



'''simple docstring'''




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase=True	,				_lowercase="pt"		)      ->			Dict:
							UpperCAmelCase     :		Optional[Any]							     =		{'add_prefix_space': True} if isinstance(lowerCAmelCase__	,				lowerCAmelCase__		) and not line.startswith(""" """		) else {}
							UpperCAmelCase     :		Any							     =		padding_side
							return tokenizer(
							    [line]	,				max_length=lowerCAmelCase__	,				padding="""max_length""" if pad_to_max_length else None	,				truncation=lowerCAmelCase__	,				return_tensors=lowerCAmelCase__	,				add_special_tokens=lowerCAmelCase__	,				**lowerCAmelCase__	,				)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=None	,				)      ->			Optional[Any]:
							UpperCAmelCase     :		str							     =		input_ids.ne(lowerCAmelCase__		).any(dim=0		)
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 			UpperCamelCase_ (							lowercase_			):





	def __init__(  self		,		A		,		A		,		A		,		A		,		A="train"		,		A=None		,		A=None		,		A=None		,		A=""		,		)			->			Tuple:
								super().__init__()
								UpperCAmelCase     :		List[str]							     =		Path(SCREAMING_SNAKE_CASE_    ).joinpath(type_path + """.source"""    )
								UpperCAmelCase     :		List[str]							     =		Path(SCREAMING_SNAKE_CASE_    ).joinpath(type_path + """.target"""    )
								UpperCAmelCase     :		Tuple							     =		self.get_char_lens(self.src_file    )
								UpperCAmelCase     :		List[Any]							     =		max_source_length
								UpperCAmelCase     :		Union[str, Any]							     =		max_target_length
								assert min(self.src_lens    ) > 0, f'''found empty line in {self.src_file}'''
								UpperCAmelCase     :		Optional[Any]							     =		tokenizer
								UpperCAmelCase     :		Optional[int]							     =		prefix
								if n_obs is not None:
															UpperCAmelCase     :		Tuple							     =		self.src_lens[:n_obs]
								UpperCAmelCase     :		str							     =		src_lang
								UpperCAmelCase     :		List[Any]							     =		tgt_lang





	def __len__(  self    )			->			Optional[Any]:
								return len(self.src_lens    )





	def __getitem__(  self		,		A    )			->			Any:
								UpperCAmelCase     :		Any							     =		index + 1  # linecache starts at 1
								UpperCAmelCase     :		Dict							     =		self.prefix + linecache.getline(str(self.src_file    )		,		SCREAMING_SNAKE_CASE_    ).rstrip("""\n"""    )
								UpperCAmelCase     :		int							     =		linecache.getline(str(self.tgt_file    )		,		SCREAMING_SNAKE_CASE_    ).rstrip("""\n"""    )
								assert source_line, f'''empty source line for index {index}'''
								assert tgt_line, f'''empty tgt line for index {index}'''

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer		,		SCREAMING_SNAKE_CASE_    ):
															source_line += self.tokenizer.eos_token
															tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								UpperCAmelCase     :		Any							     =		(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer		,		SCREAMING_SNAKE_CASE_    ) else self.tokenizer
								)
								UpperCAmelCase     :		Optional[int]							     =		self.tokenizer.generator if isinstance(self.tokenizer		,		SCREAMING_SNAKE_CASE_    ) else self.tokenizer

								UpperCAmelCase     :		Optional[Any]							     =		encode_line(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		self.max_source_length		,		"""right"""    )
								UpperCAmelCase     :		List[Any]							     =		encode_line(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		self.max_target_length		,		"""right"""    )

								UpperCAmelCase     :		List[str]							     =		source_inputs['input_ids'].squeeze()
								UpperCAmelCase     :		List[str]							     =		target_inputs['input_ids'].squeeze()
								UpperCAmelCase     :		List[str]							     =		source_inputs['attention_mask'].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}





