code: 
  
  """simple docstring"""
  
  
  
  
  import importlib
  import os
  import platform
  from pathlib import Path
  
  from packaging import version
  
  from .utils.logging import get_logger
  
  
  __A  :					Union[str, Any]												= get_logger(__name__)
  
  # Datasets
  __A  :					str												= 'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
  __A  :					Union[str, Any]												= 'https://cdn-datasets.huggingface.co/datasets/datasets'
  __A  :					str												= 'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'
  
  # Metrics
  __A  :					Optional[Any]												= 'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
  __A  :					str												= 'https://cdn-datasets.huggingface.co/datasets/metric'
  __A  :					List[str]												= 'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'
  
  # Hub
  __A  :					List[str]												= os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
  __A  :					Optional[int]												= HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
  __A  :					Optional[Any]												= 'main'
  
  __A  :					Tuple												= version.parse(platform.python_version())
  
  if PY_VERSION < version.parse('3.8'):
  	import importlib_metadata
  else:
  	import importlib.metadata as importlib_metadata
  
  # General environment variables accepted values for booleans
  __A  :					str												= {'1', 'ON', 'YES', 'TRUE'}
  __A  :					List[Any]												= ENV_VARS_TRUE_VALUES.union({'AUTO'})
  
  
  # Imports
  __A  :					List[Any]												= version.parse(importlib_metadata.version('dill'))
  __A  :					Dict												= version.parse(importlib_metadata.version('pandas'))
  __A  :					int												= version.parse(importlib_metadata.version('pyarrow'))
  
  __A  :					Dict												= os.environ.get('USE_TF', 'AUTO').upper()
  __A  :					Union[str, Any]												= os.environ.get('USE_TORCH', 'AUTO').upper()
  __A  :					List[str]												= os.environ.get('USE_JAX', 'AUTO').upper()
  
  __A  :					Tuple												= 'N/A'
  __A  :					Optional[int]												= False
  
  if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
  	__A  :					Optional[Any]												= importlib.util.find_spec('torch') is not None
  	if TORCH_AVAILABLE:
  		try:
  			__A  :					int												= version.parse(importlib_metadata.version('torch'))
  			logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
  		except importlib_metadata.PackageNotFoundError:
  			pass
  else:
  	logger.info('Disabling PyTorch because USE_TF is set')
  
  __A  :					List[Any]												= 'N/A'
  __A  :					Optional[int]												= False
  
  if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
  	__A  :					Union[str, Any]												= importlib.util.find_spec('tensorflow') is not None
  	if TF_AVAILABLE:
  		# For the metadata, we have to look for both tensorflow and tensorflow-cpu
  		for package in [
  		    "tensorflow",
  		    "tensorflow-cpu",
  		    "tensorflow-gpu",
  		    "tf-nightly",
  		    "tf-nightly-cpu",
  		    "tf-nightly-gpu",
  		    "intel-tensorflow",
  		    "tensorflow-rocm",
  		    "tensorflow-macos",
  		]:
  			try:
  				__A  :					str												= version.parse(importlib_metadata.version(package))
  			except importlib_metadata.PackageNotFoundError:
  				continue
  			else:
  				break
  		else:
  			__A  :					List[str]												= False
  	if TF_AVAILABLE:
  		if TF_VERSION.major < 2:
  			logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
  			__A  :					str												= False
  		else:
  			logger.info(f"""TensorFlow version {TF_VERSION} available.""")
  else:
  	logger.info('Disabling Tensorflow because USE_TORCH is set')
  
  
  __A  :					Any												= 'N/A'
  __A  :					List[str]												= False
  
  if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
  	__A  :					Any												= importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
  	if JAX_AVAILABLE:
  		try:
  			__A  :					Union[str, Any]												= version.parse(importlib_metadata.version('jax'))
  			logger.info(f"""JAX version {JAX_VERSION} available.""")
  		except importlib_metadata.PackageNotFoundError:
  			pass
  else:
  	logger.info('Disabling JAX because USE_JAX is set to False')
  
