



'''simple docstring'''






import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


A_				:					List[Any]							=      get_logger(__name__)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       if text.startswith(lowerCAmelCase_						):
              return text[len(lowerCAmelCase_						) :]
       return text  # or whatever




def        snake_case_   (	lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       return {remove_prefix(lowerCAmelCase_			,  """model."""						): v for k, v in sd.items()}




def        snake_case_   (	lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					{}
       for k in state_dicts[0].keys():
              _UpperCAmelCase    :		List[Any]     	=					[sd[k] for sd in state_dicts]
              _UpperCAmelCase    :		List[Any]     	=					sum(lowerCAmelCase_						) / len(lowerCAmelCase_						)
              assert isinstance(lowerCAmelCase_			,  torch.Tensor						)
              _UpperCAmelCase    :		Union[str, Any]     	=					new_t
       return new_sd




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						None:





       '''simple docstring'''


       _UpperCAmelCase    :		str     	=					AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_						)
       if os.path.isfile(lowerCAmelCase_						):
              _UpperCAmelCase    :		str     	=					[pl_ckpt_path]
       else:
              assert os.path.isdir(lowerCAmelCase_						)
              _UpperCAmelCase    :		Optional[int]     	=					list(Path(lowerCAmelCase_						).glob("""*.ckpt"""						)						)
              assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

       if len(lowerCAmelCase_						) > 1:
              logger.info(F'''averaging the weights of {ckpt_files}'''						)

       _UpperCAmelCase    :		Any     	=					[sanitize(torch.load(lowerCAmelCase_			,  map_location="""cpu"""						)["""state_dict"""]						) for x in ckpt_files]
       _UpperCAmelCase    :		Optional[Any]     	=					average_state_dicts(lowerCAmelCase_						)

       _UpperCAmelCase	,_UpperCAmelCase    :		Dict     	=					hf_model.load_state_dict(lowerCAmelCase_			,  strict=lowerCAmelCase_						)
       assert not missing, F'''missing keys: {missing}'''
       hf_model.save_pretrained(lowerCAmelCase_						)
       try:
              _UpperCAmelCase    :		List[str]     	=					AutoTokenizer.from_pretrained(lowerCAmelCase_						)
              tok.save_pretrained(lowerCAmelCase_						)
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
   fire.Fire(convert_pl_to_hf)






"""simple docstring"""



def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , ):
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )

     A__				      =	[
         [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
     A__				      =	[
         ["""0""" for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]

     for i in range(1       , len_source_seq + 1      ):
          A__				      =	i * delete_cost
          A__				      =	F"""D{source_seq[i - 1]:c}"""

     for i in range(1       , len_destination_seq + 1      ):
          A__				      =	i * insert_cost
          A__				      =	F"""I{destination_seq[i - 1]:c}"""

     for i in range(1       , len_source_seq + 1      ):
          for j in range(1       , len_destination_seq + 1      ):
               if source_seq[i - 1] == destination_seq[j - 1]:
                    A__				      =	costs[i - 1][j - 1] + copy_cost
                    A__				      =	F"""C{source_seq[i - 1]:c}"""
               else:
                    A__				      =	costs[i - 1][j - 1] + replace_cost
                    A__				      =	F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]      )

               if costs[i - 1][j] + delete_cost < costs[i][j]:
                    A__				      =	costs[i - 1][j] + delete_cost
                    A__				      =	F"""D{source_seq[i - 1]:c}"""

               if costs[i][j - 1] + insert_cost < costs[i][j]:
                    A__				      =	costs[i][j - 1] + insert_cost
                    A__				      =	F"""I{destination_seq[j - 1]:c}"""

     return costs, ops





def 			_snake_case  (     UpperCAmelCase_	:       list[list[str]]       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int      ):
     if i == 0 and j == 0:
          return []
     else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , j - 1      )
               seq.append(ops[i][j]      )
               return seq
          elif ops[i][j][0] == "D":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , UpperCAmelCase_      )
               seq.append(ops[i][j]      )
               return seq
          else:
               A__				      =	assemble_transformation(UpperCAmelCase_       , UpperCAmelCase_       , j - 1      )
               seq.append(ops[i][j]      )
               return seq


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						List[Any]					=    compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

 SCREAMING_SNAKE_CASE_       :						Any					=    len(operations)
 SCREAMING_SNAKE_CASE_       :						Dict					=    len(operations[0])
 SCREAMING_SNAKE_CASE_       :						Any					=    assemble_transformation(operations, m - 1, n - 1)

 SCREAMING_SNAKE_CASE_       :						List[str]					=    list('Python')
 SCREAMING_SNAKE_CASE_       :						Dict					=    0
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    0

 with open('min_cost.txt', 'w') as file:
  for op in sequence:
   print(''.join(string))

   if op[0] == "C":
    file.write('%-16s' % 'Copy %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost -= 1
   elif op[0] == "R":
    SCREAMING_SNAKE_CASE_       :						int					=    op[2]

    file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
    file.write('\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('%-16s' % 'Delete %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('%-16s' % 'Insert %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2

   i += 1

  print(''.join(string))
  print('Cost: ', cost)

  file.write('\r\nMinimum cost: ' + str(cost))






