
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__lowerCamelCase								=					get_logger(__name__)

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

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

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

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


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

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

__lowerCamelCase								=					'N/A'
__lowerCamelCase								=					False

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

__lowerCamelCase								=					'N/A'
__lowerCamelCase								=					False

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


__lowerCamelCase								=					'N/A'
__lowerCamelCase								=					False

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


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

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


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

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

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

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

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

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

# Download count for the website
__lowerCamelCase								=					(
    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)
__lowerCamelCase								=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
__lowerCamelCase								=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
__lowerCamelCase								=					4 << 3_0

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

# Parquet configuration
__lowerCamelCase								=					1_0_0
__lowerCamelCase								=					1_0_0
__lowerCamelCase								=					1_0_0

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

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

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

__lowerCamelCase								=					'datasets_modules'

__lowerCamelCase								=					2_5_5

# Streaming
__lowerCamelCase								=					2_0
__lowerCamelCase								=					5

# Datasets without script
__lowerCamelCase								=					2_0_0
__lowerCamelCase								=					1_0
__lowerCamelCase								=					2_0_0

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




