




import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


UpperCamelCase								=     get_logger(__name__)

# Datasets
UpperCamelCase								=     'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
UpperCamelCase								=     'https://cdn-datasets.huggingface.co/datasets/datasets'
UpperCamelCase								=     'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
UpperCamelCase								=     'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
UpperCamelCase								=     'https://cdn-datasets.huggingface.co/datasets/metric'
UpperCamelCase								=     'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
UpperCamelCase								=     os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
UpperCamelCase								=     HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
UpperCamelCase								=     'main'

UpperCamelCase								=     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
UpperCamelCase								=     {'1', 'ON', 'YES', 'TRUE'}
UpperCamelCase								=     ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
UpperCamelCase								=     version.parse(importlib_metadata.version('dill'))
UpperCamelCase								=     version.parse(importlib_metadata.version('pandas'))
UpperCamelCase								=     version.parse(importlib_metadata.version('pyarrow'))

UpperCamelCase								=     os.environ.get('USE_TF', 'AUTO').upper()
UpperCamelCase								=     os.environ.get('USE_TORCH', 'AUTO').upper()
UpperCamelCase								=     os.environ.get('USE_JAX', 'AUTO').upper()

UpperCamelCase								=     'N/A'
UpperCamelCase								=     False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
     UpperCamelCase								=     importlib.util.find_spec('torch') is not None
     if TORCH_AVAILABLE:
          try:
               UpperCamelCase								=     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')

UpperCamelCase								=     'N/A'
UpperCamelCase								=     False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
     UpperCamelCase								=     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:
                    UpperCamelCase								=     version.parse(importlib_metadata.version(package))
               except importlib_metadata.PackageNotFoundError:
                    continue
               else:
                    break
          else:
               UpperCamelCase								=     False
     if TF_AVAILABLE:
          if TF_VERSION.major < 2:
               logger.info(F"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
               UpperCamelCase								=     False
          else:
               logger.info(F"""TensorFlow version {TF_VERSION} available.""")
else:
     logger.info('Disabling Tensorflow because USE_TORCH is set')


UpperCamelCase								=     'N/A'
UpperCamelCase								=     False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
     UpperCamelCase								=     importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
     if JAX_AVAILABLE:
          try:
               UpperCamelCase								=     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')


UpperCamelCase								=     os.environ.get('USE_BEAM', 'AUTO').upper()
UpperCamelCase								=     'N/A'
UpperCamelCase								=     False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
     try:
          UpperCamelCase								=     version.parse(importlib_metadata.version('apache_beam'))
          UpperCamelCase								=     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
UpperCamelCase								=     importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
UpperCamelCase								=     importlib.util.find_spec('PIL') is not None
UpperCamelCase								=     importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
UpperCamelCase								=     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
UpperCamelCase								=     importlib.util.find_spec('rarfile') is not None
UpperCamelCase								=     importlib.util.find_spec('zstandard') is not None
UpperCamelCase								=     importlib.util.find_spec('lz4') is not None
UpperCamelCase								=     importlib.util.find_spec('py7zr') is not None


# Cache location
UpperCamelCase								=     '~/.cache'
UpperCamelCase								=     os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
UpperCamelCase								=     os.path.join(XDG_CACHE_HOME, 'huggingface')
UpperCamelCase								=     os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))

UpperCamelCase								=     os.path.join(HF_CACHE_HOME, 'datasets')
UpperCamelCase								=     Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

UpperCamelCase								=     os.path.join(HF_CACHE_HOME, 'metrics')
UpperCamelCase								=     Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

UpperCamelCase								=     os.path.join(HF_CACHE_HOME, 'modules')
UpperCamelCase								=     Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

UpperCamelCase								=     'downloads'
UpperCamelCase								=     os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
UpperCamelCase								=     Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

UpperCamelCase								=     'extracted'
UpperCamelCase								=     os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
UpperCamelCase								=     Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
UpperCamelCase								=     (
    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)
UpperCamelCase								=     1000

# Size of the preloaded record batch in `Dataset.__iter__`
UpperCamelCase								=     10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
UpperCamelCase								=     4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
UpperCamelCase								=     '500MB'

# Parquet configuration
UpperCamelCase								=     100
UpperCamelCase								=     100
UpperCamelCase								=     100

# Offline mode
UpperCamelCase								=     os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES

# In-memory
UpperCamelCase								=     0  # Disabled
UpperCamelCase								=     float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
UpperCamelCase								=     'dataset.arrow'
UpperCamelCase								=     'indices.arrow'
UpperCamelCase								=     'state.json'
UpperCamelCase								=     'dataset_info.json'
UpperCamelCase								=     'dataset_infos.json'
UpperCamelCase								=     'LICENSE'
UpperCamelCase								=     'metric_info.json'
UpperCamelCase								=     'dataset_dict.json'

UpperCamelCase								=     'datasets_modules'

UpperCamelCase								=     255

# Streaming
UpperCamelCase								=     20
UpperCamelCase								=     5

# Datasets without script
UpperCamelCase								=     200
UpperCamelCase								=     10
UpperCamelCase								=     200

# Progress bars
UpperCamelCase								=     0.05  # 20 progress updates per sec






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image





class  __lowerCamelCase       (    unittest.TestCase	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Any   )      ->       Tuple:
         lowerCAmelCase__		= load_tool("image-captioning"   )
         self.tool.setup()
         lowerCAmelCase__		= load_tool("image-captioning"   ,	remote=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Any   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Dict   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )
