

'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_snake_case     =  'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
_snake_case     =  direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
_snake_case     =  re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_snake_case     =  re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_snake_case     =  re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_snake_case     =  [
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]







def 					_A		(	snake_case     )			->	Tuple:
   _lowercase			:      Tuple			     =   re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,							A__     )
   return [m.group(0     ) for m in matches]







def 					_A		(	)			->	Optional[int]:
   _lowercase			:      List[Any]			     =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   _lowercase			:      str			     =   {
       config.replace("Config"		,							""     ): model_type for model_type, config in config_maping_names.items()
   }

   # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
   _lowercase			:      Optional[Any]			     =   collections.defaultdict(A__     )
   _lowercase			:      Optional[Any]			     =   collections.defaultdict(A__     )
   _lowercase			:      Optional[int]			     =   collections.defaultdict(A__     )

   # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
   for attr_name in dir(A__     ):
      _lowercase			:      str			     =   None
      if _re_tf_models.match(A__     ) is not None:
         _lowercase			:      Union[str, Any]			     =   tf_models
         _lowercase			:      Optional[Any]			     =   _re_tf_models.match(A__     ).groups()[0]
      elif _re_flax_models.match(A__     ) is not None:
         _lowercase			:      Union[str, Any]			     =   flax_models
         _lowercase			:      Dict			     =   _re_flax_models.match(A__     ).groups()[0]
      elif _re_pt_models.match(A__     ) is not None:
         _lowercase			:      Optional[int]			     =   pt_models
         _lowercase			:      Dict			     =   _re_pt_models.match(A__     ).groups()[0]

      if lookup_dict is not None:
         while len(A__     ) > 0:
            if attr_name in model_prefix_to_model_type:
               _lowercase			:      int			     =   True
               break
            # Try again after removing the last word in the name
            _lowercase			:      int			     =   "".join(camel_case_split(A__     )[:-1]     )

   _lowercase			:      Any			     =   set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
   _lowercase			:      str			     =   list(A__     )
   all_models.sort()

   _lowercase			:      Any			     =   {"model_type": all_models}
   _lowercase			:      Optional[Any]			     =   [pt_models[t] for t in all_models]
   _lowercase			:      Optional[Any]			     =   [tf_models[t] for t in all_models]
   _lowercase			:      str			     =   [flax_models[t] for t in all_models]

   # Now let's use the auto-mapping names to make sure
   _lowercase			:      Optional[Any]			     =   {}
   for t in all_models:
      if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
         _lowercase			:      Tuple			     =   "AutoProcessor"
      elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
         _lowercase			:      Tuple			     =   "AutoTokenizer"
      elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
         _lowercase			:      Tuple			     =   "AutoFeatureExtractor"
      else:
         # Default to AutoTokenizer if a model has nothing, for backward compatibility.
         _lowercase			:      Optional[int]			     =   "AutoTokenizer"

   _lowercase			:      Optional[int]			     =   [processors[t] for t in all_models]

   return pd.DataFrame(A__     )







def 					_A		(	snake_case     )			->	Any:
   _lowercase			:      Optional[Any]			     =   [
       transformers_module.models.auto.modeling_auto,
       transformers_module.models.auto.modeling_tf_auto,
       transformers_module.models.auto.modeling_flax_auto,
   ]
   for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
      _lowercase			:      int			     =   [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
      _lowercase			:      Any			     =   [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
      # Loop through all three frameworks
      for module, cls, mapping in zip(A__		,							A__		,							A__     ):
         # The type of pipeline may not exist in this framework
         if not hasattr(A__		,							A__     ):
            continue
         # First extract all model_names
         _lowercase			:      Any			     =   []
         for name in getattr(A__		,							A__     ).values():
            if isinstance(A__		,							A__     ):
               model_names.append(A__     )
            else:
               model_names.extend(list(A__     )     )

            # Add pipeline tag and auto model class for those models
         table.update({model_name: (pipeline_tag, cls) for model_name in model_names}     )

   return table







def 					_A		(	snake_case		,							snake_case     )			->	Any:
   _lowercase			:      Dict			     =   get_frameworks_table()
   _lowercase			:      Optional[Any]			     =   Dataset.from_pandas(A__     )

   _lowercase			:      int			     =   hf_hub_download(
       "huggingface/transformers-metadata"		,							"pipeline_tags.json"		,							repo_type="dataset"		,							token=A__     )
   _lowercase			:      Dict			     =   Dataset.from_json(A__     )
   _lowercase			:      int			     =   {
       tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
       for i in range(len(A__     )     )
   }
   _lowercase			:      List[Any]			     =   update_pipeline_and_auto_class_table(A__     )

