
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__magic_name__           =					get_logger(__name__)

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

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

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

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


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

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

__magic_name__           =					'''N/A'''
__magic_name__           =					False

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

__magic_name__           =					'''N/A'''
__magic_name__           =					False

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


__magic_name__           =					'''N/A'''
__magic_name__           =					False

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


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

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


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

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

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

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

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

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

# Download count for the website
__magic_name__           =					(
    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)
__magic_name__           =					1_000

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

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

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

# Parquet configuration
__magic_name__           =					100
__magic_name__           =					100
__magic_name__           =					100

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

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

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

__magic_name__           =					'''datasets_modules'''

__magic_name__           =					255

# Streaming
__magic_name__           =					20
__magic_name__           =					5

# Datasets without script
__magic_name__           =					200
__magic_name__           =					10
__magic_name__           =					200

# Progress bars
__magic_name__           =					0.0_5  # 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    _SCREAMING_SNAKE_CASE					( unittest.TestCase    ,   __UpperCamelCase	):
      def A_     (     self	):
          snake_case__  =			load_tool("image-captioning"	)
          self.tool.setup()
          snake_case__  =			load_tool("image-captioning"						,		remote=lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



