





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXLMForMultipleChoice,
         TFXLMForQuestionAnsweringSimple,
         TFXLMForSequenceClassification,
         TFXLMForTokenClassification,
         TFXLMModel,
         TFXLMWithLMHeadModel,
         XLMConfig,
     )



class 				_UpperCamelCase       :

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple       , )							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	parent
              UpperCamelCase_ 							=	13
              UpperCamelCase_ 							=	7
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	99
              UpperCamelCase_ 							=	0
              UpperCamelCase_ 							=	32
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	0.1
              UpperCamelCase_ 							=	0.1
              UpperCamelCase_ 							=	512
              UpperCamelCase_ 							=	16
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	0.02
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	"last"
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	0

       def lowercase			(		self:			int		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size		)
              UpperCamelCase_ 							=	random_attention_mask([self.batch_size, self.seq_length]       , dtype=tf.floataa		)

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

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

              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              if self.use_labels:
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size]       , self.type_sequence_label_size		)
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size, self.seq_length]       , self.num_labels		)
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size]       , 2       , dtype=tf.floataa		)
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size]       , self.num_choices		)

              UpperCamelCase_ 							=	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       , bos_token_id=self.bos_token_id       , )

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

       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			List[Any]       , )							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFXLMModel(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	[input_ids, input_mask]
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)		)

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , )							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	TFXLMWithLMHeadModel(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	outputs

              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)		)

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , )							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFXLMForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	{"input_ids": input_ids, "lengths": input_lengths}

              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length)		)
              self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length)		)

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , )							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	TFXLMForSequenceClassification(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	{"input_ids": input_ids, "lengths": input_lengths}

              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size)		)

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			str       , )							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.num_labels
              UpperCamelCase_ 							=	TFXLMForTokenClassification(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels)		)

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			str       , )							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.num_choices
              UpperCamelCase_ 							=	TFXLMForMultipleChoice(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       , 1		)       , (1, self.num_choices, 1)		)
              UpperCamelCase_ 							=	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       , 1		)       , (1, self.num_choices, 1)		)
              UpperCamelCase_ 							=	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       , 1		)       , (1, self.num_choices, 1)		)
              UpperCamelCase_ 							=	{
                  "input_ids": multiple_choice_inputs_ids,
                  "attention_mask": multiple_choice_input_mask,
                  "token_type_ids": multiple_choice_token_type_ids,
              }
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices)		)



       def lowercase			(		self:			Union[str, Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	self.prepare_config_and_inputs()
              (
                  (
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,			(
                  UpperCamelCase_	
              )							,				
              ) 							=	config_and_inputs
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "token_type_ids": token_type_ids,
                  "langs": token_type_ids,
                  "lengths": input_lengths,
              }
              return config, inputs_dict



@require_tf
class 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       Dict       = (
           (
               TFXLMModel,
               TFXLMWithLMHeadModel,
               TFXLMForSequenceClassification,
               TFXLMForQuestionAnsweringSimple,
               TFXLMForTokenClassification,
               TFXLMForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       _UpperCamelCase					:       Union[str, Any]       = (
           (TFXLMWithLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Check other models whether language generation is also applicable
       _UpperCamelCase					:       Tuple       = (
           {
               """feature-extraction""": TFXLMModel,
               """fill-mask""": TFXLMWithLMHeadModel,
               """question-answering""": TFXLMForQuestionAnsweringSimple,
               """text-classification""": TFXLMForSequenceClassification,
               """text-generation""": TFXLMWithLMHeadModel,
               """token-classification""": TFXLMForTokenClassification,
               """zero-shot""": TFXLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _UpperCamelCase					:       List[str]       = False
       _UpperCamelCase					:       Optional[int]       = False

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			List[str]:




              """simple docstring"""


              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 lowercase			(		self:			Union[str, Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	TFXLMModelTester(self		)
              UpperCamelCase_ 							=	ConfigTester(self       , config_class=_SCREAMING_SNAKE_CASE       , emb_dim=37		)

       def lowercase			(		self:			Dict		)							->			str:




              """simple docstring"""


              self.config_tester.run_common_tests()

       def lowercase			(		self:			int		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_model(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[str]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_lm_head(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Tuple		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_qa(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_sequence_classif(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Dict		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_for_token_classification(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[Any]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_xlm_for_multiple_choice(*_SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase			(		self:			List[Any]		)							->			str:




              """simple docstring"""


              for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase_ 							=	TFXLMModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)



@require_tf
class 				_UpperCamelCase       (						unittest.TestCase  ):

