



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
 from ...processing_utils import ProcessorMixin
 from ...utils import TensorType


__A       =						logging.get_logger(__name__)

__A       =						{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}






class 		lowercase						(			snake_case__):






 """simple docstring"""


 a__				: str		       =							"""layoutlmv3"""




 def __init__(     self     :						Tuple						,  __UpperCAmelCase     :						int=50_265						,  __UpperCAmelCase     :						str=768						,  __UpperCAmelCase     :						Dict=12						,  __UpperCAmelCase     :						Tuple=12						,  __UpperCAmelCase     :						Union[str, Any]=3_072						,  __UpperCAmelCase     :						Any="gelu"						,  __UpperCAmelCase     :						List[str]=0.1						,  __UpperCAmelCase     :						Tuple=0.1						,  __UpperCAmelCase     :						Any=512						,  __UpperCAmelCase     :						Optional[Any]=2						,  __UpperCAmelCase     :						str=0.02						,  __UpperCAmelCase     :						Optional[int]=1E-5						,  __UpperCAmelCase     :						str=1						,  __UpperCAmelCase     :						List[Any]=0						,  __UpperCAmelCase     :						int=2						,  __UpperCAmelCase     :						Union[str, Any]=1_024						,  __UpperCAmelCase     :						Union[str, Any]=128						,  __UpperCAmelCase     :						Optional[Any]=128						,  __UpperCAmelCase     :						int=True						,  __UpperCAmelCase     :						Union[str, Any]=32						,  __UpperCAmelCase     :						Any=128						,  __UpperCAmelCase     :						int=64						,  __UpperCAmelCase     :						Union[str, Any]=256						,  __UpperCAmelCase     :						Dict=True						,  __UpperCAmelCase     :						List[Any]=True						,  __UpperCAmelCase     :						List[Any]=True						,  __UpperCAmelCase     :						Any=224						,  __UpperCAmelCase     :						List[str]=3						,  __UpperCAmelCase     :						str=16						,  __UpperCAmelCase     :						Dict=None						,  **__UpperCAmelCase     :						Optional[Any]						,  )      ->			Any:
       super().__init__(
           vocab_size=_A						,  hidden_size=_A						,  num_hidden_layers=_A						,  num_attention_heads=_A						,  intermediate_size=_A						,  hidden_act=_A						,  hidden_dropout_prob=_A						,  attention_probs_dropout_prob=_A						,  max_position_embeddings=_A						,  type_vocab_size=_A						,  initializer_range=_A						,  layer_norm_eps=_A						,  pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A						,  )
       UpperCAmelCase_=       max_ad_position_embeddings
       UpperCAmelCase_=       coordinate_size
       UpperCAmelCase_=       shape_size
       UpperCAmelCase_=       has_relative_attention_bias
       UpperCAmelCase_=       rel_pos_bins
       UpperCAmelCase_=       max_rel_pos
       UpperCAmelCase_=       has_spatial_attention_bias
       UpperCAmelCase_=       rel_ad_pos_bins
       UpperCAmelCase_=       max_rel_ad_pos
       UpperCAmelCase_=       text_embed
       UpperCAmelCase_=       visual_embed
       UpperCAmelCase_=       input_size
       UpperCAmelCase_=       num_channels
       UpperCAmelCase_=       patch_size
       UpperCAmelCase_=       classifier_dropout






class 		lowercase						(			snake_case__):






 """simple docstring"""


 a__				: Tuple		       =							version.parse("1.12")




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			str:



       if self.task in ["question-answering", "sequence-classification"]:
             return OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                     ("""bbox""", {0: """batch""", 1: """sequence"""}),
                     ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ]					)
       else:
             return OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                     ("""bbox""", {0: """batch""", 1: """sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                     ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
                 ]					)




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[int]:
       return 1E-5




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Dict:
       return 12




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Any = -1						,  __UpperCAmelCase     :						Dict = -1						,  __UpperCAmelCase     :						Optional[int] = False						,  __UpperCAmelCase     :						Any = None						,  __UpperCAmelCase     :						Dict = 3						,  __UpperCAmelCase     :						Optional[Any] = 40						,  __UpperCAmelCase     :						List[Any] = 40						,  )      ->			str:
       setattr(processor.image_processor						,  """apply_ocr"""						,  _A					)

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase_=       compute_effective_axis_dimension(
           _A						,  fixed_dimension=OnnxConfig.default_fixed_batch						,  num_token_to_add=0					)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase_=       processor.tokenizer.num_special_tokens_to_add(_A					)
       UpperCAmelCase_=       compute_effective_axis_dimension(
           _A						,  fixed_dimension=OnnxConfig.default_fixed_sequence						,  num_token_to_add=_A					)
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_=       [[""" """.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCAmelCase_=       [[[48, 84, 73, 128]]] * batch_size

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
       UpperCAmelCase_=       self._generate_dummy_images(_A						,  _A						,  _A						,  _A					)

       UpperCAmelCase_=       dict(
           processor(
               _A						,  text=_A						,  boxes=_A						,  return_tensors=_A						,  )					)

       return inputs









import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

__A       =						['''names''', '''prefix''']
__A       =						['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__A       =						['''encoding_errors''', '''on_bad_lines''']
__A       =						['''date_format''']






@dataclass
class 		lowercase						(			datasets.BuilderConfig):