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   _lowercase			:      Optional[int]			     =   sorted(table.keys()     )
   _lowercase			:      Any			     =   pd.DataFrame(
       {
           "model_class": model_classes,
           "pipeline_tag": [table[m][0] for m in model_classes],
           "auto_class": [table[m][1] for m in model_classes],
       }     )
   _lowercase			:      int			     =   Dataset.from_pandas(A__     )

   with tempfile.TemporaryDirectory() as tmp_dir:
      frameworks_dataset.to_json(os.path.join(A__		,							"frameworks.json"     )     )
      tags_dataset.to_json(os.path.join(A__		,							"pipeline_tags.json"     )     )

      if commit_sha is not None:
         _lowercase			:      Union[str, Any]			     =   (
             F'''Update with commit {commit_sha}\n\nSee: '''
             F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
         )
      else:
         _lowercase			:      List[Any]			     =   "Update"

      upload_folder(
          repo_id="huggingface/transformers-metadata"		,							folder_path=A__		,							repo_type="dataset"		,							token=A__		,							commit_message=A__		,							)







def 					_A		(	)			->	int:
   _lowercase			:      Union[str, Any]			     =   {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   _lowercase			:      str			     =   transformers_module.pipelines.SUPPORTED_TASKS
   _lowercase			:      Optional[int]			     =   []
   for key in pipeline_tasks:
      if key not in in_table:
         _lowercase			:      str			     =   pipeline_tasks[key]["pt"]
         if isinstance(A__		,							(list, tuple)     ):
            _lowercase			:      Dict			     =   model[0]
         _lowercase			:      int			     =   model.__name__
         if model not in in_table.values():
            missing.append(A__     )

   if len(A__     ) > 0:
      _lowercase			:      Optional[Any]			     =   ", ".join(A__     )
      raise ValueError(
          "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
          F'''`utils/update_metadata.py`: {msg}. Please add them!'''     )


if __name__ == "__main__":
    _snake_case     =  argparse.ArgumentParser()
    parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
    parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
    parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
    _snake_case     =  parser.parse_args()

    if args.check_only:
        check_pipeline_tags()
    else:
        update_metadata(args.token, args.commit_sha)














"""simple docstring"""
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor







class 			UpperCamelCase_       :





	def __init__(      self					: Optional[Any]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Optional[int]=3	,	lowerCAmelCase_					: Optional[int]=32	,	lowerCAmelCase_					: List[Any]=3	,	lowerCAmelCase_					: List[Any]=10	,	lowerCAmelCase_					: Any=[10, 20, 30, 40]	,	lowerCAmelCase_					: Any=[1, 1, 2, 1]	,	lowerCAmelCase_					: Union[str, Any]=True	,	lowerCAmelCase_					: Union[str, Any]=True	,	lowerCAmelCase_					: int="relu"	,	lowerCAmelCase_					: Tuple=3	,	lowerCAmelCase_					: Optional[int]=None	,	)						->			str:
				UpperCAmelCase_     : Tuple			     =					parent
				UpperCAmelCase_     : int			     =					batch_size
				UpperCAmelCase_     : str			     =					image_size
				UpperCAmelCase_     : List[Any]			     =					num_channels
				UpperCAmelCase_     : Tuple			     =					embeddings_size
				UpperCAmelCase_     : Union[str, Any]			     =					hidden_sizes
				UpperCAmelCase_     : int			     =					depths
				UpperCAmelCase_     : Optional[Any]			     =					is_training
				UpperCAmelCase_     : Dict			     =					use_labels
				UpperCAmelCase_     : str			     =					hidden_act
				UpperCAmelCase_     : str			     =					num_labels
				UpperCAmelCase_     : str			     =					scope
				UpperCAmelCase_     : str			     =					len(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Dict:
				UpperCAmelCase_     : Any			     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				UpperCAmelCase_     : Union[str, Any]			     =					None
				if self.use_labels:
							UpperCAmelCase_     : List[str]			     =					ids_tensor([self.batch_size]	,	self.num_labels						)

				UpperCAmelCase_     : Optional[Any]			     =					self.get_config()
				return config, pixel_values, labels





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				return RegNetConfig(
				    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	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : List[Any]			     =					TFRegNetModel(config=lowerCAmelCase_						)
				UpperCAmelCase_     : List[Any]			     =					model(lowerCAmelCase_	,	training=lowerCAmelCase_						)
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape	,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: List[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					self.num_labels
				UpperCAmelCase_     : List[Any]			     =					TFRegNetForImageClassification(lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					model(lowerCAmelCase_	,	labels=lowerCAmelCase_	,	training=lowerCAmelCase_						)
				self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Dict:
				UpperCAmelCase_     : Any			     =					self.prepare_config_and_inputs()
				UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Dict			     =					config_and_inputs
				UpperCAmelCase_     : List[str]			     =					{"pixel_values": pixel_values}
				return config, inputs_dict







@require_tf
class 			UpperCamelCase_       (__A				,						__A				,						unittest.TestCase     ):
	__magic_name__	      =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
	__magic_name__	      =				(
	    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					TFRegNetModelTester(self						)
				UpperCAmelCase_     : Optional[Any]			     =					ConfigTester(self	,	config_class=lowerCAmelCase_	,	has_text_modality=lowerCAmelCase_						)





