




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__       				=	1_000

# 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__       				=	0.0_5  # 20 progress updates per sec





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__A       ={
    "configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
    "processing_speech_to_text_2": ["Speech2Text2Processor"],
    "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =[
				    "SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2Text2ForCausalLM",
				    "Speech2Text2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
				from .processing_speech_to_text_a import SpeechaTextaProcessor
				from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text_a import (
								    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextaForCausalLM,
								    SpeechaTextaPreTrainedModel,
								)

else:
				import sys

				__A       =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





