






"""simple docstring"""
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A_     :					str							=     get_logger(__name__)

# Datasets
A_     :					List[Any]							=     "https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A_     :					Dict							=     "https://cdn-datasets.huggingface.co/datasets/datasets"
A_     :					Optional[int]							=     "https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A_     :					Optional[int]							=     "https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A_     :					Union[str, Any]							=     "https://cdn-datasets.huggingface.co/datasets/metric"
A_     :					int							=     "https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A_     :					List[Any]							=     os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A_     :					Tuple							=     HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A_     :					Optional[int]							=     "main"

A_     :					Union[str, Any]							=     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
A_     :					Dict							=     {"1", "ON", "YES", "TRUE"}
A_     :					int							=     ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A_     :					Union[str, Any]							=     version.parse(importlib_metadata.version("dill"))
A_     :					int							=     version.parse(importlib_metadata.version("pandas"))
A_     :					Optional[int]							=     version.parse(importlib_metadata.version("pyarrow"))

A_     :					List[str]							=     os.environ.get("USE_TF", "AUTO").upper()
A_     :					Any							=     os.environ.get("USE_TORCH", "AUTO").upper()
A_     :					Union[str, Any]							=     os.environ.get("USE_JAX", "AUTO").upper()

A_     :					Dict							=     "N/A"
A_     :					Union[str, Any]							=     False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
      A_     :					Any							=     importlib.util.find_spec("torch") is not None
      if TORCH_AVAILABLE:
            try:
                  A_     :					Tuple							=     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")

A_     :					List[Any]							=     "N/A"
A_     :					str							=     False

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


A_     :					List[str]							=     "N/A"
A_     :					Optional[Any]							=     False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      A_     :					Dict							=     importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
      if JAX_AVAILABLE:
            try:
                  A_     :					Optional[int]							=     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")


A_     :					Union[str, Any]							=     os.environ.get("USE_BEAM", "AUTO").upper()
A_     :					Union[str, Any]							=     "N/A"
A_     :					List[str]							=     False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            A_     :					List[Any]							=     version.parse(importlib_metadata.version("apache_beam"))
            A_     :					Tuple							=     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
A_     :					int							=     importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A_     :					Tuple							=     importlib.util.find_spec("PIL") is not None
A_     :					int							=     importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A_     :					Dict							=     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
A_     :					Optional[Any]							=     importlib.util.find_spec("rarfile") is not None
A_     :					List[str]							=     importlib.util.find_spec("zstandard") is not None
A_     :					Union[str, Any]							=     importlib.util.find_spec("lz4") is not None
A_     :					Optional[Any]							=     importlib.util.find_spec("py7zr") is not None


# Cache location
A_     :					str							=     "~/.cache"
A_     :					Optional[int]							=     os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A_     :					Union[str, Any]							=     os.path.join(XDG_CACHE_HOME, "huggingface")
A_     :					List[str]							=     os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A_     :					Tuple							=     os.path.join(HF_CACHE_HOME, "datasets")
A_     :					Union[str, Any]							=     Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A_     :					Optional[Any]							=     os.path.join(HF_CACHE_HOME, "metrics")
A_     :					Any							=     Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A_     :					Tuple							=     os.path.join(HF_CACHE_HOME, "modules")
A_     :					Optional[Any]							=     Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A_     :					Dict							=     "downloads"
A_     :					str							=     os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A_     :					int							=     Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A_     :					Dict							=     "extracted"
A_     :					Any							=     os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A_     :					Optional[Any]							=     Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A_     :					Union[str, Any]							=     (
    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)
A_     :					Optional[Any]							=     10_00

# Size of the preloaded record batch in `Dataset.__iter__`
A_     :					Union[str, Any]							=     10

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

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A_     :					List[str]							=     "500MB"

# Parquet configuration
A_     :					Optional[int]							=     1_00
A_     :					Dict							=     1_00
A_     :					Union[str, Any]							=     1_00

# Offline mode
A_     :					Optional[int]							=     os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A_     :					str							=     0  # Disabled
A_     :					Optional[int]							=     float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A_     :					Any							=     "dataset.arrow"
A_     :					Union[str, Any]							=     "indices.arrow"
A_     :					Tuple							=     "state.json"
A_     :					Optional[int]							=     "dataset_info.json"
A_     :					List[str]							=     "dataset_infos.json"
A_     :					Union[str, Any]							=     "LICENSE"
A_     :					Optional[int]							=     "metric_info.json"
A_     :					Union[str, Any]							=     "dataset_dict.json"

A_     :					Dict							=     "datasets_modules"

A_     :					List[str]							=     2_55

# Streaming
A_     :					Optional[int]							=     20
A_     :					Tuple							=     5

# Datasets without script
A_     :					List[Any]							=     2_00
A_     :					List[str]							=     10
A_     :					Union[str, Any]							=     2_00

# Progress bars
A_     :					Optional[Any]							=     0.05  # 20 progress updates per sec









"""simple docstring"""
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
      import transformer_engine.pytorch as te






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase=True     ,  _lowerCamelCase=True     ,  _lowerCamelCase=True							):
 if not is_fpa_available():
  raise ImportError('Using `convert_model` requires transformer_engine to be installed.'							)
 for name, module in model.named_children():
  if isinstance(_lowerCamelCase     ,  nn.Linear							) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape							):
    return
   lowerCamelCase__	:     List[Any]								=		module.bias is not None
   lowerCamelCase__	:     Tuple								=		te.Linear(
       module.in_features     ,  module.out_features     ,  bias=_lowerCamelCase     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Optional[Any]								=		module.weight.data.clone()
   if has_bias:
    lowerCamelCase__	:     List[str]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  nn.LayerNorm							) and to_transformer_engine and _convert_ln:
   lowerCamelCase__	:     Tuple								=		te.LayerNorm(module.normalized_shape[0]     ,  eps=module.eps     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Tuple								=		module.weight.data.clone()
   lowerCamelCase__	:     Optional[Any]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  te.Linear							) and not to_transformer_engine and _convert_linear:
   lowerCamelCase__	:     Optional[Any]								=		module.bias is not None
   lowerCamelCase__	:     Union[str, Any]								=		nn.Linear(
       module.in_features     ,  module.out_features     ,  bias=_lowerCamelCase     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     str								=		module.weight.data.clone()
   if has_bias:
    lowerCamelCase__	:     Optional[int]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  te.LayerNorm							) and not to_transformer_engine and _convert_ln:
   lowerCamelCase__	:     str								=		nn.LayerNorm(module.normalized_shape[0]     ,  eps=module.eps     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Dict								=		module.weight.data.clone()
   lowerCamelCase__	:     Dict								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  else:
   convert_model(
       _lowerCamelCase     ,  to_transformer_engine=_lowerCamelCase     ,  _convert_linear=_convert_linear     ,  _convert_ln=_convert_ln     ,  )






def 			lowerCamelCase_    (		_lowerCamelCase							):
 if not is_fpa_available():
  raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'							)
 for m in model.modules():
  if isinstance(_lowerCamelCase     ,  (te.LayerNorm, te.Linear)							):
   return True
 return False

