






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase  :							Optional[Any]			  =    logging.get_logger(__name__)

lowercase  :							Dict			  =    {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowercase  :							Optional[Any]			  =    {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

lowercase  :							Tuple			  =    {
    '''camembert-base''': 5_12,
}

lowercase  :							List[Any]			  =    '''▁'''




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


      '''simple docstring'''




      A     :   Tuple						=					VOCAB_FILES_NAMES
      A     :   Optional[Any]						=					PRETRAINED_VOCAB_FILES_MAP
      A     :   Dict						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A     :   str						=					['input_ids', 'attention_mask']






      def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE="<s>"		,				_SCREAMING_SNAKE_CASE="</s>"		,				_SCREAMING_SNAKE_CASE="</s>"		,				_SCREAMING_SNAKE_CASE="<s>"		,				_SCREAMING_SNAKE_CASE="<unk>"		,				_SCREAMING_SNAKE_CASE="<pad>"		,				_SCREAMING_SNAKE_CASE="<mask>"		,				_SCREAMING_SNAKE_CASE=["<s>NOTUSED", "</s>NOTUSED"]		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				None:
        # Mask token behave like a normal word, i.e. include the space before it
        snake_case_				:							Optional[Any]						 =       AddedToken(_SCREAMING_SNAKE_CASE		,				lstrip=_SCREAMING_SNAKE_CASE		,				rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) else mask_token

        snake_case_				:							List[str]						 =       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE		,				eos_token=_SCREAMING_SNAKE_CASE		,				unk_token=_SCREAMING_SNAKE_CASE		,				sep_token=_SCREAMING_SNAKE_CASE		,				cls_token=_SCREAMING_SNAKE_CASE		,				pad_token=_SCREAMING_SNAKE_CASE		,				mask_token=_SCREAMING_SNAKE_CASE		,				additional_special_tokens=_SCREAMING_SNAKE_CASE		,				sp_model_kwargs=self.sp_model_kwargs		,				**_SCREAMING_SNAKE_CASE		,				)
        snake_case_				:							int						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(str(_SCREAMING_SNAKE_CASE				)				)
        snake_case_				:							Dict						 =       vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        snake_case_				:							Optional[int]						 =       {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
        snake_case_				:							Optional[Any]						 =       len(self.fairseq_tokens_to_ids				)
        snake_case_				:							str						 =       len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
        snake_case_				:							str						 =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:

        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        snake_case_				:							Optional[Any]						 =       [self.cls_token_id]
        snake_case_				:							Union[str, Any]						 =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False				) ->				List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_SCREAMING_SNAKE_CASE		,				token_ids_a=_SCREAMING_SNAKE_CASE		,				already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

        if token_ids_a is None:
          return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
        return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				List[int]:
        snake_case_				:							Dict						 =       [self.sep_token_id]
        snake_case_				:							Any						 =       [self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep				) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]






      @property
      def       _lowerCAmelCase       (	self				) ->				Dict:
        return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)






      def       _lowerCAmelCase       (	self				) ->				Tuple:
        snake_case_				:							Dict						 =       {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
        return self.sp_model.encode(_SCREAMING_SNAKE_CASE		,				out_type=_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE				) == 0:
          # Convert sentence piece unk token to fairseq unk token index
          return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
        if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
        snake_case_				:							Dict						 =       []
        snake_case_				:							int						 =       ""
        snake_case_				:							List[Any]						 =       False
        for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            if not prev_is_special:
              out_string += " "
            out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
            snake_case_				:							Optional[Any]						 =       True
            snake_case_				:							List[str]						 =       []
          else:
            current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
            snake_case_				:							Union[str, Any]						 =       False
        out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
        return out_string.strip()






      def __getstate__(	self				) ->				int:
        snake_case_				:							List[str]						 =       self.__dict__.copy()
        snake_case_				:							List[Any]						 =       None
        return state






      def __setstate__(	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
        snake_case_				:							Tuple						 =       d

        # for backward compatibility
        if not hasattr(self		,				"sp_model_kwargs"				):
          snake_case_				:							Optional[int]						 =       {}

        snake_case_				:							int						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.vocab_file				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				Tuple[str]:
        if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
          return
        snake_case_				:							Tuple						 =       os.path.join(
            _SCREAMING_SNAKE_CASE		,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
          copyfile(self.vocab_file		,				_SCREAMING_SNAKE_CASE				)
        elif not os.path.isfile(self.vocab_file				):
          with open(_SCREAMING_SNAKE_CASE		,				"wb"				) as fi:
            snake_case_				:							Any						 =       self.sp_model.serialized_model_proto()
            fi.write(_SCREAMING_SNAKE_CASE				)

        return (out_vocab_file,)








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






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								snake_case_				:							Optional[Any]						 =       None
								if self.use_labels:
										snake_case_				:							Any						 =       ids_tensor([self.batch_size]		,				self.num_labels				)

								snake_case_				:							Optional[int]						 =       self.get_config()
								return config, pixel_values, labels






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								snake_case_				:							Union[str, Any]						 =       TFRegNetModel(config=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       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 // 32, self.image_size // 32)		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								snake_case_				:							Optional[int]						 =       self.num_labels
								snake_case_				:							Tuple						 =       TFRegNetForImageClassification(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       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       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Optional[Any]						 =       self.prepare_config_and_inputs()
								snake_case_ ,       snake_case_ ,       snake_case_				:							Union[str, Any]						 =       config_and_inputs
								snake_case_				:							Any						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[int]						=					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						A     :   Dict						=					(
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						A     :   List[Any]						=					False
						A     :   List[str]						=					False
						A     :   Optional[Any]						=					False
						A     :   List[Any]						=					False
						A     :   List[Any]						=					False






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[Any]						 =       TFRegNetModelTester(self				)
								snake_case_				:							Any						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								return






						@unittest.skip(reason="RegNet does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				Dict:
								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       _lowerCAmelCase       (	self				) ->				Any:
								super().test_keras_fit()






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






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

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

										snake_case_				:							Dict						 =       ["pixel_values"]
										self.assertListEqual(arg_names[:1]		,				_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								def check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Tuple						 =       model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)		,				training=_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Union[str, Any]						 =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										snake_case_				:							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]		,				)

								snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				:							Optional[int]						 =       ["basic", "bottleneck"]
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												snake_case_				:							Any						 =       layer_type
												snake_case_				:							int						 =       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"]
												snake_case_				:							Any						 =       True

												check_hidden_states_output(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_ ,       snake_case_				:							List[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE={}				):
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				).to_tuple()

										def recursive_check(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
												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:
										snake_case_				:							str						 =       model_class(_SCREAMING_SNAKE_CASE				)

										snake_case_				:							List[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										check_equivalence(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

										snake_case_				:							Union[str, Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, 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				)

										snake_case_				:							Optional[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       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}				)

										snake_case_				:							List[Any]						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Dict						 =       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       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Any:
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_				:							Dict						 =       TFRegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							int						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




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


						'''simple docstring'''




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






						@slow
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							str						 =       TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

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

								# forward pass
								snake_case_				:							Any						 =       model(**_SCREAMING_SNAKE_CASE		,				training=_SCREAMING_SNAKE_CASE				)

								# verify the logits
								snake_case_				:							List[str]						 =       tf.TensorShape((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       tf.constant([-0.4180, -1.5051, -3.4836]				)

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