


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if gpta_config_file == "":
          UpperCAmelCase_      :							List[Any]       =      GPTaConfig()
     else:
          UpperCAmelCase_      :							Dict       =      GPTaConfig.from_json_file(_lowercase				)
     UpperCAmelCase_      :							int       =      GPTaModel(_lowercase				)

     # Load weights from numpy
     load_tf_weights_in_gpta(_lowercase			,						_lowercase			,						_lowercase				)

     # Save pytorch-model
     UpperCAmelCase_      :							int       =      pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     UpperCAmelCase_      :							List[Any]       =      pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
     torch.save(model.state_dict()			,						_lowercase				)
     print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
     with open(_lowercase			,						'''w'''			,						encoding='''utf-8'''				) as f:
          f.write(config.to_json_string()				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--gpt2_config_file',
         default='',
         type=str,
         help=(
             'An optional config json file corresponding to the pre-trained OpenAI model. \n'
             'This specifies the model architecture.'
         ),
     )
     __a          =			parser.parse_args()
     convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)





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   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''layoutlmv3'''





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


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.12'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         # 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  )			->		float:
         return 1e-5





    @property
    def 							a__				(				self  )			->		int:
         return 12





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 40       ,_SCREAMING_SNAKE_CASE = 40       ,)			->		Mapping[str, Any]:
         setattr(processor.image_processor       ,'''apply_ocr'''       ,_SCREAMING_SNAKE_CASE  )

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

         # Generate dummy bounding boxes
         UpperCAmelCase_      :							Tuple       =      [[[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_      :							Union[str, Any]       =      self._generate_dummy_images(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      dict(
             processor(
                 _SCREAMING_SNAKE_CASE       ,text=_SCREAMING_SNAKE_CASE       ,boxes=_SCREAMING_SNAKE_CASE       ,return_tensors=_SCREAMING_SNAKE_CASE       ,)  )

         return inputs