	"""simple docstring"""


	a__				: str		       =							","
	a__				: Optional[str]		       =							None
	a__				: Optional[Union[int, List[int], str]]		       =							"infer"
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[List[str]]		       =							None
	a__				: Optional[Union[int, str, List[int], List[str]]]		       =							None
	a__				: Optional[Union[List[int], List[str]]]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: Optional[Literal["c", "python", "pyarrow"]]		       =							None
	a__				: Dict[Union[int, str], Callable[[Any], Any]]		       =							None
	a__				: Optional[list]		       =							None
	a__				: Optional[list]		       =							None
	a__				: bool		       =							False
	a__				: Optional[Union[int, List[int]]]		       =							None
	a__				: Optional[int]		       =							None
	a__				: Optional[Union[str, List[str]]]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: bool		       =							True
	a__				: Optional[str]		       =							None
	a__				: str		       =							"."
	a__				: Optional[str]		       =							None
	a__				: str		       =							'"'
	a__				: int		       =							0
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: Optional[str]		       =							None
	a__				: bool		       =							True
	a__				: bool		       =							True
	a__				: int		       =							0
	a__				: bool		       =							True
	a__				: bool		       =							False
	a__				: Optional[str]		       =							None
	a__				: int		       =							1_0000
	a__				: Optional[datasets.Features]		       =							None
	a__				: Optional[str]		       =							"strict"
	a__				: Literal["error", "warn", "skip"]		       =							"error"
	a__				: Optional[str]		       =							None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							if self.delimiter is not None:
													UpperCAmelCase_=       self.delimiter
							if self.column_names is not None:
													UpperCAmelCase_=       self.column_names




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							UpperCAmelCase_=       {
							    """sep""": self.sep,
							    """header""": self.header,
							    """names""": self.names,
							    """index_col""": self.index_col,
							    """usecols""": self.usecols,
							    """prefix""": self.prefix,
							    """mangle_dupe_cols""": self.mangle_dupe_cols,
							    """engine""": self.engine,
							    """converters""": self.converters,
							    """true_values""": self.true_values,
							    """false_values""": self.false_values,
							    """skipinitialspace""": self.skipinitialspace,
							    """skiprows""": self.skiprows,
							    """nrows""": self.nrows,
							    """na_values""": self.na_values,
							    """keep_default_na""": self.keep_default_na,
							    """na_filter""": self.na_filter,
							    """verbose""": self.verbose,
							    """skip_blank_lines""": self.skip_blank_lines,
							    """thousands""": self.thousands,
							    """decimal""": self.decimal,
							    """lineterminator""": self.lineterminator,
							    """quotechar""": self.quotechar,
							    """quoting""": self.quoting,
							    """escapechar""": self.escapechar,
							    """comment""": self.comment,
							    """encoding""": self.encoding,
							    """dialect""": self.dialect,
							    """error_bad_lines""": self.error_bad_lines,
							    """warn_bad_lines""": self.warn_bad_lines,
							    """skipfooter""": self.skipfooter,
							    """doublequote""": self.doublequote,
							    """memory_map""": self.memory_map,
							    """float_precision""": self.float_precision,
							    """chunksize""": self.chunksize,
							    """encoding_errors""": self.encoding_errors,
							    """on_bad_lines""": self.on_bad_lines,
							    """date_format""": self.date_format,
							}

							# some kwargs must not be passed if they don't have a default value
							# some others are deprecated and we can also not pass them if they are the default value
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
													if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,  __UpperCAmelCase					):
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 2):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

							return pd_read_csv_kwargs






class 		lowercase						(			datasets.ArrowBasedBuilder):






	"""simple docstring"""


	a__				: int		       =							CsvConfig




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							return datasets.DatasetInfo(features=self.config.features					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							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_=       dl_manager.download_and_extract(self.config.data_files					)
							if isinstance(__UpperCAmelCase						,  (str, list, tuple)					):
													UpperCAmelCase_=       data_files
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,  gen_kwargs={"""files""": files}					)]
							UpperCAmelCase_=       []
							for split_name, files in data_files.items():
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													splits.append(datasets.SplitGenerator(name=__UpperCAmelCase						,  gen_kwargs={"""files""": files}					)					)
							return splits




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						pa.Table					)      ->			pa.Table:
							if self.config.features is not None:
													UpperCAmelCase_=       self.config.features.arrow_schema
													if all(not require_storage_cast(__UpperCAmelCase					) for feature in self.config.features.values()					):
																			# cheaper cast
																			UpperCAmelCase_=       pa.Table.from_arrays([pa_table[field.name] for field in schema]						,  schema=__UpperCAmelCase					)
													else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			UpperCAmelCase_=       table_cast(__UpperCAmelCase						,  __UpperCAmelCase					)
							return pa_table




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							UpperCAmelCase_=       self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							UpperCAmelCase_=       (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase					) else object
							        for name, dtype, feature in zip(schema.names						,  schema.types						,  self.config.features.values()					)
							    }
							    if schema is not None
							    else None
							)
							for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase					)					):
													UpperCAmelCase_=       pd.read_csv(__UpperCAmelCase						,  iterator=__UpperCAmelCase						,  dtype=__UpperCAmelCase						,  **self.config.pd_read_csv_kwargs					)
													try:
																			for batch_idx, df in enumerate(__UpperCAmelCase					):
																									UpperCAmelCase_=       pa.Table.from_pandas(__UpperCAmelCase					)
																									# 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					)
													except ValueError as e:
																			logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase					)}: {e}"""					)
																			raise