       @slow
       def lowercase			(		self:			Optional[Any]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"		)
              UpperCamelCase_ 							=	tf.convert_to_tensor([[14, 447]]       , dtype=tf.intaa		)  # the president
              UpperCamelCase_ 							=	[
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
                  14,
                  447,
              ]  # 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
              UpperCamelCase_ 							=	model.generate(_SCREAMING_SNAKE_CASE       , do_sample=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(output_ids[0].numpy().tolist()       , _SCREAMING_SNAKE_CASE		)






"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class   UpperCAmelCase__		(   unittest.TestCase  ):



		def __init__(			self	:     Dict   ,				snake_case	:     Dict   ,				snake_case	:     Optional[Any]=3   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     int=3   ,				snake_case	:     Any=10   ,				snake_case	:     int=[10, 20, 30, 40]   ,				snake_case	:     List[Any]=[1, 1, 2, 1]   ,				snake_case	:     List[str]=True   ,				snake_case	:     Any=True   ,				snake_case	:     str="relu"   ,				snake_case	:     List[str]=3   ,				snake_case	:     List[str]=None   ,				)      ->    int:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       image_size
									A    =       num_channels
									A    =       embeddings_size
									A    =       hidden_sizes
									A    =       depths
									A    =       is_training
									A    =       use_labels
									A    =       hidden_act
									A    =       num_labels
									A    =       scope
									A    =       len(snake_case					)



		def        A_		(			self	:     Optional[int]					)      ->    Any:






									'''simple docstring'''

									A    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									A    =       self.get_config()

									return config, pixel_values



		def        A_		(			self	:     int					)      ->    List[Any]:






									'''simple docstring'''

									return ResNetConfig(
									    num_channels=self.num_channels   ,				embeddings_size=self.embeddings_size   ,				hidden_sizes=self.hidden_sizes   ,				depths=self.depths   ,				hidden_act=self.hidden_act   ,				num_labels=self.num_labels   ,				image_size=self.image_size   ,				)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     str   ,				snake_case	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       FlaxResNetModel(config=snake_case					)
									A    =       model(snake_case					)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)



		def        A_		(			self	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       FlaxResNetForImageClassification(config=snake_case					)
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)



		def        A_		(			self	:     List[str]					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A   ,	A    =       config_and_inputs
									A    =       {'pixel_values': pixel_values}
									return config, inputs_dict






@require_flax
class   UpperCAmelCase__		(   UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[str]							=  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		lowerCAmelCase_							:						Tuple							=  False
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						List[str]							=  False



		def        A_		(			self	:     Optional[Any]					)      ->    None:






									'''simple docstring'''

									A    =       FlaxResNetModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case   ,				has_text_modality=snake_case					)



		def        A_		(			self	:     List[str]					)      ->    Dict:






									'''simple docstring'''

									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()



		def        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									return



		def        A_		(			self	:     str					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case					)



		def        A_		(			self	:     Optional[int]					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case					)



		@unittest.skip(reason='ResNet does not use inputs_embeds'					)
		def        A_		(			self	:     Tuple					)      ->    str:






									'''simple docstring'''

									pass



		@unittest.skip(reason='ResNet does not support input and output embeddings'					)
		def        A_		(			self	:     Tuple					)      ->    Any:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Optional[Any]					)      ->    int:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

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

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



		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									def check_hidden_states_output(snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[int]					):
																A    =       model_class(snake_case					)

																A    =       model(**self._prepare_for_class(snake_case   ,				snake_case					)					)

																A    =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																A    =       self.model_tester.num_stages
																self.assertEqual(len(snake_case					)   ,				expected_num_stages + 1					)



		@unittest.skip(reason='ResNet does not use feedforward chunking'					)
		def        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Union[str, Any]					)      ->    Union[str, Any]:






									'''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(snake_case   ,				snake_case					)
																							A    =       model_class(snake_case					)

																							@jax.jit
																							def model_jitted(snake_case	:     Any   ,				**snake_case	:     int					):
																														return model(pixel_values=snake_case   ,				**snake_case					)

																							with self.subTest('JIT Enabled'					):
																														A    =       model_jitted(**snake_case					).to_tuple()

																							with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																					A    =       model_jitted(**snake_case					).to_tuple()

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



def 	lowerCAmelCase__      (  )			->				int:
							A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image






@require_flax
class   UpperCAmelCase__		(   unittest.TestCase  ):



		@cached_property
		def        A_		(			self	:     Union[str, Any]					)      ->    int:






									'''simple docstring'''

									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'					) if is_vision_available() else None



		@slow
		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									A    =       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'					)

									A    =       self.default_image_processor
									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='np'					)

									A    =       model(**snake_case					)

									# verify the logits
									A    =       (1, 1_000)
									self.assertEqual(outputs.logits.shape   ,				snake_case					)

									A    =       jnp.array([-11.1069, -9.7877, -8.3777]					)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,				snake_case   ,				atol=1E-4					)					)



