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__    							= logging.get_logger(__name__)

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





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Dict              =      'layoutlmv3'


   def __init__(self					:						Dict	,  __UpperCAmelCase					:						Any=5_0_2_6_5	,  __UpperCAmelCase					:						List[Any]=7_6_8	,  __UpperCAmelCase					:						Union[str, Any]=1_2	,  __UpperCAmelCase					:						Optional[int]=1_2	,  __UpperCAmelCase					:						str=3_0_7_2	,  __UpperCAmelCase					:						Any="gelu"	,  __UpperCAmelCase					:						str=0.1	,  __UpperCAmelCase					:						Tuple=0.1	,  __UpperCAmelCase					:						Dict=5_1_2	,  __UpperCAmelCase					:						Any=2	,  __UpperCAmelCase					:						Optional[int]=0.02	,  __UpperCAmelCase					:						List[str]=1E-5	,  __UpperCAmelCase					:						str=1	,  __UpperCAmelCase					:						Dict=0	,  __UpperCAmelCase					:						Any=2	,  __UpperCAmelCase					:						int=1_0_2_4	,  __UpperCAmelCase					:						List[Any]=1_2_8	,  __UpperCAmelCase					:						Any=1_2_8	,  __UpperCAmelCase					:						Union[str, Any]=True	,  __UpperCAmelCase					:						List[str]=3_2	,  __UpperCAmelCase					:						Union[str, Any]=1_2_8	,  __UpperCAmelCase					:						List[str]=6_4	,  __UpperCAmelCase					:						Any=2_5_6	,  __UpperCAmelCase					:						str=True	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						List[Any]=True	,  __UpperCAmelCase					:						Dict=2_2_4	,  __UpperCAmelCase					:						str=3	,  __UpperCAmelCase					:						Tuple=1_6	,  __UpperCAmelCase					:						str=None	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    Dict:
         """simple docstring"""



         super().__init__(
             vocab_size=__UpperCAmelCase	,  hidden_size=__UpperCAmelCase	,  num_hidden_layers=__UpperCAmelCase	,  num_attention_heads=__UpperCAmelCase	,  intermediate_size=__UpperCAmelCase	,  hidden_act=__UpperCAmelCase	,  hidden_dropout_prob=__UpperCAmelCase	,  attention_probs_dropout_prob=__UpperCAmelCase	,  max_position_embeddings=__UpperCAmelCase	,  type_vocab_size=__UpperCAmelCase	,  initializer_range=__UpperCAmelCase	,  layer_norm_eps=__UpperCAmelCase	,  pad_token_id=__UpperCAmelCase	,  bos_token_id=__UpperCAmelCase	,  eos_token_id=__UpperCAmelCase	,  **__UpperCAmelCase	,  )
         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 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Dict              =      version.parse('1.12'					)


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



         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       lowercase_				(self					:						List[Any]		)						->    float:
         """simple docstring"""



         return 1E-5


   @property
   def       lowercase_				(self					:						Optional[Any]		)						->    int:
         """simple docstring"""



         return 1_2





   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						"ProcessorMixin"	,  __UpperCAmelCase					:						int = -1	,  __UpperCAmelCase					:						int = -1	,  __UpperCAmelCase					:						bool = False	,  __UpperCAmelCase					:						Optional["TensorType"] = None	,  __UpperCAmelCase					:						int = 3	,  __UpperCAmelCase					:						int = 4_0	,  __UpperCAmelCase					:						int = 4_0	,  )						->    Mapping[str, Any]:
         """simple docstring"""



         setattr(processor.image_processor	,  "apply_ocr"	,  __UpperCAmelCase		)

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

         # Generate dummy bounding boxes
         UpperCAmelCase__								=  [[[4_8, 8_4, 7_3, 1_2_8]]] * 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(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  dict(
             processor(
                 __UpperCAmelCase	,  text=__UpperCAmelCase	,  boxes=__UpperCAmelCase	,  return_tensors=__UpperCAmelCase	,  )		)

         return inputs




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser




def        lowerCAmelCase_			(					__A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(add_help=__A,  allow_abbrev=__A							)
      # The main config parser
      UpperCAmelCase__								=  config_command_parser(__A							)
      # The subparser to add commands to
      UpperCAmelCase__								=  config_parser.add_subparsers(title="subcommands",  dest="subcommand"							)

      # Then add other parsers with the parent parser
      default_command_parser(__A,  parents=[parent_parser]							)
      update_command_parser(__A,  parents=[parent_parser]							)

      return config_parser




def        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  get_config_parser()
      UpperCAmelCase__								=  config_parser.parse_args()

      if not hasattr(__A,  "func"							):
            config_parser.print_help()
            exit(1							)

      # Run
      args.func(__A							)


if __name__ == "__main__":
      main()



