
"""simple docstring"""




import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

SCREAMING_SNAKE_CASE__							=    {
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

SCREAMING_SNAKE_CASE__							=    {
    """microsoft/biogpt""": 1024,
}




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]      )					->						Any:
				__lowercase								= set()
				__lowercase								= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)      )
								__lowercase								= char
				return pairs
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Optional[Any]         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						Dict         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						int         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Union[str, Any]         =							["input_ids", "attention_mask"]





							def __init__(			self  :       Any					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]="<unk>"					,			_UpperCAmelCase  :       str="<s>"					,			_UpperCAmelCase  :       List[str]="</s>"					,			_UpperCAmelCase  :       Tuple="</s>"					,			_UpperCAmelCase  :       Tuple="<pad>"					,			**_UpperCAmelCase  :       Optional[Any]					,			)						->		Optional[Any]:



											"""simple docstring"""


											super().__init__(
											    bos_token=_UpperCAmelCase					,			eos_token=_UpperCAmelCase					,			sep_token=_UpperCAmelCase					,			unk_token=_UpperCAmelCase					,			pad_token=_UpperCAmelCase					,			**_UpperCAmelCase					,			)

											try:
															import sacremoses
											except ImportError:
															raise ImportError(
															    'You need to install sacremoses to use BioGptTokenizer. '
															    'See https://pypi.org/project/sacremoses/ for installation.'    )

											__lowercase								= 'en'
											__lowercase								= sacremoses
											# cache of sm.MosesTokenizer instance
											__lowercase								= {}
											__lowercase								= {}
											with open(_UpperCAmelCase					,			encoding='utf-8'    ) as vocab_handle:
															__lowercase								= json.load(_UpperCAmelCase    )
											__lowercase								= {v: k for k, v in self.encoder.items()}
											with open(_UpperCAmelCase					,			encoding='utf-8'    ) as merges_handle:
															__lowercase								= merges_handle.read().split('\n'    )[:-1]
											__lowercase								= [tuple(merge.split()[:2]    ) for merge in merges]
											__lowercase								= dict(zip(_UpperCAmelCase					,			range(len(_UpperCAmelCase    )    )    )    )
											__lowercase								= {}





							@property
							def       a__			(			self  :       int    )						->		Tuple:



											"""simple docstring"""


											return len(self.encoder    )





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



											"""simple docstring"""


											return dict(self.encoder					,			**self.added_tokens_encoder    )





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Tuple    )						->		Tuple:



											"""simple docstring"""


											if lang not in self.cache_moses_tokenizer:
															__lowercase								= self.sm.MosesTokenizer(lang=_UpperCAmelCase    )
															__lowercase								= moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    _UpperCAmelCase					,			aggressive_dash_splits=_UpperCAmelCase					,			return_str=_UpperCAmelCase					,			escape=_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Dict    )						->		str:



											"""simple docstring"""


											if lang not in self.cache_moses_detokenizer:
															__lowercase								= self.sm.MosesDetokenizer(lang=_UpperCAmelCase    )
															__lowercase								= moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase    )





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       int    )						->		int:



											"""simple docstring"""


											__lowercase								= tuple(token[:-1]    ) + (token[-1] + '</w>',)
											if token in self.cache:
															return self.cache[token]
											__lowercase								= get_pairs(_UpperCAmelCase    )

											if not pairs:
															return token + "</w>"

											while True:
															__lowercase								= min(_UpperCAmelCase					,			key=lambda _UpperCAmelCase    : self.bpe_ranks.get(_UpperCAmelCase					,			float('inf'    )    )    )
															if bigram not in self.bpe_ranks:
																			break
															__lowercase		, __lowercase								= bigram
															__lowercase								= []
															__lowercase								= 0
															while i < len(_UpperCAmelCase    ):
																			try:
																							__lowercase								= word.index(_UpperCAmelCase					,			_UpperCAmelCase    )
																			except ValueError:
																							new_word.extend(word[i:]    )
																							break
																			else:
																							new_word.extend(word[i:j]    )
																							__lowercase								= j

																			if word[i] == first and i < len(_UpperCAmelCase    ) - 1 and word[i + 1] == second:
																							new_word.append(first + second    )
																							i += 2
																			else:
																							new_word.append(word[i]    )
																							i += 1
															__lowercase								= tuple(_UpperCAmelCase    )
															__lowercase								= new_word
															if len(_UpperCAmelCase    ) == 1:
																			break
															else:
																			__lowercase								= get_pairs(_UpperCAmelCase    )
											__lowercase								= ' '.join(_UpperCAmelCase    )
											if word == "\n  </w>":
															__lowercase								= '\n</w>'
											__lowercase								= word
											return word





