


'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCamelCase__    					=      datasets.utils.logging.get_logger(__name__)




@dataclass
class 			lowerCamelCase_   (	datasets.BuilderConfig      ):
      lowerCAmelCase__		     =     None
      lowerCAmelCase__		     =     "utf-8"
      lowerCAmelCase__		     =     None
      lowerCAmelCase__		     =     None
      lowerCAmelCase__		     =     True  # deprecated
      lowerCAmelCase__		     =     None  # deprecated
      lowerCAmelCase__		     =     1_0 << 2_0  # 10MB
      lowerCAmelCase__		     =     None




class 			lowerCamelCase_   (	datasets.ArrowBasedBuilder      ):
      lowerCAmelCase__		     =     JsonConfig






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          if self.config.block_size is not None:
              logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
              UpperCAmelCase__			:		List[str]													=				self.config.block_size
          if self.config.use_threads is not True:
              logger.warning(
                  '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
          if self.config.newlines_in_values is not None:
              raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
          return datasets.DatasetInfo(features=self.config.features )






      def lowercase_							(					self				:    str						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          if not self.config.data_files:
              raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
          UpperCAmelCase__			:		Dict													=				dl_manager.download_and_extract(self.config.data_files )
          if isinstance(UpperCamelCase__						,						(str, list, tuple) ):
              UpperCAmelCase__			:		Optional[int]													=				data_files
              if isinstance(UpperCamelCase__						,						UpperCamelCase__ ):
                  UpperCAmelCase__			:		Union[str, Any]													=				[files]
              UpperCAmelCase__			:		Any													=				[dl_manager.iter_files(UpperCamelCase__ ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,						gen_kwargs={'''files''': files} )]
          UpperCAmelCase__			:		Optional[Any]													=				[]
          for split_name, files in data_files.items():
              if isinstance(UpperCamelCase__						,						UpperCamelCase__ ):
                  UpperCAmelCase__			:		Optional[int]													=				[files]
              UpperCAmelCase__			:		int													=				[dl_manager.iter_files(UpperCamelCase__ ) for file in files]
              splits.append(datasets.SplitGenerator(name=UpperCamelCase__						,						gen_kwargs={'''files''': files} ) )
          return splits






      def lowercase_							(					self				:    Tuple						,						_A				:    pa.Table ):


          '''simple docstring'''


          if self.config.features is not None:
              # adding missing columns
              for column_name in set(self.config.features ) - set(pa_table.column_names ):
                  UpperCAmelCase__			:		str													=				self.config.features.arrow_schema.field(UpperCamelCase__ ).type
                  UpperCAmelCase__			:		Any													=				pa_table.append_column(UpperCamelCase__						,						pa.array([None] * len(UpperCamelCase__ )						,						type=UpperCamelCase__ ) )
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              UpperCAmelCase__			:		Dict													=				table_cast(UpperCamelCase__						,						self.config.features.arrow_schema )
          return pa_table






      def lowercase_							(					self				:    Any						,						_A				:    Tuple ):


          '''simple docstring'''


          for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                  with open(UpperCamelCase__						,						encoding=self.config.encoding						,						errors=self.config.encoding_errors ) as f:
                      UpperCAmelCase__			:		Optional[int]													=				json.load(UpperCamelCase__ )

                  # We keep only the field we are interested in
                  UpperCAmelCase__			:		str													=				dataset[self.config.field]

                  # We accept two format: a list of dicts or a dict of lists
                  if isinstance(UpperCamelCase__						,						(list, tuple) ):
                      UpperCAmelCase__			:		List[str]													=				set().union(*[row.keys() for row in dataset] )
                      UpperCAmelCase__			:		Optional[Any]													=				{col: [row.get(UpperCamelCase__ ) for row in dataset] for col in keys}
                  else:
                      UpperCAmelCase__			:		int													=				dataset
                  UpperCAmelCase__			:		Tuple													=				pa.Table.from_pydict(UpperCamelCase__ )
                  yield file_idx, self._cast_table(UpperCamelCase__ )

