'''simple docstring'''




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						""
      __UpperCamelCase:  str						=						(
          None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
      )
      __UpperCamelCase:  str						=						None  # compression type in fsspec. ex: "gzip"
      __UpperCamelCase:  str						=						None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




      def __init__(       self						:				Union[str, Any]						,					A						:				str = ""						,					A						:				Optional[str] = None						,					A						:				Optional[dict] = None						,					**A						:				Dict       ):
             super().__init__(self						,					**A       )
             # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
             _UpperCAmelCase     :  List[str]							      =    fsspec.open(
                 A						,					mode="rb"						,					protocol=A						,					compression=self.compression						,					client_kwargs={
                     "requote_redirect_url": False,  # see https://github.com/huggingface/datasets/pull/5459
                     "trust_env": True,  # Enable reading proxy env variables.
                     **(target_options or {}).pop("client_kwargs"						,					{}       ),  # To avoid issues if it was already passed.
                 }						,					**(target_options or {})						,					)
             _UpperCAmelCase     :  Dict							      =    os.path.basename(self.file.path.split("::"       )[0]       )
             _UpperCAmelCase     :  Dict							      =    (
                 self.compressed_name[: self.compressed_name.rindex("."       )]
                 if "." in self.compressed_name
                 else self.compressed_name
             )
             _UpperCAmelCase     :  Union[str, Any]							      =    None




      @classmethod
      def      _A	(       cls						:				Optional[int]						,					A						:				Union[str, Any]       ):
             # compressed file paths are always relative to the archive root
             return super()._strip_protocol(A       ).lstrip("/"       )




      def      _A	(       self						:				int       ):
             if self.dir_cache is None:
                    _UpperCAmelCase     :  int							      =    {**self.file.fs.info(self.file.path       ), "name": self.uncompressed_name}
                    _UpperCAmelCase     :  str							      =    {f["name"]: f}




      def      _A	(       self						:				Tuple						,					A						:				str       ):
             return self.file.open().read()





      def      _A	(       self						:				List[str]						,					A						:				str						,					A						:				str = "rb"						,					A						:				int=None						,					A						:				Optional[Any]=True						,					A						:				int=None						,					**A						:				Any						,					):
             _UpperCAmelCase     :  List[Any]							      =    self._strip_protocol(A       )
             if mode != "rb":
                    raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"""       )
             return self.file.open()

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						"bz2"
      __UpperCamelCase:  Optional[int]						=						"bz2"
      __UpperCamelCase:  Union[str, Any]						=						".bz2"

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"gzip"
      __UpperCamelCase:  str						=						"gzip"
      __UpperCamelCase:  int						=						".gz"

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						"lz4"
      __UpperCamelCase:  List[Any]						=						"lz4"
      __UpperCamelCase:  int						=						".lz4"

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"xz"
      __UpperCamelCase:  Tuple						=						"xz"
      __UpperCamelCase:  str						=						".xz"

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Any						=						"zstd"
      __UpperCamelCase:  Tuple						=						"zstd"
      __UpperCamelCase:  Tuple						=						".zst"




      def __init__(       self						:				Union[str, Any]						,					A						:				str						,					A						:				str = "rb"						,					A						:				Optional[str] = None						,					A						:				Optional[dict] = None						,					A						:				int = DEFAULT_BLOCK_SIZE						,					**A						:				List[str]						,					):
             super().__init__(
                 fo=A						,					mode=A						,					target_protocol=A						,					target_options=A						,					block_size=A						,					**A						,					)
             # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
             #
             # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
             #     out.close = close
             # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
             #
             # see https://github.com/intake/filesystem_spec/issues/725
             _UpperCAmelCase     :  List[str]							      =    self.file.__enter__

             class 	lowerCamelCase_	:
                   '''simple docstring'''

                   def __init__(       self						:				Optional[Any]						,					A						:				Any       ):
                          _UpperCAmelCase     :  Optional[Any]							      =    file_




                   def __enter__(       self						:				Tuple       ):
                          self._file.__enter__()
                          return self




                   def __exit__(       self						:				str						,					*A						:				Optional[Any]						,					**A						:				Optional[Any]       ):
                          self._file.__exit__(*A						,					**A       )




                   def __iter__(       self						:				int       ):
                          return iter(self._file       )




                   def      _A	(       self						:				Union[str, Any]       ):
                          return next(self._file       )





                   def __getattr__(       self						:				Union[str, Any]						,					A						:				Optional[int]       ):
                          return getattr(self._file						,					A       )

             def fixed_enter(*A						:				int						,					**A						:				Any       ):
                    return WrappedFile(_enter(*A						,					**A       )       )

             _UpperCAmelCase     :  Optional[Any]							      =    fixed_enter









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


UpperCAmelCase_ :						Dict      =      logging.get_logger(__name__)

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

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """layoutlmv3"""





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

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  version.parse("""1.12"""      )





	@property
	def 						_A (		self				:					Optional[int]	):
		# The order of inputs is different for question answering and sequence classification
		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 						_A (		self				:					str	):
		return 1E-5





	@property
	def 						_A (		self				:					Dict	):
		return 12





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					"ProcessorMixin" ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional["TensorType"] = None ,  __lowerCamelCase				:					int = 3 ,  __lowerCamelCase				:					int = 40 ,  __lowerCamelCase				:					int = 40 ,  ):
		setattr(processor.image_processor ,  """apply_ocr""" ,  __lowerCamelCase	)

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

		# Generate dummy bounding boxes
		UpperCamelCase     :Optional[Any]          =	[[[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     :List[str]          =	self._generate_dummy_images(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	dict(
		    processor(
		        __lowerCamelCase ,  text=__lowerCamelCase ,  boxes=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  )	)

		return inputs
