code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  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			,					UpperCamelCase_			,					UpperCamelCase_=3			,					UpperCamelCase_=32			,					UpperCamelCase_=3			,					UpperCamelCase_=10			,					UpperCamelCase_=[10, 20, 30, 40]			,					UpperCamelCase_=[1, 1, 2, 1]			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_="relu"			,					UpperCamelCase_=3			,					UpperCamelCase_=None			,					)			->		Optional[Any]:
  		snake_case__  					=      parent
  		snake_case__  					=      batch_size
  		snake_case__  					=      image_size
  		snake_case__  					=      num_channels
  		snake_case__  					=      embeddings_size
  		snake_case__  					=      hidden_sizes
  		snake_case__  					=      depths
  		snake_case__  					=      is_training
  		snake_case__  					=      use_labels
  		snake_case__  					=      hidden_act
  		snake_case__  					=      num_labels
  		snake_case__  					=      scope
  		snake_case__  					=      len(UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
  		snake_case__  					=      self.get_config()
  
  		return config, pixel_values
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		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      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		List[str]:
  		snake_case__  					=      FlaxResNetModel(config=UpperCamelCase_		)
  		snake_case__  					=      model(UpperCamelCase_		)
  
  		# 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      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[Any]:
  		snake_case__  					=      self.num_labels
  		snake_case__  					=      FlaxResNetForImageClassification(config=UpperCamelCase_		)
  		snake_case__  					=      model(UpperCamelCase_		)
  		self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      self.prepare_config_and_inputs()
  		snake_case__					,     snake_case__  					=      config_and_inputs
  		snake_case__  					=      {'pixel_values': pixel_values}
  		return config, inputs_dict
  
  
  
  
  
  
  @require_flax
  class 							__snake_case    (				__magic_name__					,					unittest.TestCase       ):
  	__lowerCAmelCase						=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()
  
  	__lowerCAmelCase						=     False
  	__lowerCAmelCase						=     False
  	__lowerCAmelCase						=     False
  
  
  
  
  
  	def      _snake_case      (		self		)			->		None:
  		snake_case__  					=      FlaxResNetModelTester(self		)
  		snake_case__  					=      ConfigTester(self			,					config_class=UpperCamelCase_			,					has_text_modality=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		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      _snake_case      (		self		)			->		Dict:
  		return
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_model(*UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)
  
  
  
  
  
  	@unittest.skip(reason='ResNet does not use inputs_embeds'		)
  	def      _snake_case      (		self		)			->		int:
  		pass
  
  
  
  
  
  	@unittest.skip(reason='ResNet does not support input and output embeddings'		)
  	def      _snake_case      (		self		)			->		Tuple:
  		pass
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__					,     snake_case__  					=      self.model_tester.prepare_config_and_inputs_for_common()
  
  		for model_class in self.all_model_classes:
  			snake_case__  					=      model_class(UpperCamelCase_		)
  			snake_case__  					=      inspect.signature(model.__call__		)
  			# signature.parameters is an OrderedDict => so arg_names order is deterministic
  			snake_case__  					=      [*signature.parameters.keys()]
  
  			snake_case__  					=      ['pixel_values']
  			self.assertListEqual(arg_names[:1]			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		def check_hidden_states_output(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		):
  			snake_case__  					=      model_class(UpperCamelCase_		)
  
  			snake_case__  					=      model(**self._prepare_for_class(UpperCamelCase_			,					UpperCamelCase_		)		)
  
  			snake_case__  					=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  			snake_case__  					=      self.model_tester.num_stages
  			self.assertEqual(len(UpperCamelCase_		)			,					expected_num_stages + 1		)
  
  
  
  
  
  	@unittest.skip(reason='ResNet does not use feedforward chunking'		)
  	def      _snake_case      (		self		)			->		str:
  		pass
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__					,     snake_case__  					=      self.model_tester.prepare_config_and_inputs_for_common()
  
  		for model_class in self.all_model_classes:
  			with self.subTest(model_class.__name__		):
  				snake_case__  					=      self._prepare_for_class(UpperCamelCase_			,					UpperCamelCase_		)
  				snake_case__  					=      model_class(UpperCamelCase_		)
  
  				@jax.jit
  				def model_jitted(UpperCamelCase_			,					**UpperCamelCase_		):
  					return model(pixel_values=UpperCamelCase_			,					**UpperCamelCase_		)
  
  				with self.subTest('JIT Enabled'		):
  					snake_case__  					=      model_jitted(**UpperCamelCase_		).to_tuple()
  
  				with self.subTest('JIT Disabled'		):
  					with jax.disable_jit():
  						snake_case__  					=      model_jitted(**UpperCamelCase_		).to_tuple()
  
  				self.assertEqual(len(UpperCamelCase_		)			,					len(UpperCamelCase_		)		)
  				for jitted_output, output in zip(UpperCamelCase_			,					UpperCamelCase_		):
  					self.assertEqual(jitted_output.shape			,					output.shape		)
  
  
  
  
  
  def 	__lowerCamelCase (  )      ->Optional[int]:
  	snake_case__  					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  	return image
  
  
  
  
  
  
  
  @require_flax
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	@cached_property
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None
  
  
  
  
  
  	@slow
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)
  
  		snake_case__  					=      self.default_image_processor
  		snake_case__  					=      prepare_img()
  		snake_case__  					=      image_processor(images=UpperCamelCase_			,					return_tensors='np'		)
  
  		snake_case__  					=      model(**UpperCamelCase_		)
  
