code: 
  import inspect
  import unittest
  
  from transformers import ResNetConfig, is_flax_available
  from transformers.testing_utils import require_flax, slow
  from transformers.utils import cached_property, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
  
  
  if is_flax_available():
  				import jax
  				import jax.numpy as jnp
  
  				from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import AutoImageProcessor
  
  
  
  
  
  class    _snake_case					(  unittest.TestCase       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a=3		,     _a=32		,     _a=3		,     _a=10		,     _a=[10, 20, 30, 40]		,     _a=[1, 1, 2, 1]		,     _a=True		,     _a=True		,     _a="relu"		,     _a=3		,     _a=None		,     ):
  				__magic_name__	:	Tuple			     =  parent
  				__magic_name__	:	Optional[int]			     =  batch_size
  				__magic_name__	:	Dict			     =  image_size
  				__magic_name__	:	Any			     =  num_channels
  				__magic_name__	:	List[Any]			     =  embeddings_size
  				__magic_name__	:	Optional[int]			     =  hidden_sizes
  				__magic_name__	:	Tuple			     =  depths
  				__magic_name__	:	int			     =  is_training
  				__magic_name__	:	str			     =  use_labels
  				__magic_name__	:	str			     =  hidden_act
  				__magic_name__	:	str			     =  num_labels
  				__magic_name__	:	Dict			     =  scope
  				__magic_name__	:	Tuple			     =  len(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
  				__magic_name__	:	Dict			     =  self.get_config()
  
  				return config, pixel_values
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return ResNetConfig(
  				    num_channels=self.num_channels		,     embeddings_size=self.embeddings_size		,     hidden_sizes=self.hidden_sizes		,     depths=self.depths		,     hidden_act=self.hidden_act		,     num_labels=self.num_labels		,     image_size=self.image_size		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				__magic_name__	:	Tuple			     =  FlaxResNetModel(config=_a	)
  				__magic_name__	:	Optional[int]			     =  model(_a	)
  
  				# Output shape (b, c, h, w)
  				self.parent.assertEqual(
  				    result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				__magic_name__	:	List[Any]			     =  self.num_labels
  				__magic_name__	:	List[str]			     =  FlaxResNetForImageClassification(config=_a	)
  				__magic_name__	:	List[Any]			     =  model(_a	)
  				self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  self.prepare_config_and_inputs()
  				__magic_name__	:	Optional[Any]			     =  config_and_inputs
  				__magic_name__	:	int			     =  {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_flax
  class    _snake_case					(  snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()
  
  			UpperCamelCase__					   =					False
  			UpperCamelCase__					   =					False
  			UpperCamelCase__					   =					False
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Union[str, Any]			     =  FlaxResNetModelTester(self	)
  				__magic_name__	:	Union[str, Any]			     =  ConfigTester(self		,     config_class=_a		,     has_text_modality=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				self.create_and_test_config_common_properties()
  				self.config_tester.create_and_test_config_to_json_string()
  				self.config_tester.create_and_test_config_to_json_file()
  				self.config_tester.create_and_test_config_from_and_save_pretrained()
  				self.config_tester.create_and_test_config_with_num_labels()
  				self.config_tester.check_config_can_be_init_without_params()
  				self.config_tester.check_config_arguments_init()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[int]			     =  self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_image_classification(*_a	)
  
  
  
  
  
  
  
  			@unittest.skip(reason="ResNet does not use inputs_embeds"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			@unittest.skip(reason="ResNet does not support input and output embeddings"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Union[str, Any]			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	str			     =  model_class(_a	)
  					__magic_name__	:	Tuple			     =  inspect.signature(model.__call__	)
  					# signature.parameters is an OrderedDict => so arg_names order is deterministic
  					__magic_name__	:	Tuple			     =  [*signature.parameters.keys()]
  
  					__magic_name__	:	Optional[Any]			     =  ["pixel_values"]
  					self.assertListEqual(arg_names[:1]		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				def check_hidden_states_output(_a		,     _a		,     _a	):
  					__magic_name__	:	str			     =  model_class(_a	)
  
  					__magic_name__	:	Optional[int]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  
  					__magic_name__	:	Any			     =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  					__magic_name__	:	str			     =  self.model_tester.num_stages
  					self.assertEqual(len(_a	)		,     expected_num_stages + 1	)
  
  
  
  
  
  
  
  			@unittest.skip(reason="ResNet does not use feedforward chunking"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	List[Any]			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					with self.subTest(model_class.__name__	):
  						__magic_name__	:	List[Any]			     =  self._prepare_for_class(_a		,     _a	)
  						__magic_name__	:	List[Any]			     =  model_class(_a	)
  
  						@jax.jit
  						def model_jitted(_a		,     **_a	):
  							return model(pixel_values=_a		,     **_a	)
  
  						with self.subTest("JIT Enabled"	):
  							__magic_name__	:	List[str]			     =  model_jitted(**_a	).to_tuple()
  
  						with self.subTest("JIT Disabled"	):
  							with jax.disable_jit():
  								__magic_name__	:	Tuple			     =  model_jitted(**_a	).to_tuple()
  
  						self.assertEqual(len(_a	)		,     len(_a	)	)
  						for jitted_output, output in zip(_a		,     _a	):
  							self.assertEqual(jitted_output.shape		,     output.shape	)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[str]			     =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
  	return image
  
  
  
  
  
  @require_flax
  class    _snake_case					(  unittest.TestCase       ):
  
  
  
  
  
  
  
  			@cached_property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return AutoImageProcessor.from_pretrained("microsoft/resnet-50"	) if is_vision_available() else None
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"	)
  
  				__magic_name__	:	List[str]			     =  self.default_image_processor
  				__magic_name__	:	Tuple			     =  prepare_img()
  				__magic_name__	:	List[Any]			     =  image_processor(images=_a		,     return_tensors="np"	)
  
  				__magic_name__	:	Optional[int]			     =  model(**_a	)
  
  				# verify the logits
  				__magic_name__	:	Any			     =  (1, 1_000)
  				self.assertEqual(outputs.logits.shape		,     _a	)
  
  				__magic_name__	:	List[Any]			     =  jnp.array([-11.10_69, -9.78_77, -8.37_77]	)
  
  				self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,     _a		,     atol=1e-4	)	)
  
style_context: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  				from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
  				snake_case				:	Any							   =						None
  
  
  snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Optional[Any]							   =						{
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Optional[int]							   =						{
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					XLMRobertaTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )
  
  				__magic_name__	:	Optional[int]			     =  vocab_file
  				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				__magic_name__	:	List[str]			     =  [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]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if not self.can_save_slow_tokenizer:
  					raise ValueError(
  					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  					    "tokenizer."	)
  
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  
  				if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	):
  					copyfile(self.vocab_file		,     _a	)
  
  				return (out_vocab_file,)
  