





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      List[Any] ):
    A__       =    TaConfig.from_json_file(_lowerCamelCase )
    print(F"Building PyTorch model from configuration: {config}" )
    A__       =    TaForConditionalGeneration(_lowerCamelCase )

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )

    # Save pytorch-model
    print(F"Save PyTorch model to {pytorch_dump_path}" )
    model.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
  __lowerCAmelCase							:			List[Any]				=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __lowerCAmelCase							:			Optional[int]				=parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''

import torch

from transformers import AutoTokenizer






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
     A__       =    AutoTokenizer.from_pretrained(lowercase_							)
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:

     if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
         A__       =    None
         for l in x:
             A__       =    self.tokenizer(
                 lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
             A__       =    torch.tensor([len(lowercase_							)]							)
             if d is not None:
                 for k in d.keys():
                     A__       =    torch.cat((d[k], t[k])		, 0							)
             else:
                 A__       =    t

         A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
         A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)

     elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
         A__       =    self.tokenizer(
             lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )

     else:
         raise Exception(
             "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
             " strings` for supports are supported."							)

     return d



 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
     A__       =    []
     for idx in range(len(W_query["input_ids"]							)							):
         A__       =    A__       =    range(p_start.shape[1]							)

         A__       =    []
         for start_id in start_indexes:
             for end_id in end_indexes:
                 if start_id < end_id:
                     output.append(
                         (
                             start_id,
                             end_id,
                             p_start[idx][start_id].item(),
                             p_end[idx][end_id].item(),
                         )							)

         output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
         A__       =    []
         for k in range(len(lowercase_							)							):
             if output[k][2] * output[k][3] >= thresh:
                 A__,			A__       =    output[k][0], output[k][1]
                 A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                 temp.append((decoded, output[k][2] * output[k][3])							)

         final_outputs.append(lowercase_							)

     return final_outputs