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Union[str, Any]=False    )						->		Optional[Any]:



											"""simple docstring"""


											if bypass_tokenizer:
															__lowercase								= text.split()
											else:
															__lowercase								= self.moses_tokenize(_UpperCAmelCase					,			self.lang    )

											__lowercase								= []
											for token in text:
															if token:
																			split_tokens.extend(list(self.bpe(_UpperCAmelCase    ).split(' '    )    )    )

											return split_tokens





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       Any    )						->		List[Any]:



											"""simple docstring"""


											return self.encoder.get(_UpperCAmelCase					,			self.encoder.get(self.unk_token    )    )





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[Any]    )						->		str:



											"""simple docstring"""


											return self.decoder.get(_UpperCAmelCase					,			self.unk_token    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= [t.replace(' '					,			''    ).replace('</w>'					,			' '    ) for t in tokens]
											__lowercase								= ''.join(_UpperCAmelCase    ).split()
											# detokenize
											__lowercase								= self.moses_detokenize(_UpperCAmelCase					,			self.lang    )
											return text





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											if token_ids_a is None:
															return [self.sep_token_id] + token_ids_a
											__lowercase								= [self.sep_token_id]
											return sep + token_ids_a + sep + token_ids_a





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None					,			_UpperCAmelCase  :       bool = False    )						->		List[int]:



											"""simple docstring"""


											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCAmelCase					,			token_ids_a=_UpperCAmelCase					,			already_has_special_tokens=_UpperCAmelCase    )
											# no bos used in fairseq
											if token_ids_a is not None:
															return [1] + ([0] * len(_UpperCAmelCase    )) + [1] + ([0] * len(_UpperCAmelCase    ))
											return [1] + ([0] * len(_UpperCAmelCase    ))





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											__lowercase								= [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
															return len(token_ids_a + sep    ) * [0]
											return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Optional[str] = None    )						->		Tuple[str]:



											"""simple docstring"""


											if not os.path.isdir(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
															return
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

											with open(_UpperCAmelCase					,			'w'					,			encoding='utf-8'    ) as f:
															f.write(json.dumps(self.encoder					,			indent=2					,			sort_keys=_UpperCAmelCase					,			ensure_ascii=_UpperCAmelCase    ) + '\n'    )

											__lowercase								= 0
											with open(_UpperCAmelCase					,			'w'					,			encoding='utf-8'    ) as writer:
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,			key=lambda _UpperCAmelCase    : kv[1]    ):
																			if index != token_index:
																							logger.warning(
																							    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																							    ' Please check that the tokenizer is not corrupted!'    )
																							__lowercase								= token_index
																			writer.write(' '.join(_UpperCAmelCase    ) + '\n'    )
																			index += 1

											return vocab_file, merge_file





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



											"""simple docstring"""


											__lowercase								= self.__dict__.copy()
											__lowercase								= None
											return state





							def __setstate__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= d

											try:
															import sacremoses
											except ImportError:
															raise ImportError(
															    'You need to install sacremoses to use XLMTokenizer. '
															    'See https://pypi.org/project/sacremoses/ for installation.'    )

											__lowercase								= sacremoses







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor
class      A__       :





							def __init__(			self  :       Any					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]=13					,			_UpperCAmelCase  :       Union[str, Any]=32					,			_UpperCAmelCase  :       Optional[Any]=3					,			_UpperCAmelCase  :       Optional[Any]=4					,			_UpperCAmelCase  :       Optional[Any]=[10, 20, 30, 40]					,			_UpperCAmelCase  :       Dict=[2, 2, 3, 2]					,			_UpperCAmelCase  :       Union[str, Any]=True					,			_UpperCAmelCase  :       str=True					,			_UpperCAmelCase  :       str=37					,			_UpperCAmelCase  :       List[str]="gelu"					,			_UpperCAmelCase  :       List[str]=10					,			_UpperCAmelCase  :       Optional[int]=0.02					,			_UpperCAmelCase  :       List[Any]=3					,			_UpperCAmelCase  :       List[Any]=None					,			)						->		str:



											"""simple docstring"""


											__lowercase								= parent
											__lowercase								= batch_size
											__lowercase								= image_size
											__lowercase								= num_channels
											__lowercase								= num_stages
											__lowercase								= hidden_sizes
											__lowercase								= depths
											__lowercase								= is_training
											__lowercase								= use_labels
											__lowercase								= intermediate_size
											__lowercase								= hidden_act
											__lowercase								= type_sequence_label_size
											__lowercase								= initializer_range
											__lowercase								= scope