	@staticmethod
	def  _lowercase(  A    )			->			List[str]:
								return [len(SCREAMING_SNAKE_CASE_    ) for x in Path(SCREAMING_SNAKE_CASE_    ).open().readlines()]






	def  _lowercase(  self		,		A    )			->			int:
								UpperCAmelCase     :		Optional[int]							     =		torch.stack([x["""input_ids"""] for x in batch]    )
								UpperCAmelCase     :		Dict							     =		torch.stack([x["""attention_mask"""] for x in batch]    )
								UpperCAmelCase     :		str							     =		torch.stack([x["""decoder_input_ids"""] for x in batch]    )
								UpperCAmelCase     :		Any							     =		(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer		,		SCREAMING_SNAKE_CASE_    )
								    else self.tokenizer.pad_token_id
								)
								UpperCAmelCase     :		Dict							     =		(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer		,		SCREAMING_SNAKE_CASE_    )
								    else self.tokenizer.pad_token_id
								)
								UpperCAmelCase     :		int							     =		trim_batch(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_    )
								UpperCAmelCase     :		Optional[int]							     =		trim_batch(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		attention_mask=SCREAMING_SNAKE_CASE_    )
								UpperCAmelCase     :		List[str]							     =		{
								    'input_ids': source_ids,
								    'attention_mask': source_mask,
								    'decoder_input_ids': y,
								}
								return batch




a   :			Optional[int]					  =    getLogger(__name__)



def       __lowerCamelCase  (					_lowercase		)      ->			str:
							return list(itertools.chain.from_iterable(lowerCAmelCase__		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			None:
							UpperCAmelCase     :		str							     =		get_git_info()
							save_json(lowerCAmelCase__	,				os.path.join(lowerCAmelCase__	,				"""git_log.json"""		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=4	,				**_lowercase		)      ->			int:




							with open(lowerCAmelCase__	,				"""w"""		) as f:
														json.dump(lowerCAmelCase__	,				lowerCAmelCase__	,				indent=lowerCAmelCase__	,				**lowerCAmelCase__		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:




							with open(lowerCAmelCase__		) as f:
														return json.load(lowerCAmelCase__		)



def       __lowerCamelCase  (					)      ->			Union[str, Any]:
							UpperCAmelCase     :		str							     =		git.Repo(search_parent_directories=lowerCAmelCase__		)
							UpperCAmelCase     :		int							     =		{
							    'repo_id': str(lowerCAmelCase__		),
							    'repo_sha': str(repo.head.object.hexsha		),
							    'repo_branch': str(repo.active_branch		),
							    'hostname': str(socket.gethostname()		),
							}
							return repo_infos



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List:
							return list(map(lowerCAmelCase__	,				lowerCAmelCase__		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Dict:




							with open(lowerCAmelCase__	,				"""wb"""		) as f:
														return pickle.dump(lowerCAmelCase__	,				lowerCAmelCase__		)



def       __lowerCamelCase  (					_lowercase		)      ->			str:




							def remove_articles(_lowercase		):
														return re.sub(R"""\b(a|an|the)\b"""	,				""" """	,				lowerCAmelCase__		)

							def white_space_fix(_lowercase		):
														return " ".join(text.split()		)

							def remove_punc(_lowercase		):
														UpperCAmelCase     :		Any							     =		set(string.punctuation		)
														return "".join(ch for ch in text if ch not in exclude		)

							def lower(_lowercase		):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__		)		)		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[Any]:
							UpperCAmelCase     :		Optional[int]							     =		normalize_answer(lowerCAmelCase__		).split()
							UpperCAmelCase     :		List[str]							     =		normalize_answer(lowerCAmelCase__		).split()
							UpperCAmelCase     :		int							     =		Counter(lowerCAmelCase__		) & Counter(lowerCAmelCase__		)
							UpperCAmelCase     :		Optional[Any]							     =		sum(common.values()		)
							if num_same == 0:
														return 0
							UpperCAmelCase     :		Tuple							     =		1.0 * num_same / len(lowerCAmelCase__		)
							UpperCAmelCase     :		List[Any]							     =		1.0 * num_same / len(lowerCAmelCase__		)
							UpperCAmelCase     :		Any							     =		(2 * precision * recall) / (precision + recall)
							return fa



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[int]:
							return normalize_answer(lowerCAmelCase__		) == normalize_answer(lowerCAmelCase__		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Dict:
							assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
							UpperCAmelCase     :		Optional[Any]							     =		0
							for hypo, pred in zip(lowerCAmelCase__	,				lowerCAmelCase__		):
														em += exact_match_score(lowerCAmelCase__	,				lowerCAmelCase__		)
							if len(lowerCAmelCase__		) > 0:
														em /= len(lowerCAmelCase__		)
							return {"em": em}



