
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       ,							)					->      Union[str, Any]:


								'''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				)					->      List[Any]:


								'''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       ,							)					->      Any:


								'''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





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


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

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	huggingface_hub.__version__

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

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

								_UpperCamelCase						    =	"""not installed"""
								if is_transformers_available():
										import transformers

										_UpperCamelCase						    =	transformers.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_accelerate_available():
										import accelerate

										_UpperCamelCase						    =	accelerate.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_xformers_available():
										import xformers

										_UpperCamelCase						    =	xformers.__version__

								_UpperCamelCase						    =	{
								    """`diffusers` version""": version,
								    """Platform""": platform.platform(),
								    """Python version""": platform.python_version(),
								    """PyTorch version (GPU?)""": F'{pt_version} ({pt_cuda_available})',
								    """Huggingface_hub version""": hub_version,
								    """Transformers version""": transformers_version,
								    """Accelerate version""": accelerate_version,
								    """xFormers version""": xformers_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(a				)				)

								return info







						@staticmethod
						def  A_				(			a				)					->      Dict:


								'''simple docstring'''

								return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]				) + "\n"