  		# verify the logits
  		snake_case__  					=      (1, 1000)
  		self.assertEqual(outputs.logits.shape			,					UpperCamelCase_		)
  
  		snake_case__  					=      jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]		)
  
  		self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,					UpperCamelCase_			,					atol=1E-4		)		)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers import BertTokenizer, BertTokenizerFast
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_vision
  from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
  
  
  if is_vision_available():
  			from PIL import Image
  
  			from transformers import FlavaImageProcessor, FlavaProcessor
  			from transformers.models.flava.image_processing_flava import (
  			    FLAVA_CODEBOOK_MEAN,
  			    FLAVA_CODEBOOK_STD,
  			    FLAVA_IMAGE_MEAN,
  			    FLAVA_IMAGE_STD,
  			)
  
  
  
  
  
  
  @require_vision
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      tempfile.mkdtemp()
  
  		# fmt: off
  		snake_case__  					=      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
  		# fmt: on
  		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)
  
  		with open(self.vocab_file			,					'w'			,					encoding='utf-8'		) as fp:
  			fp.write(''.join([x + '\n' for x in vocab_tokens]		)		)
  
  		snake_case__  					=      {
  		    'image_mean': FLAVA_IMAGE_MEAN,
  		    'image_std': FLAVA_IMAGE_STD,
  		    'do_normalize': True,
  		    'do_resize': True,
  		    'size': 224,
  		    'do_center_crop': True,
  		    'crop_size': 224,
  		    'input_size_patches': 14,
  		    'total_mask_patches': 75,
  		    'mask_group_max_patches': None,
  		    'mask_group_min_patches': 16,
  		    'mask_group_min_aspect_ratio': 0.3,
  		    'mask_group_max_aspect_ratio': None,
  		    'codebook_do_resize': True,
  		    'codebook_size': 112,
  		    'codebook_do_center_crop': True,
  		    'codebook_crop_size': 112,
  		    'codebook_do_map_pixels': True,
  		    'codebook_do_normalize': True,
  		    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
  		    'codebook_image_std': FLAVA_CODEBOOK_STD,
  		}
  
  		snake_case__  					=      os.path.join(self.tmpdirname			,					UpperCamelCase_		)
  		with open(self.image_processor_file			,					'w'			,					encoding='utf-8'		) as fp:
  			json.dump(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
  		return BertTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
  		return BertTokenizerFast.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
  		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      [np.random.randint(255			,					size=(3, 30, 400)			,					dtype=np.uinta		)]
  
  		snake_case__  					=      [Image.fromarray(np.moveaxis(UpperCamelCase_			,					0			,					-1		)		) for x in image_inputs]
  
  		return image_inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      self.get_tokenizer()
  		snake_case__  					=      self.get_rust_tokenizer()
  		snake_case__  					=      self.get_image_processor()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  		processor_slow.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname			,					use_fast=UpperCamelCase_		)
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  		processor_fast.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname		)
  
  		self.assertEqual(processor_slow.tokenizer.get_vocab()			,					tokenizer_slow.get_vocab()		)
  		self.assertEqual(processor_fast.tokenizer.get_vocab()			,					tokenizer_fast.get_vocab()		)
  		self.assertEqual(tokenizer_slow.get_vocab()			,					tokenizer_fast.get_vocab()		)
  		self.assertIsInstance(processor_slow.tokenizer			,					UpperCamelCase_		)
  		self.assertIsInstance(processor_fast.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor_slow.image_processor.to_json_string()			,					image_processor.to_json_string()		)
  		self.assertEqual(processor_fast.image_processor.to_json_string()			,					image_processor.to_json_string()		)
  		self.assertIsInstance(processor_slow.image_processor			,					UpperCamelCase_		)
  		self.assertIsInstance(processor_fast.image_processor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      FlavaProcessor(tokenizer=self.get_tokenizer()			,					image_processor=self.get_image_processor()		)
  		processor.save_pretrained(self.tmpdirname		)
  
  		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
  		snake_case__  					=      self.get_image_processor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		snake_case__  					=      FlavaProcessor.from_pretrained(
  		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.image_processor.to_json_string()			,					image_processor_add_kwargs.to_json_string()		)
  		self.assertIsInstance(processor.image_processor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      image_processor(UpperCamelCase_			,					return_tensors='np'		)
  		snake_case__  					=      processor(images=UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  		# With rest of the args
  		random.seed(1234		)
  		snake_case__  					=      image_processor(
  		    UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
  		random.seed(1234		)
  		snake_case__  					=      processor(
  		    images=UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  
  		snake_case__  					=      processor(text=UpperCamelCase_		)
  
  		snake_case__  					=      tokenizer(UpperCamelCase_		)
  
  		for key in encoded_tok.keys():
  			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)
  
  		self.assertListEqual(list(inputs.keys()		)			,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']		)
  
  		# add extra args
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_image_mask=UpperCamelCase_		)
  
  		self.assertListEqual(
  		    list(inputs.keys()		)			,					[
  		        'input_ids',
  		        'token_type_ids',
  		        'attention_mask',
  		        'pixel_values',
  		        'codebook_pixel_values',
  		        'bool_masked_pos',
  		    ]			,					)
  
  		# test if it raises when no input is passed
  		with pytest.raises(UpperCamelCase_		):
  			processor()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
  		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)
  
  		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)
  
  		self.assertListEqual(list(inputs.keys()		)			,					processor.model_input_names		)
  
  
  
  