



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def  UpperCamelCase						(       _A			:				Tuple    )->       List[str]:





						"""simple docstring"""





						return EnvironmentCommand()
def  UpperCamelCase						(       _A			:				Any    )->       Any:





						"""simple docstring"""





						return EnvironmentCommand(args.accelerate_config_file    )






class      UpperCamelCase    (						_UpperCAmelCase     ):



	@staticmethod
	def 			__A							(       UpperCAmelCase__		):
							A__							=       parser.add_parser("env"		)
							download_parser.set_defaults(func=UpperCAmelCase__		)
							download_parser.add_argument(
							    "--accelerate-config_file"			,	default=UpperCAmelCase__			,	help="The accelerate config file to use for the default values in the launching script."			,	)
							download_parser.set_defaults(func=UpperCAmelCase__		)



	def __init__(       self			,	UpperCAmelCase__			,	*UpperCAmelCase__		):
							A__							=       accelerate_config_file



	def 			__A							(       self		):
							A__							=       "not installed"
							if is_safetensors_available():
													import safetensors

													A__							=       safetensors.__version__
							elif importlib.util.find_spec("safetensors"		) is not None:
													import safetensors

													A__							=       F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

							A__							=       "not installed"
							A__							=       A__							=       "not found"
							if is_accelerate_available():
													import accelerate
													from accelerate.commands.config import default_config_file, load_config_from_file

													A__							=       accelerate.__version__
													# Get the default from the config file.
													if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase__		):
																			A__							=       load_config_from_file(self._accelerate_config_file		).to_dict()

													A__							=       (
													    "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]		)
													    if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		)
													    else F"""\t{accelerate_config}"""
													)

							A__							=       "not installed"
							A__							=       "NA"
							if is_torch_available():
													import torch

													A__							=       torch.__version__
													A__							=       torch.cuda.is_available()

							A__							=       "not installed"
							A__							=       "NA"
							if is_tf_available():
													import tensorflow as tf

													A__							=       tf.__version__
													try:
																			# deprecated in v2.1
																			A__							=       tf.test.is_gpu_available()
													except AttributeError:
																			# returns list of devices, convert to bool
																			A__							=       bool(tf.config.list_physical_devices("GPU"		)		)

							A__							=       "not installed"
							A__							=       "not installed"
							A__							=       "not installed"
							A__							=       "NA"
							if is_flax_available():
													import flax
													import jax
													import jaxlib

													A__							=       flax.__version__
													A__							=       jax.__version__
													A__							=       jaxlib.__version__
													A__							=       jax.lib.xla_bridge.get_backend().platform

							A__							=       {
							    "`transformers` version": version,
							    "Platform": platform.platform(),
							    "Python version": platform.python_version(),
							    "Huggingface_hub version": huggingface_hub.__version__,
							    "Safetensors version": F"""{safetensors_version}""",
							    "Accelerate version": F"""{accelerate_version}""",
							    "Accelerate config": F"""{accelerate_config_str}""",
							    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
							    "Tensorflow version (GPU?)": F"""{tf_version} ({tf_cuda_available})""",
							    "Flax version (CPU?/GPU?/TPU?)": F"""{flax_version} ({jax_backend})""",
							    "Jax version": F"""{jax_version}""",
							    "JaxLib version": F"""{jaxlib_version}""",
							    "Using GPU in script?": "<fill in>",
							    "Using distributed or parallel set-up in script?": "<fill in>",
							}

							print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"		)
							print(self.format_dict(UpperCAmelCase__		)		)

							return info



	@staticmethod
	def 			__A							(       UpperCAmelCase__		):
							return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]		) + "\n"










import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							super().__init__(
							    UpperCAmelCase__			,	split=UpperCAmelCase__			,	features=UpperCAmelCase__			,	cache_dir=UpperCAmelCase__			,	keep_in_memory=UpperCAmelCase__			,	streaming=UpperCAmelCase__			,	num_proc=UpperCAmelCase__			,	**UpperCAmelCase__			,	)
							A__							=       field
							A__							=       path_or_paths if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else {self.split: path_or_paths}
							A__							=       Json(
							    cache_dir=UpperCAmelCase__			,	data_files=UpperCAmelCase__			,	features=UpperCAmelCase__			,	field=UpperCAmelCase__			,	**UpperCAmelCase__			,	)



	def 			__A							(       self		):
							# Build iterable dataset
							if self.streaming:
													A__							=       self.builder.as_streaming_dataset(split=self.split		)
							# Build regular (map-style) dataset
							else:
													A__							=       None
													A__							=       None
													A__							=       None
													A__							=       None

													self.builder.download_and_prepare(
													    download_config=UpperCAmelCase__			,	download_mode=UpperCAmelCase__			,	verification_mode=UpperCAmelCase__			,	base_path=UpperCAmelCase__			,	num_proc=self.num_proc			,	)
													A__							=       self.builder.as_dataset(
													    split=self.split			,	verification_mode=UpperCAmelCase__			,	in_memory=self.keep_in_memory		)
							return dataset






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							if num_proc is not None and num_proc <= 0:
													raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

							A__							=       dataset
							A__							=       path_or_buf
							A__							=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							A__							=       num_proc
							A__							=       "utf-8"
							A__							=       to_json_kwargs



	def 			__A							(       self		):
							A__							=       self.to_json_kwargs.pop("path_or_buf"			,	UpperCAmelCase__		)
							A__							=       self.to_json_kwargs.pop("orient"			,	"records"		)
							A__							=       self.to_json_kwargs.pop("lines"			,	True if orient == "records" else False		)
							A__							=       self.to_json_kwargs.pop("index"			,	False if orient in ["split", "table"] else True		)
							A__							=       self.to_json_kwargs.pop("compression"			,	UpperCAmelCase__		)

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
													raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""		)

							if isinstance(self.path_or_buf			,	(str, bytes, os.PathLike)		):
													with fsspec.open(self.path_or_buf			,	"wb"			,	compression=UpperCAmelCase__		) as buffer:
																			A__							=       self._write(file_obj=UpperCAmelCase__			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							else:
													if compression:
																			raise NotImplementedError(
																			    F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
																			    " was passed. Please provide a local path instead."		)
													A__							=       self._write(
													    file_obj=self.path_or_buf			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							return written



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__						,       A__						,       A__						,       A__						,       A__							=       args

							A__							=       query_table(
							    table=self.dataset.data			,	key=slice(UpperCAmelCase__			,	offset + self.batch_size		)			,	indices=self.dataset._indices			,	)
							A__							=       batch.to_pandas().to_json(
							    path_or_buf=UpperCAmelCase__			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**UpperCAmelCase__		)
							if not json_str.endswith("\n"		):
													json_str += "\n"
							return json_str.encode(self.encoding		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__			,	):
							A__							=       0

							if self.num_proc is None or self.num_proc == 1:
													for offset in logging.tqdm(
													    range(0			,	len(self.dataset		)			,	self.batch_size		)			,	unit="ba"			,	disable=not logging.is_progress_bar_enabled()			,	desc="Creating json from Arrow format"			,	):
																			A__							=       self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
																			written += file_obj.write(UpperCAmelCase__		)
							else:
													A__						,       A__							=       len(self.dataset		), self.batch_size
													with multiprocessing.Pool(self.num_proc		) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,	UpperCAmelCase__			,	UpperCAmelCase__		)]			,	)			,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,	unit="ba"			,	disable=not logging.is_progress_bar_enabled()			,	desc="Creating json from Arrow format"			,	):
																									written += file_obj.write(UpperCAmelCase__		)

							return written