              # If the file has one json object per line
              else:
                  with open(UpperCamelCase__						,						'''rb''' ) as f:
                      UpperCAmelCase__			:		Union[str, Any]													=				0
                      # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                      # Set a default minimum value of 16kB if the chunk size is really small
                      UpperCAmelCase__			:		int													=				max(self.config.chunksize // 32						,						16 << 10 )
                      UpperCAmelCase__			:		List[Any]													=				(
                          self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                      )
                      while True:
                          UpperCAmelCase__			:		Union[str, Any]													=				f.read(self.config.chunksize )
                          if not batch:
                              break
                          # Finish current line
                          try:
                              batch += f.readline()
                          except (AttributeError, io.UnsupportedOperation):
                              batch += readline(UpperCamelCase__ )
                          # PyArrow only accepts utf-8 encoded bytes
                          if self.config.encoding != "utf-8":
                              UpperCAmelCase__			:		List[Any]													=				batch.decode(self.config.encoding						,						errors=UpperCamelCase__ ).encode('''utf-8''' )
                          try:
                              while True:
                                  try:
                                      UpperCAmelCase__			:		Optional[Any]													=				paj.read_json(
                                          io.BytesIO(UpperCamelCase__ )						,						read_options=paj.ReadOptions(block_size=UpperCamelCase__ ) )
                                      break
                                  except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                      if (
                                          isinstance(UpperCamelCase__						,						pa.ArrowInvalid )
                                          and "straddling" not in str(UpperCamelCase__ )
                                          or block_size > len(UpperCamelCase__ )
                                      ):
                                          raise
                                      else:
                                          # Increase the block size in case it was too small.
                                          # The block size will be reset for the next file.
                                          logger.debug(
                                              f"""Batch of {len(UpperCamelCase__ )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""" )
                                          block_size *= 2
                          except pa.ArrowInvalid as e:
                              try:
                                  with open(
                                      UpperCamelCase__						,						encoding=self.config.encoding						,						errors=self.config.encoding_errors ) as f:
                                      UpperCAmelCase__			:		Union[str, Any]													=				json.load(UpperCamelCase__ )
                              except json.JSONDecodeError:
                                  logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
                                  raise e
                              # If possible, parse the file as a list of json objects and exit the loop
                              if isinstance(UpperCamelCase__						,						UpperCamelCase__ ):  # list is the only sequence type supported in JSON
                                  try:
                                      UpperCAmelCase__			:		List[Any]													=				set().union(*[row.keys() for row in dataset] )
                                      UpperCAmelCase__			:		Optional[int]													=				{col: [row.get(UpperCamelCase__ ) for row in dataset] for col in keys}
                                      UpperCAmelCase__			:		Any													=				pa.Table.from_pydict(UpperCamelCase__ )
                                  except (pa.ArrowInvalid, AttributeError) as e:
                                      logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
                                      raise ValueError(f"""Not able to read records in the JSON file at {file}.""" ) from None
                                  yield file_idx, self._cast_table(UpperCamelCase__ )
                                  break
                              else:
                                  logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
                                  raise ValueError(
                                      f"""Not able to read records in the JSON file at {file}. """
                                      f"""You should probably indicate the field of the JSON file containing your records. """
                                      f"""This JSON file contain the following fields: {str(list(dataset.keys() ) )}. """
                                      f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """ ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                          yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__ )
                          batch_idx += 1



"""simple docstring"""


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						   =			{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  """beit"""

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				List[str]=8_1_9_2     ,  UpperCamelCase__							:				Dict=7_6_8     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2     ,  UpperCamelCase__							:				Dict=3_0_7_2     ,  UpperCamelCase__							:				Optional[int]="gelu"     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Union[str, Any]=0.02     ,  UpperCamelCase__							:				Optional[Any]=1e-12     ,  UpperCamelCase__							:				str=2_2_4     ,  UpperCamelCase__							:				str=1_6     ,  UpperCamelCase__							:				Union[str, Any]=3     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Dict=0.1     ,  UpperCamelCase__							:				List[Any]=0.1     ,  UpperCamelCase__							:				Any=True     ,  UpperCamelCase__							:				Optional[Any]=[3, 5, 7, 1_1]     ,  UpperCamelCase__							:				Optional[Any]=[1, 2, 3, 6]     ,  UpperCamelCase__							:				Optional[Any]=True     ,  UpperCamelCase__							:				Tuple=0.4     ,  UpperCamelCase__							:				Optional[Any]=2_5_6     ,  UpperCamelCase__							:				Optional[int]=1     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Optional[Any]=2_5_5     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   int:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:       str          =			vocab_size
						__lowerCAmelCase:       List[Any]          =			hidden_size
						__lowerCAmelCase:       str          =			num_hidden_layers
						__lowerCAmelCase:       Tuple          =			num_attention_heads
						__lowerCAmelCase:       Union[str, Any]          =			intermediate_size
						__lowerCAmelCase:       List[Any]          =			hidden_act
						__lowerCAmelCase:       Optional[Any]          =			hidden_dropout_prob
						__lowerCAmelCase:       List[Any]          =			attention_probs_dropout_prob
						__lowerCAmelCase:       str          =			initializer_range
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_eps

						__lowerCAmelCase:       Any          =			image_size
						__lowerCAmelCase:       Any          =			patch_size
						__lowerCAmelCase:       Union[str, Any]          =			num_channels
						__lowerCAmelCase:       Tuple          =			use_mask_token
						__lowerCAmelCase:       Optional[Any]          =			use_absolute_position_embeddings
						__lowerCAmelCase:       List[Any]          =			use_relative_position_bias
						__lowerCAmelCase:       Optional[Any]          =			use_shared_relative_position_bias
						__lowerCAmelCase:       List[str]          =			layer_scale_init_value
						__lowerCAmelCase:       str          =			drop_path_rate
						__lowerCAmelCase:       str          =			use_mean_pooling
						# decode head attributes (semantic segmentation)
						__lowerCAmelCase:       Optional[Any]          =			out_indices
						__lowerCAmelCase:       Union[str, Any]          =			pool_scales
						# auxiliary head attributes (semantic segmentation)
						__lowerCAmelCase:       List[str]          =			use_auxiliary_head
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss_weight
						__lowerCAmelCase:       Optional[int]          =			auxiliary_channels
						__lowerCAmelCase:       Dict          =			auxiliary_num_convs
						__lowerCAmelCase:       List[Any]          =			auxiliary_concat_input
						__lowerCAmelCase:       str          =			semantic_loss_ignore_index





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  version.parse("""1.11"""  )

				@property
				def 							lowercase_     (	self							:				str)->   Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


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




				@property
				def 							lowercase_     (	self							:				Any)->   float:



						'''simple docstring'''


						return 1e-4