  
  __A  :					Optional[Any]												= os.environ.get('USE_BEAM', 'AUTO').upper()
  __A  :					int												= 'N/A'
  __A  :					str												= False
  if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
  	try:
  		__A  :					List[Any]												= version.parse(importlib_metadata.version('apache_beam'))
  		__A  :					Any												= True
  		logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
  	except importlib_metadata.PackageNotFoundError:
  		pass
  else:
  	logger.info('Disabling Apache Beam because USE_BEAM is set to False')
  
  
  # Optional tools for data loading
  __A  :					List[Any]												= importlib.util.find_spec('sqlalchemy') is not None
  
  # Optional tools for feature decoding
  __A  :					List[Any]												= importlib.util.find_spec('PIL') is not None
  __A  :					Tuple												= importlib.util.find_spec('soundfile') is not None and version.parse(
      importlib.import_module('soundfile').__libsndfile_version__
  ) >= version.parse('1.0.31')
  __A  :					Union[str, Any]												= importlib.util.find_spec('soundfile') is not None and version.parse(
      importlib.import_module('soundfile').__libsndfile_version__
  ) >= version.parse('1.1.0')
  
  # Optional compression tools
  __A  :					str												= importlib.util.find_spec('rarfile') is not None
  __A  :					List[Any]												= importlib.util.find_spec('zstandard') is not None
  __A  :					Union[str, Any]												= importlib.util.find_spec('lz4') is not None
  __A  :					List[str]												= importlib.util.find_spec('py7zr') is not None
  
  
  # Cache location
  __A  :					str												= '~/.cache'
  __A  :					List[Any]												= os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
  __A  :					Any												= os.path.join(XDG_CACHE_HOME, 'huggingface')
  __A  :					Optional[Any]												= os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))
  
  __A  :					Dict												= os.path.join(HF_CACHE_HOME, 'datasets')
  __A  :					Any												= Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))
  
  __A  :					Dict												= os.path.join(HF_CACHE_HOME, 'metrics')
  __A  :					Dict												= Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))
  
  __A  :					Optional[int]												= os.path.join(HF_CACHE_HOME, 'modules')
  __A  :					List[Any]												= Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))
  
  __A  :					str												= 'downloads'
  __A  :					List[str]												= os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
  __A  :					Optional[Any]												= Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))
  
  __A  :					Any												= 'extracted'
  __A  :					Dict												= os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
  __A  :					int												= Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))
  
  # Download count for the website
  __A  :					int												= (
      os.environ.get('HF_UPDATE_DOWNLOAD_COUNTS', 'AUTO').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
  )
  
  # Batch size constants. For more info, see:
  # https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
  __A  :					Optional[int]												= 10_00
  
  # Size of the preloaded record batch in `Dataset.__iter__`
  __A  :					List[str]												= 10
  
  # Pickling tables works only for small tables (<4GiB)
  # For big tables, we write them on disk instead
  __A  :					List[Any]												= 4 << 30
  
  # Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
  __A  :					List[Any]												= '500MB'
  
  # Parquet configuration
  __A  :					Optional[Any]												= 1_00
  __A  :					Dict												= 1_00
  __A  :					Any												= 1_00
  
  # Offline mode
  __A  :					int												= os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES
  
  # In-memory
  __A  :					List[str]												= 0  # Disabled
  __A  :					str												= float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))
  
  # File names
  __A  :					Union[str, Any]												= 'dataset.arrow'
  __A  :					Any												= 'indices.arrow'
  __A  :					int												= 'state.json'
  __A  :					Optional[Any]												= 'dataset_info.json'
  __A  :					Optional[Any]												= 'dataset_infos.json'
  __A  :					Optional[Any]												= 'LICENSE'
  __A  :					Optional[int]												= 'metric_info.json'
  __A  :					Dict												= 'dataset_dict.json'
  
