


"""simple docstring"""







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		):







    """simple docstring"""
    a 		=np.inf

    def set_batch_size(lowercase		) -> None:
        nonlocal batch_size
        if isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		) and feature.dtype == "binary":
            a 		=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       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




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





 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     # 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=__A ,    download_mode=__A ,    verification_mode=__A ,    base_path=__A ,    num_proc=self.num_proc ,    )
         a 		=self.builder.as_dataset(
             split=self.split ,    verification_mode=__A ,    in_memory=self.keep_in_memory						)
     return dataset






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     Optional[int]:
     a 		=dataset
     a 		=path_or_buf
     a 		=batch_size or get_writer_batch_size(dataset.features						)
     a 		=parquet_writer_kwargs


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		=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:
             a 		=self._write(file_obj=__A ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     else:
         a 		=self._write(file_obj=self.path_or_buf ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     return written





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    **__A						)					->     int:
     a 		=0
     a 		=parquet_writer_kwargs.pop('''path_or_buf''' ,    __A						)
     a 		=self.dataset.features.arrow_schema

     a 		=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''' ,    ):
         a 		=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






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


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""yolos"""





			def __init__(    self       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=[5_1_2, 8_6_4]       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=1_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.1       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(**__lowerCAmelCase							)

				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		patch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		qkv_bias
				lowerCamelCase__								=		num_detection_tokens
				lowerCamelCase__								=		use_mid_position_embeddings
				lowerCamelCase__								=		auxiliary_loss
				# Hungarian matcher
				lowerCamelCase__								=		class_cost
				lowerCamelCase__								=		bbox_cost
				lowerCamelCase__								=		giou_cost
				# Loss coefficients
				lowerCamelCase__								=		bbox_loss_coefficient
				lowerCamelCase__								=		giou_loss_coefficient
				lowerCamelCase__								=		eos_coefficient






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						version.parse("""1.11""" )





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1E-4





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1_2



