


"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Optional[Any]							  =  '''donut-swin'''

   __UpperCAmelCase			:	str							  =  {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }







   def __init__(     self						,     UpperCamelCase__=224						,     UpperCamelCase__=4						,     UpperCamelCase__=3						,     UpperCamelCase__=96						,     UpperCamelCase__=[2, 2, 6, 2]						,     UpperCamelCase__=[3, 6, 12, 24]						,     UpperCamelCase__=7						,     UpperCamelCase__=4.0						,     UpperCamelCase__=True						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.1						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=False						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1e-5						,     **UpperCamelCase__						,     )     ->		Tuple:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)

       snake_case   :				Any				 =   image_size
       snake_case   :				Optional[Any]				 =   patch_size
       snake_case   :				Tuple				 =   num_channels
       snake_case   :				Optional[int]				 =   embed_dim
       snake_case   :				Any				 =   depths
       snake_case   :				Optional[int]				 =   len(UpperCamelCase__						)
       snake_case   :				List[Any]				 =   num_heads
       snake_case   :				Any				 =   window_size
       snake_case   :				Dict				 =   mlp_ratio
       snake_case   :				Optional[Any]				 =   qkv_bias
       snake_case   :				Any				 =   hidden_dropout_prob
       snake_case   :				Union[str, Any]				 =   attention_probs_dropout_prob
       snake_case   :				List[str]				 =   drop_path_rate
       snake_case   :				Tuple				 =   hidden_act
       snake_case   :				Tuple				 =   use_absolute_embeddings
       snake_case   :				List[Any]				 =   layer_norm_eps
       snake_case   :				Optional[int]				 =   initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       snake_case   :				List[str]				 =   int(embed_dim * 2 ** (len(UpperCamelCase__						) - 1)						)









"""simple docstring"""



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__snake_case             =   data_utils.TransfoXLTokenizer
__snake_case             =   data_utils.TransfoXLCorpus
__snake_case             =   data_utils
__snake_case             =   data_utils







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  List[Any]						,							lowercase  :  Union[str, Any]			)     ->						List[Any]:


    """simple docstring"""





    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(lowercase						,							"rb"			) as fp:
            snake_case   :				int				 =   pickle.load(lowercase						,							encoding="latin1"			)
        # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
        snake_case   :				int				 =   pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
        print(F'Save vocabulary to {pytorch_vocab_dump_path}'			)
        snake_case   :				str				 =   corpus.vocab.__dict__
        torch.save(lowercase						,							lowercase			)

        snake_case   :				str				 =   corpus.__dict__
        corpus_dict_no_vocab.pop("vocab"						,							lowercase			)
        snake_case   :				Dict				 =   pytorch_dump_folder_path + "/" + CORPUS_NAME
        print(F'Save dataset to {pytorch_dataset_dump_path}'			)
        torch.save(lowercase						,							lowercase			)

    if tf_checkpoint_path:
        # Convert a pre-trained TensorFlow model
        snake_case   :				Union[str, Any]				 =   os.path.abspath(lowercase			)
        snake_case   :				str				 =   os.path.abspath(lowercase			)

        print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'			)
        # Initialise PyTorch model
        if transfo_xl_config_file == "":
            snake_case   :				int				 =   TransfoXLConfig()
        else:
            snake_case   :				Optional[int]				 =   TransfoXLConfig.from_json_file(lowercase			)
        print(F'Building PyTorch model from configuration: {config}'			)
        snake_case   :				str				 =   TransfoXLLMHeadModel(lowercase			)

        snake_case   :				str				 =   load_tf_weights_in_transfo_xl(lowercase						,							lowercase						,							lowercase			)
        # Save pytorch-model
        snake_case   :				Union[str, Any]				 =   os.path.join(lowercase						,							lowercase			)
        snake_case   :				Optional[Any]				 =   os.path.join(lowercase						,							lowercase			)
        print(F'Save PyTorch model to {os.path.abspath(lowercase			)}'			)
        torch.save(model.state_dict()						,							lowercase			)
        print(F'Save configuration file to {os.path.abspath(lowercase			)}'			)
        with open(lowercase						,							"w"						,							encoding="utf-8"			) as f:
            f.write(config.to_json_string()			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
  )
  parser.add_argument(
      """--tf_checkpoint_path""",
      default="""""",
      type=str,
      help="""An optional path to a TensorFlow checkpoint path to be converted.""",
  )
  parser.add_argument(
      """--transfo_xl_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--transfo_xl_dataset_file""",
      default="""""",
      type=str,
      help="""An optional dataset file to be converted in a vocabulary.""",
  )
  __snake_case             =   parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )