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





	@unittest.skip(reason="RegNet does not use inputs_embeds"						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				pass





	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0	,	reason="TF does not support backprop for grouped convolutions on CPU."	,	)
	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Dict:
				super().test_keras_fit()





	@unittest.skip(reason="RegNet does not support input and output embeddings"						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Any:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			List[str]:
				UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_     : Dict			     =					model_class(lowerCAmelCase_						)
							UpperCAmelCase_     : Tuple			     =					inspect.signature(model.call						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase_     : List[Any]			     =					[*signature.parameters.keys()]

							UpperCAmelCase_     : int			     =					["pixel_values"]
							self.assertListEqual(arg_names[:1]	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Union[str, Any]:
				UpperCAmelCase_     : List[Any]			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Optional[int]:
				def check_hidden_states_output(lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: int						):
							UpperCAmelCase_     : str			     =					model_class(lowerCAmelCase_						)
							UpperCAmelCase_     : Any			     =					model(**self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_						)	,	training=lowerCAmelCase_						)

							UpperCAmelCase_     : str			     =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							UpperCAmelCase_     : Optional[Any]			     =					self.model_tester.num_stages
							self.assertEqual(len(lowerCAmelCase_						)	,	expected_num_stages + 1						)

							# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]						)	,	[self.model_tester.image_size // 2, self.model_tester.image_size // 2]	,	)

				UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_     : Tuple			     =					["basic", "bottleneck"]
				for model_class in self.all_model_classes:
							for layer_type in layers_type:
										UpperCAmelCase_     : List[Any]			     =					layer_type
										UpperCAmelCase_     : int			     =					True
										check_hidden_states_output(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										UpperCAmelCase_     : Optional[int]			     =					True

										check_hidden_states_output(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)





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

				def check_equivalence(lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: List[str]={}						):
							UpperCAmelCase_     : Tuple			     =					model(lowerCAmelCase_	,	return_dict=lowerCAmelCase_	,	**lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	return_dict=lowerCAmelCase_	,	**lowerCAmelCase_						).to_tuple()

							def recursive_check(lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Any						):
										if isinstance(lowerCAmelCase_	,	(List, Tuple)						):
													for tuple_iterable_value, dict_iterable_value in zip(lowerCAmelCase_	,	lowerCAmelCase_						):
																recursive_check(lowerCAmelCase_	,	lowerCAmelCase_						)
										elif tuple_object is None:
													return
										else:
													self.assertTrue(
													    all(tf.equal(lowerCAmelCase_	,	lowerCAmelCase_						)						)	,	msg=(
													        "Tuple and dict output are not equal. Difference:"
													        f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"""
													    )	,	)

										recursive_check(lowerCAmelCase_	,	lowerCAmelCase_						)

				for model_class in self.all_model_classes:
							UpperCAmelCase_     : Union[str, Any]			     =					model_class(lowerCAmelCase_						)

							UpperCAmelCase_     : List[Any]			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_						)
							UpperCAmelCase_     : str			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_						)
							check_equivalence(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)

							UpperCAmelCase_     : Any			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_	,	return_labels=lowerCAmelCase_						)
							UpperCAmelCase_     : List[Any]			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_	,	return_labels=lowerCAmelCase_						)
							check_equivalence(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)

							UpperCAmelCase_     : Union[str, Any]			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_						)
							UpperCAmelCase_     : str			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_						)
							check_equivalence(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	{"output_hidden_states": True}						)

							UpperCAmelCase_     : List[str]			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_	,	return_labels=lowerCAmelCase_						)
							UpperCAmelCase_     : Tuple			     =					self._prepare_for_class(lowerCAmelCase_	,	lowerCAmelCase_	,	return_labels=lowerCAmelCase_						)
							check_equivalence(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	{"output_hidden_states": True}						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				UpperCAmelCase_     : List[str]			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Union[str, Any]:
				for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_     : Any			     =					TFRegNetModel.from_pretrained(lowerCAmelCase_						)
							self.assertIsNotNone(lowerCAmelCase_						)





def 				snake_case      (   ):
			UpperCAmelCase_     : Dict			     =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
			return image







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





	@cached_property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			List[Any]:
				return (
				    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
				    if is_vision_available()
				    else None
				)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Dict:
				UpperCAmelCase_     : Any			     =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

				UpperCAmelCase_     : Union[str, Any]			     =					self.default_image_processor
				UpperCAmelCase_     : int			     =					prepare_img()
				UpperCAmelCase_     : List[Any]			     =					image_processor(images=lowerCAmelCase_	,	return_tensors="tf"						)

				# forward pass
				UpperCAmelCase_     : Tuple			     =					model(**lowerCAmelCase_	,	training=lowerCAmelCase_						)

				# verify the logits
				UpperCAmelCase_     : List[str]			     =					tf.TensorShape((1, 1_000)						)
				self.assertEqual(outputs.logits.shape	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[Any]			     =					tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]						)

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



