import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
						from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm






def        UpperCamelCase_							(			lowerCAmelCase__  ):



					"""simple docstring"""

					if is_torch_version("<"						,	"2.0.0"  ) or not hasattr(lowerCAmelCase__						,	"_dynamo"  ):
										return False
					return isinstance(lowerCAmelCase__						,	torch._dynamo.eval_frame.OptimizedModule  )






def        UpperCamelCase_							(			lowerCAmelCase__						,	lowerCAmelCase__ = True  ):



					"""simple docstring"""

					_lowerCAmelCase			:		Union[str, Any]          = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

					_lowerCAmelCase			:		List[str]          = is_compiled_module(lowerCAmelCase__  )
					if is_compiled:
										_lowerCAmelCase			:		Optional[int]          = model
										_lowerCAmelCase			:		Optional[int]          = model._orig_mod

					if is_deepspeed_available():
										options += (DeepSpeedEngine,)

					while isinstance(lowerCAmelCase__						,	lowerCAmelCase__  ):
										_lowerCAmelCase			:		List[str]          = model.module

					if not keep_fpaa_wrapper:
										_lowerCAmelCase			:		Any          = getattr(lowerCAmelCase__						,	"forward"  )
										_lowerCAmelCase			:		List[Any]          = model.__dict__.pop("_original_forward"						,	lowerCAmelCase__  )
										if original_forward is not None:
															while hasattr(lowerCAmelCase__						,	"__wrapped__"  ):
																				_lowerCAmelCase			:		Union[str, Any]          = forward.__wrapped__
																				if forward == original_forward:
																									break
															_lowerCAmelCase			:		Tuple          = forward
										if getattr(lowerCAmelCase__						,	"_converted_to_transformer_engine"						,	lowerCAmelCase__  ):
															convert_model(lowerCAmelCase__						,	to_transformer_engine=lowerCAmelCase__  )

					if is_compiled:
										_lowerCAmelCase			:		Optional[Any]          = model
										_lowerCAmelCase			:		Optional[int]          = compiled_model

					return model






def        UpperCamelCase_							(			):



					"""simple docstring"""

					PartialState().wait_for_everyone()






def        UpperCamelCase_							(			lowerCAmelCase__						,	lowerCAmelCase__  ):



					"""simple docstring"""

					if PartialState().distributed_type == DistributedType.TPU:
										xm.save(lowerCAmelCase__						,	lowerCAmelCase__  )
					elif PartialState().local_process_index == 0:
										torch.save(lowerCAmelCase__						,	lowerCAmelCase__  )






@contextmanager
def        UpperCamelCase_							(			**lowerCAmelCase__  ):



					"""simple docstring"""

					for key, value in kwargs.items():
										_lowerCAmelCase			:		Tuple          = str(lowerCAmelCase__  )

					yield

					for key in kwargs:
										if key.upper() in os.environ:
															del os.environ[key.upper()]






def        UpperCamelCase_							(			lowerCAmelCase__  ):



					"""simple docstring"""

					if not hasattr(lowerCAmelCase__						,	"__qualname__"  ) and not hasattr(lowerCAmelCase__						,	"__name__"  ):
										_lowerCAmelCase			:		Dict          = getattr(lowerCAmelCase__						,	"__class__"						,	lowerCAmelCase__  )
					if hasattr(lowerCAmelCase__						,	"__qualname__"  ):
										return obj.__qualname__
					if hasattr(lowerCAmelCase__						,	"__name__"  ):
										return obj.__name__
					return str(lowerCAmelCase__  )






def        UpperCamelCase_							(			lowerCAmelCase__						,	lowerCAmelCase__  ):



					"""simple docstring"""

					for key, value in source.items():
										if isinstance(lowerCAmelCase__						,	lowerCAmelCase__  ):
															_lowerCAmelCase			:		Union[str, Any]          = destination.setdefault(lowerCAmelCase__						,	{}  )
															merge_dicts(lowerCAmelCase__						,	lowerCAmelCase__  )
										else:
															_lowerCAmelCase			:		Dict          = value

					return destination






def        UpperCamelCase_							(			lowerCAmelCase__ = None  ):



					"""simple docstring"""

					if port is None:
										_lowerCAmelCase			:		Optional[int]          = 2_95_00
					with socket.socket(socket.AF_INET						,	socket.SOCK_STREAM  ) as s:
										return s.connect_ex(("localhost", port)  ) == 0








import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def 				__A(lowerCAmelCase			)		->					Optional[int]:

		"""simple docstring"""






		_UpperCamelCase         =						np.inf

		def set_batch_size(lowerCAmelCase			) -> None:
				nonlocal batch_size
				if isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			) and feature.dtype == "binary":
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS			)

		_visit(lowerCAmelCase    ,	lowerCAmelCase			)

		return None if batch_size is np.inf else batch_size







class 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = False							,				a = False							,				a = None							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

								super().__init__(
								    a							,				split=a							,				features=a							,				cache_dir=a							,				keep_in_memory=a							,				streaming=a							,				num_proc=a							,				**a							,				)
								_UpperCamelCase         =						path_or_paths if isinstance(a							,				a					) else {self.split: path_or_paths}
								_UpperCamelCase         =						_PACKAGED_DATASETS_MODULES["""parquet"""][1]
								_UpperCamelCase         =						Parquet(
								    cache_dir=a							,				data_files=a							,				features=a							,				hash=a							,				**a							,				)







						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

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

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







class 							lowerCAmelCase__   :

						def __init__(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						dataset
								_UpperCamelCase         =						path_or_buf
								_UpperCamelCase         =						batch_size or get_writer_batch_size(dataset.features					)
								_UpperCamelCase         =						parquet_writer_kwargs

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

								if isinstance(self.path_or_buf							,				(str, bytes, os.PathLike)					):
										with open(self.path_or_buf							,				"""wb+"""					) as buffer:
												_UpperCamelCase         =						self._write(file_obj=a							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								else:
										_UpperCamelCase         =						self._write(file_obj=self.path_or_buf							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								return written







						def 			A_					(      self							,				a							,				a							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						parquet_writer_kwargs.pop("""path_or_buf"""							,				a					)
								_UpperCamelCase         =						self.dataset.features.arrow_schema

								_UpperCamelCase         =						pq.ParquetWriter(a							,				schema=a							,				**a					)

								for offset in logging.tqdm(
								    range(0							,				len(self.dataset					)							,				a					)							,				unit="""ba"""							,				disable=not logging.is_progress_bar_enabled()							,				desc="""Creating parquet from Arrow format"""							,				):
										_UpperCamelCase         =						query_table(
										    table=self.dataset._data							,				key=slice(a							,				offset + batch_size					)							,				indices=self.dataset._indices if self.dataset._indices is not None else None							,				)
										writer.write_table(a					)
										written += batch.nbytes
								writer.close()
								return written



