual(
						    [isinstance(A__    ,   A__    ) for iter_attentions in attentions]    ,   [True] * len(A__    )    )
						self.assertEqual(len(A__    )    ,   (max_length - min_length) * num_beam_groups    )

						for idx, iter_attentions in enumerate(A__    ):
							# adds PAD dummy token
							snake_case				=			min_length + idx + 1
							snake_case				=			min_length + idx + 1

							snake_case				=			(
							    batch_size * num_beam_groups,
							    config.num_attention_heads,
							    tgt_len,
							    src_len,
							)
							# check attn size
							self.assertListEqual(
							    [layer_attention.shape for layer_attention in iter_attentions]    ,   [expected_shape] * len(A__    )    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__=False    ,   A__=1    ) ->	Tuple:
						self.assertIsInstance(A__    ,   A__    )
						self.assertListEqual(
						    [isinstance(A__    ,   A__    ) for iter_hidden_states in hidden_states]    ,   [True] * len(A__    )    ,   )
						self.assertEqual(len(A__    )    ,   (max_length - min_length) * num_beam_groups    )

						for idx, iter_hidden_states in enumerate(A__    ):
							# adds PAD dummy token
							snake_case				=			min_length + idx + 1
							snake_case				=			(batch_size * num_beam_groups, seq_len, config.hidden_size)
							# check hidden size
							self.assertListEqual(
							    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]    ,   [expected_shape] * len(A__    )    ,   )
						pass







					@slow
					def     UpperCamelCase			( self    ) ->	Tuple:
						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case				=			XLMModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )




@require_torch
class 			_lowercase						(       unittest.TestCase					):







					@slow
					def     UpperCamelCase			( self    ) ->	List[str]:
						snake_case				=			XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''    )
						model.to(A__    )
						snake_case				=			torch.tensor([[14, 4_47]]    ,   dtype=torch.long    ,   device=A__    )  # the president
						snake_case				=			[
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						snake_case				=			model.generate(A__    ,   do_sample=A__    )
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()    ,   A__    )








'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=30    ,   A__=2    ,   A__=3    ,   A__=True    ,   A__=True    ,   A__=32    ,   A__=2    ,   A__=4    ,   A__=37    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=10    ,   A__=0.0_2    ,   A__=3    ,   A__=None    ,   ) ->	List[Any]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			image_size
      snake_case				=			patch_size
      snake_case				=			num_channels
      snake_case				=			is_training
      snake_case				=			use_labels
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			scope

      # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      snake_case				=			(image_size // patch_size) ** 2
      snake_case				=			num_patches + 1







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      snake_case				=			None
      if self.use_labels:
       snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )

      snake_case				=			self.get_config()

      return config, pixel_values, labels







     def     UpperCamelCase			( self    ) ->	int:
      return ViTConfig(
          image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   is_decoder=A__    ,   initializer_range=self.initializer_range    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			TFViTModel(config=A__    )
      snake_case				=			model(A__    ,   training=A__    )
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      snake_case				=			(image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, seq_length, self.hidden_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Optional[int]:
      snake_case				=			self.type_sequence_label_size
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			model(A__    ,   labels=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      snake_case				=			1
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.prepare_config_and_inputs()
      snake_case				,		snake_case				,		snake_case				=			config_and_inputs
      snake_case				=			{'''pixel_values''': pixel_values}
      return config, inputs_dict




@require_tf
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     _UpperCAmelCase										=					(
         {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			TFViTModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   has_text_modality=A__    ,   hidden_size=37    )







     def     UpperCamelCase			( self    ) ->	int:
      self.config_tester.run_common_tests()







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	int:
      pass







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	str:
      pass







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )
       self.assertIsInstance(model.get_input_embeddings()    ,   (tf.keras.layers.Layer)    )
       snake_case				=			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(A__    ,   tf.keras.layers.Layer    )    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )
       snake_case				=			inspect.signature(model.call    )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       snake_case				=			[*signature.parameters.keys()]

       snake_case				=			['''pixel_values''']
       self.assertListEqual(arg_names[:1]    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A__    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			TFViTModel.from_pretrained('''google/vit-base-patch16-224'''    )
      self.assertIsNotNone(A__    )




def    __UpperCamelCase	(	) ->Any:
 snake_case				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image






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







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''    ) if is_vision_available() else None







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''    )

      snake_case				=			self.default_image_processor
      snake_case				=			prepare_img()
      snake_case				=			image_processor(images=A__    ,   return_tensors='''tf'''    )

      # forward pass
      snake_case				=			model(**A__    )

      # verify the logits
      snake_case				=			tf.TensorShape((1, 10_00)    )
      self.assertEqual(outputs.logits.shape    ,   A__    )

      snake_case				=			tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]    )

      tf.debugging.assert_near(outputs.logits[0, :3]    ,   A__    ,   atol=1e-4    )

