






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





'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowercase__		    =	get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		tempfile.mkdtemp()

				snake_case   :    Optional[int]			       =		SpeechTaTokenizer(UpperCamelCase__	)
				tokenizer.save_pretrained(self.tmpdirname	)

				snake_case   :    Any			       =		{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1e-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				snake_case   :    Tuple			       =		os.path.join(self.tmpdirname   ,     UpperCamelCase__	)
				with open(self.feature_extraction_file   ,     '''w'''   ,     encoding='''utf-8'''	) as fp:
						fp.write(json.dumps(UpperCamelCase__	) + '''\n'''	)



		def 	lowerCAmelCase							(	self							:       Dict   ,     **UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       int	)		->       Optional[Any]:






				"""simple docstring"""







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Any:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_tokenizer()
				snake_case   :    List[str]			       =		self.get_feature_extractor()

				snake_case   :    Dict			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				processor.save_pretrained(self.tmpdirname	)
				snake_case   :    Dict			       =		SpeechTaProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    int			       =		SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				snake_case   :    int			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''	)
				snake_case   :    str			       =		self.get_feature_extractor(do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				snake_case   :    Tuple			       =		SpeechTaProcessor.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.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    List[str]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Union[str, Any]			       =		floats_list((3, 1000)	)

				snake_case   :    List[str]			       =		feature_extractor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    Any			       =		processor(audio=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 	lowerCAmelCase							(	self							:       Dict	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_feature_extractor()
				snake_case   :    Optional[int]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		floats_list((3, 1000)	)

				snake_case   :    str			       =		feature_extractor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    List[Any]			       =		processor(audio_target=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 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    Union[str, Any]			       =		self.get_tokenizer()

				snake_case   :    List[str]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		'''This is a test string'''

				snake_case   :    Dict			       =		processor(text=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    str			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		'''This is a test string'''

				snake_case   :    Any			       =		processor(text_target=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    Optional[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[int]			       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				snake_case   :    List[str]			       =		processor.batch_decode(UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer.batch_decode(UpperCamelCase__	)

				self.assertListEqual(UpperCamelCase__   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    Tuple			       =		self.get_tokenizer()

				snake_case   :    List[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				self.assertListEqual(
				    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg='''`processor` and `feature_extractor` model input names do not match'''   ,     )


