





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCAmelCase	:     Optional[int]		       = logging.get_logger(__name__)

_UpperCAmelCase	:     str		       = {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}







class 				lowercase       ( lowercase__    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    '''conditional_detr'''
				__SCREAMING_SNAKE_CASE      :				Tuple     =    ['''past_key_values''']
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}




				def __init__(      self   ,     snake_case=True   ,     snake_case=None   ,     snake_case=3   ,     snake_case=300   ,     snake_case=6   ,     snake_case=2048   ,     snake_case=8   ,     snake_case=6   ,     snake_case=2048   ,     snake_case=8   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=True   ,     snake_case="relu"   ,     snake_case=256   ,     snake_case=0.1   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=0.02   ,     snake_case=1.0   ,     snake_case=False   ,     snake_case="sine"   ,     snake_case="resnet50"   ,     snake_case=True   ,     snake_case=False   ,     snake_case=2   ,     snake_case=5   ,     snake_case=2   ,     snake_case=1   ,     snake_case=1   ,     snake_case=2   ,     snake_case=5   ,     snake_case=2   ,     snake_case=0.25   ,     **snake_case   ,     ):
											if backbone_config is not None and use_timm_backbone:
																		raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )

											if not use_timm_backbone:
																		if backbone_config is None:
																									logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
																									snake_case_           =					CONFIG_MAPPING["resnet"](out_features=['stage4'] )
																		elif isinstance(snake_case   ,     snake_case ):
																									snake_case_           =					backbone_config.get('model_type' )
																									snake_case_           =					CONFIG_MAPPING[backbone_model_type]
																									snake_case_           =					config_class.from_dict(snake_case )

											snake_case_           =					use_timm_backbone
											snake_case_           =					backbone_config
											snake_case_           =					num_channels
											snake_case_           =					num_queries
											snake_case_           =					d_model
											snake_case_           =					encoder_ffn_dim
											snake_case_           =					encoder_layers
											snake_case_           =					encoder_attention_heads
											snake_case_           =					decoder_ffn_dim
											snake_case_           =					decoder_layers
											snake_case_           =					decoder_attention_heads
											snake_case_           =					dropout
											snake_case_           =					attention_dropout
											snake_case_           =					activation_dropout
											snake_case_           =					activation_function
											snake_case_           =					init_std
											snake_case_           =					init_xavier_std
											snake_case_           =					encoder_layerdrop
											snake_case_           =					decoder_layerdrop
											snake_case_           =					encoder_layers
											snake_case_           =					auxiliary_loss
											snake_case_           =					position_embedding_type
											snake_case_           =					backbone
											snake_case_           =					use_pretrained_backbone
											snake_case_           =					dilation
											# Hungarian matcher
											snake_case_           =					class_cost
											snake_case_           =					bbox_cost
											snake_case_           =					giou_cost
											# Loss coefficients
											snake_case_           =					mask_loss_coefficient
											snake_case_           =					dice_loss_coefficient
											snake_case_           =					cls_loss_coefficient
											snake_case_           =					bbox_loss_coefficient
											snake_case_           =					giou_loss_coefficient
											snake_case_           =					focal_alpha
											super().__init__(is_encoder_decoder=snake_case   ,     **snake_case )




				@property
				def 					a							(      self ):
											return self.encoder_attention_heads




				@property
				def 					a							(      self ):
											return self.d_model




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											if self.backbone_config is not None:
																		snake_case_           =					self.backbone_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output







class 				lowercase       ( lowercase__    ):
				__SCREAMING_SNAKE_CASE      :				int     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											        ('pixel_mask', {0: 'batch'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-5




				@property
				def 					a							(      self ):
											return 12







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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.inf

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

							_visit(UpperCamelCase__     ,							UpperCamelCase__   )

							return None if batch_size is np.inf else batch_size







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = None   ,     **snake_case   ,     ):
											super().__init__(
											    snake_case   ,     split=snake_case   ,     features=snake_case   ,     cache_dir=snake_case   ,     keep_in_memory=snake_case   ,     streaming=snake_case   ,     num_proc=snake_case   ,     **snake_case   ,     )
											snake_case_           =					path_or_paths if isinstance(snake_case   ,     snake_case ) else {self.split: path_or_paths}
											snake_case_           =					_PACKAGED_DATASETS_MODULES['parquet'][1]
											snake_case_           =					Parquet(
											    cache_dir=snake_case   ,     data_files=snake_case   ,     features=snake_case   ,     hash=snake_case   ,     **snake_case   ,     )




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

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







class 				lowercase       :




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					dataset
											snake_case_           =					path_or_buf
											snake_case_           =					batch_size or get_writer_batch_size(dataset.features )
											snake_case_           =					parquet_writer_kwargs




				def 					a							(      self ):
											snake_case_           =					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:
																									snake_case_           =					self._write(file_obj=snake_case   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											else:
																		snake_case_           =					self._write(file_obj=self.path_or_buf   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											return written




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     **snake_case ):
											snake_case_           =					0
											snake_case_           =					parquet_writer_kwargs.pop('path_or_buf'   ,     snake_case )
											snake_case_           =					self.dataset.features.arrow_schema

											snake_case_           =					pq.ParquetWriter(snake_case   ,     schema=snake_case   ,     **snake_case )

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