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



											"""simple docstring"""


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

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

											__lowercase								= self.get_config()

											return config, pixel_values, labels





							def       a__			(			self  :       List[str]    )						->		Tuple:



											"""simple docstring"""


											return ConvNextConfig(
											    num_channels=self.num_channels					,			hidden_sizes=self.hidden_sizes					,			depths=self.depths					,			num_stages=self.num_stages					,			hidden_act=self.hidden_act					,			is_decoder=_UpperCAmelCase					,			initializer_range=self.initializer_range					,			)





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Any    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= TFConvNextModel(config=_UpperCAmelCase    )
											__lowercase								= model(_UpperCAmelCase					,			training=_UpperCAmelCase    )
											# 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       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.type_sequence_label_size
											__lowercase								= TFConvNextForImageClassification(_UpperCAmelCase    )
											__lowercase								= model(_UpperCAmelCase					,			labels=_UpperCAmelCase					,			training=_UpperCAmelCase    )
											self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.type_sequence_label_size)    )





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



											"""simple docstring"""


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






@require_tf
class      A__       (						lowerCAmelCase__   ,						lowerCAmelCase__   ,						unittest.TestCase    ):
							lowerCAmelCase__		:						List[Any]         =							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
							lowerCAmelCase__		:						List[Any]         =							(
							    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
							    if is_tf_available()
							    else {}
							)

							lowerCAmelCase__		:						Union[str, Any]         =							False
							lowerCAmelCase__		:						Union[str, Any]         =							False
							lowerCAmelCase__		:						Any         =							False
							lowerCAmelCase__		:						Optional[Any]         =							False
							lowerCAmelCase__		:						Optional[int]         =							False





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



											"""simple docstring"""


											__lowercase								= TFConvNextModelTester(self    )
											__lowercase								= ConfigTester(
											    self					,			config_class=_UpperCAmelCase					,			has_text_modality=_UpperCAmelCase					,			hidden_size=37					,			)





							@unittest.skip(reason='ConvNext does not use inputs_embeds'    )
							def       a__			(			self  :       List[Any]    )						->		Tuple:



											"""simple docstring"""


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



											"""simple docstring"""


											super().test_keras_fit()





							@unittest.skip(reason='ConvNext does not support input and output embeddings'    )
							def       a__			(			self  :       Optional[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											pass





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



											"""simple docstring"""


											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

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

															__lowercase								= ['pixel_values']
															self.assertListEqual(arg_names[:1]					,			_UpperCAmelCase    )





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



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase    )





							@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.'					,			)
							def       a__			(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											super().test_dataset_conversion()





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



											"""simple docstring"""


											def check_hidden_states_output(_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       str    ):
															__lowercase								= model_class(_UpperCAmelCase    )

															__lowercase								= model(**self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )    )
															__lowercase								= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__lowercase								= self.model_tester.num_stages
															self.assertEqual(len(_UpperCAmelCase    )					,			expected_num_stages + 1    )

															# ConvNext'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 // 4, self.model_tester.image_size // 4]					,			)

											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__lowercase								= True
															check_hidden_states_output(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__lowercase								= True

															check_hidden_states_output(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )





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



											"""simple docstring"""


											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

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

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

																			recursive_check(_UpperCAmelCase					,			_UpperCAmelCase    )

											for model_class in self.all_model_classes:
															__lowercase								= model_class(_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			{'output_hidden_states': True}    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			{'output_hidden_states': True}    )





							def       a__			(			self  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase    )





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



											"""simple docstring"""


											__lowercase								= TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'    )
											self.assertIsNotNone(_UpperCAmelCase    )






def 							__SCREAMING_SNAKE_CASE       (		)					->						List[str]:
				__lowercase								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
				return image
@require_tf
@require_vision
class      A__       (						unittest.TestCase    ):





							@cached_property
							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'    ) if is_vision_available() else None





							@slow
							def       a__			(			self  :       List[Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'    )

											__lowercase								= self.default_image_processor
											__lowercase								= prepare_img()
											__lowercase								= image_processor(images=_UpperCAmelCase					,			return_tensors='tf'    )

											# forward pass
											__lowercase								= model(**_UpperCAmelCase    )

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

											__lowercase								= tf.constant([-0.0_260, -0.4_739, 0.1_911]    )

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