  __A  :					Any												= 'datasets_modules'
  
  __A  :					Optional[Any]												= 2_55
  
  # Streaming
  __A  :					Tuple												= 20
  __A  :					Optional[Any]												= 5
  
  # Datasets without script
  __A  :					Any												= 2_00
  __A  :					int												= 10
  __A  :					str												= 2_00
  
  # Progress bars
  __A  :					Optional[int]												= 0.05  # 20 progress updates per sec
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      PreTrainedTokenizer,
      is_vision_available,
  )
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_tf,
      require_torch,
      require_torch_or_tf,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_vision_available():
  	from PIL import Image
  else:
  
  
  
  
  
  	class     __UpperCamelCase				:
  
  
  			@staticmethod
  			def       SCREAMING_SNAKE_CASE__     (*__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Any):
  								pass
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class     __UpperCamelCase				(     unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  		SCREAMING_SNAKE_CASE              =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      ImageClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,  image_processor=__SCREAMING_SNAKE_CASE  ,  top_k=2)
  							A    =      [
  							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
  							    "http://images.cocodataset.org/val2017/000000039769.jpg",
  							]
  							return image_classifier, examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  							A    =      image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")
  
  							self.assertEqual(
  							    __SCREAMING_SNAKE_CASE  ,  [
  							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							    ]  ,  )
  
  							import datasets
  
  							A    =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,  "image"  ,  split="test")
  
  							# Accepts URL + PIL.Image + lists
  							A    =      image_classifier(
  							    [
  							        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							        # RGBA
  							        dataset[0]["file"],
  							        # LA
  							        dataset[1]["file"],
  							        # L
  							        dataset[2]["file"],
  							    ])
  							self.assertEqual(
  							    __SCREAMING_SNAKE_CASE  ,  [
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							        [
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
  							        ],
  							    ]  ,  )
  
  
  		@require_torch
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							A    =      "hf-internal-testing/tiny-random-vit"
  							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE)
  
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )
  
  							A    =      image_classifier(
  							    [
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							    ]  ,  top_k=2  ,  )
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							    ]  ,  )
  
  
  		@require_tf
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    =      "hf-internal-testing/tiny-random-vit"
  							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE  ,  framework="tf")
  
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )
  
  							A    =      image_classifier(
  							    [
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							        "http://images.cocodataset.org/val2017/000000039769.jpg",
  							    ]  ,  top_k=2  ,  )
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							    ]  ,  )
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							A    =      PreTrainedTokenizer()
  
  							# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
  							A    =      pipeline(
  							    "image-classification"  ,  model="hf-internal-testing/tiny-random-vit"  ,  tokenizer=__SCREAMING_SNAKE_CASE)
  
  							self.assertIs(image_classifier.tokenizer  ,  __SCREAMING_SNAKE_CASE)
  
  
  		@slow
  		@require_torch
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							# Perceiver is not tested by `run_pipeline_test` properly.
  							# That is because the type of feature_extractor and model preprocessor need to be kept
  							# in sync, which is not the case in the current design
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-conv")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
  							        {"score": 0.3_2_1, "label": "tiger cat"},
  							        {"score": 0.0_5_0_2, "label": "Egyptian cat"},
  							        {"score": 0.0_1_3_7, "label": "crib, cot"},
  							        {"score": 0.0_0_7, "label": "radiator"},
  							    ]  ,  )
  
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-fourier")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
  							        {"score": 0.1_3_0_9, "label": "tiger cat"},
  							        {"score": 0.0_7_2_2, "label": "Egyptian cat"},
  							        {"score": 0.0_7_0_7, "label": "remote control, remote"},
  							        {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
  							    ]  ,  )
  
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-learned")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
  							        {"score": 0.2_3_6_2, "label": "Egyptian cat"},
  							        {"score": 0.1_8_5_6, "label": "tiger cat"},
  							        {"score": 0.0_3_2_4, "label": "remote control, remote"},
  							        {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
  							    ]  ,  )
  
  
  
  
  
  