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							return model_prefix.startswith("""rag"""		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Optional[Any]							     =		{p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							UpperCAmelCase     :		Union[str, Any]							     =		'dropout_rate'
							for p in extra_params:
														if getattr(lowerCAmelCase__	,				lowerCAmelCase__	,				lowerCAmelCase__		):
																					if not hasattr(lowerCAmelCase__	,				lowerCAmelCase__		) and not hasattr(lowerCAmelCase__	,				equivalent_param[p]		):
																												logger.info("""config doesn\'t have a `{}` attribute""".format(lowerCAmelCase__		)		)
																												delattr(lowerCAmelCase__	,				lowerCAmelCase__		)
																												continue
																					UpperCAmelCase     :		Any							     =		p if hasattr(lowerCAmelCase__	,				lowerCAmelCase__		) else equivalent_param[p]
																					setattr(lowerCAmelCase__	,				lowerCAmelCase__	,				getattr(lowerCAmelCase__	,				lowerCAmelCase__		)		)
																					delattr(lowerCAmelCase__	,				lowerCAmelCase__		)
							return hparams, config






"""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__   :




      """simple docstring"""




      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3    ,			SCREAMING_SNAKE_CASE_				:      Any=3_2    ,			SCREAMING_SNAKE_CASE_				:      int=3    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=1_0    ,			SCREAMING_SNAKE_CASE_				:      Dict=[1_0, 2_0, 3_0, 4_0]    ,			SCREAMING_SNAKE_CASE_				:      int=[1, 1, 2, 1]    ,			SCREAMING_SNAKE_CASE_				:      int=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      int="relu"    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=3    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			):
        lowerCAmelCase_   :				Optional[int]								= parent
        lowerCAmelCase_   :				Union[str, Any]								= batch_size
        lowerCAmelCase_   :				Optional[int]								= image_size
        lowerCAmelCase_   :				Optional[int]								= num_channels
        lowerCAmelCase_   :				Any								= embeddings_size
        lowerCAmelCase_   :				Dict								= hidden_sizes
        lowerCAmelCase_   :				Any								= depths
        lowerCAmelCase_   :				Optional[int]								= is_training
        lowerCAmelCase_   :				int								= use_labels
        lowerCAmelCase_   :				List[Any]								= hidden_act
        lowerCAmelCase_   :				Dict								= num_labels
        lowerCAmelCase_   :				Optional[int]								= scope
        lowerCAmelCase_   :				Tuple								= len(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				List[str]								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

        lowerCAmelCase_   :				List[str]								= self.get_config()
        return config, pixel_values, labels



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        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				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= TFRegNetModel(config=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= model(SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )
        # 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 // 3_2, self.image_size // 3_2)    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				int								= self.num_labels
        lowerCAmelCase_   :				int								= TFRegNetForImageClassification(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= model(SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				str								= self.prepare_config_and_inputs()
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= config_and_inputs
        lowerCAmelCase_   :				Optional[Any]								= {'pixel_values': pixel_values}
        return config, inputs_dict







@require_tf
class       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE  				=     (
          {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[Any]								= TFRegNetModelTester(self       )
        lowerCAmelCase_   :				Tuple								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			has_text_modality=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        return



      @unittest.skip(reason='RegNet does not use inputs_embeds'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[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				:      Tuple       ):
        super().test_keras_fit()



      @unittest.skip(reason='RegNet does not support input and output embeddings'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase_   :				Any								= ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
          lowerCAmelCase_   :				List[Any]								= model_class(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )    ,			training=SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				List[str]								= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase_   :				Union[str, Any]								= self.model_tester.num_stages
          self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			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]    ,			)

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

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowerCAmelCase_   :				List[str]								= True

            check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs_for_common()

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

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

            recursive_check(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

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

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

          lowerCAmelCase_   :				Optional[int]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				str								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				Tuple								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				int								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			{'output_hidden_states': True}       )

          lowerCAmelCase_   :				List[Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Any								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			{'output_hidden_states': True}       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				List[str]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_   :				int								= TFRegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )




def 				UpperCamelCase_    (    )						->   List[str]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







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




      """simple docstring"""




      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
            if is_vision_available()
            else None
        )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				int								= TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

        lowerCAmelCase_   :				Tuple								= self.default_image_processor
        lowerCAmelCase_   :				Dict								= prepare_img()
        lowerCAmelCase_   :				Union[str, Any]								= image_processor(images=SCREAMING_SNAKE_CASE_    ,			return_tensors='tf'       )

        # forward pass
        lowerCAmelCase_   :				List[str]								= model(**SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )

        # verify the logits
        lowerCAmelCase_   :				Optional[Any]								= tf.TensorShape((1, 1_0_0_0)       )
        self.assertEqual(outputs.logits.shape    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[Any]								= tf.constant([-0.41_80, -1.50_51, -3.48_36]       )

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





