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			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.inf

						def set_batch_size(_lowercase		) -> None:
												nonlocal batch_size
												if isinstance(_lowercase	,						_lowercase		):
																		SCREAMING_SNAKE_CASE							:					int           =							min(_lowercase	,						config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
												elif isinstance(_lowercase	,						_lowercase		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							min(_lowercase	,						config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
												elif isinstance(_lowercase	,						_lowercase		) and feature.dtype == "binary":
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							min(_lowercase	,						config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

						_visit(_lowercase	,						_lowercase		)

						return None if batch_size is np.inf else batch_size





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       NestedDataStructureLike[PathLike]					,	UpperCamelCase__			:       Optional[NamedSplit] = None					,	UpperCamelCase__			:       Optional[Features] = None					,	UpperCamelCase__			:       str = None					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional[int] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												super().__init__(
												    UpperCamelCase__					,	split=UpperCamelCase__					,	features=UpperCamelCase__					,	cache_dir=UpperCamelCase__					,	keep_in_memory=UpperCamelCase__					,	streaming=UpperCamelCase__					,	num_proc=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							path_or_paths if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else {self.split: path_or_paths}
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							_PACKAGED_DATASETS_MODULES['''parquet'''][1]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							Parquet(
												    cache_dir=UpperCamelCase__					,	data_files=UpperCamelCase__					,	features=UpperCamelCase__					,	hash=UpperCamelCase__					,	**UpperCamelCase__					,	)



						def  __A ( self			:       str		):





												'''simple docstring'''





												if self.streaming:
																		SCREAMING_SNAKE_CASE							:					int           =							self.builder.as_streaming_dataset(split=self.split		)
												# Build regular (map-style) dataset
												else:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							None
																		SCREAMING_SNAKE_CASE							:					Any           =							None
																		SCREAMING_SNAKE_CASE							:					str           =							None
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							None

																		self.builder.download_and_prepare(
																		    download_config=UpperCamelCase__					,	download_mode=UpperCamelCase__					,	verification_mode=UpperCamelCase__					,	base_path=UpperCamelCase__					,	num_proc=self.num_proc					,	)
																		SCREAMING_SNAKE_CASE							:					str           =							self.builder.as_dataset(
																		    split=self.split					,	verification_mode=UpperCamelCase__					,	in_memory=self.keep_in_memory		)
												return dataset






class       lowercase__		:


						def __init__( self			:       List[str]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       Union[PathLike, BinaryIO]					,	UpperCamelCase__			:       Optional[int] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Any           =							path_or_buf
												SCREAMING_SNAKE_CASE							:					Any           =							batch_size or get_writer_batch_size(dataset.features		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							parquet_writer_kwargs


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							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:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._write(file_obj=UpperCamelCase__					,	batch_size=UpperCamelCase__					,	**self.parquet_writer_kwargs		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							self._write(file_obj=self.path_or_buf					,	batch_size=UpperCamelCase__					,	**self.parquet_writer_kwargs		)
												return written



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       BinaryIO					,	UpperCamelCase__			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							0
												SCREAMING_SNAKE_CASE							:					str           =							parquet_writer_kwargs.pop('''path_or_buf'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dataset.features.arrow_schema

												SCREAMING_SNAKE_CASE							:					List[Any]           =							pq.ParquetWriter(UpperCamelCase__					,	schema=UpperCamelCase__					,	**UpperCamelCase__		)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[Any]    		=  ['MaskFormerFeatureExtractor']
					__UpperCamelCase	: List[Any]    		=  ['MaskFormerImageProcessor']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[int]    		=  [
					    'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MaskFormerForInstanceSegmentation',
					    'MaskFormerModel',
					    'MaskFormerPreTrainedModel',
					]
					__UpperCamelCase	: Union[str, Any]    		=  [
					    'MaskFormerSwinBackbone',
					    'MaskFormerSwinModel',
					    'MaskFormerSwinPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
					from .configuration_maskformer_swin import MaskFormerSwinConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_maskformer import MaskFormerFeatureExtractor
										from .image_processing_maskformer import MaskFormerImageProcessor
					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskformer import (
										    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskFormerForInstanceSegmentation,
										    MaskFormerModel,
										    MaskFormerPreTrainedModel,
										)
										from .modeling_maskformer_swin import (
										    MaskFormerSwinBackbone,
										    MaskFormerSwinModel,
										    MaskFormerSwinPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase	: List[str]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure)





