



'''simple docstring'''





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase		)		-> Any:
      snake_case__  :    int    						=      torch.load(_lowerCAmelCase     ,   map_location="""cpu"""		)
      if "model" in sd.keys():
            snake_case__  :    Union[str, Any]    						=      torch.load(_lowerCAmelCase     ,   map_location="""cpu"""		)["""model"""]

      # pop unnecessary weights
      snake_case__  :    Dict    						=      [
          """decoder.version""",
          """decoder.output_projection.weight""",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      {
          """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
          """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
          """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
          """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  snake_case__  :    Tuple    						=      sd.pop(_lowerCAmelCase		)

      snake_case__  :    Any    						=      list(sd.keys()		)
      for key in keys:
            if ".qkv_proj." in key:
                  snake_case__  :    str    						=      sd[key]
                  # We split QKV in separate Q,K,V

                  snake_case__  :    str    						=      key.replace(""".qkv_proj."""     ,   """.q_proj."""		)
                  snake_case__  :    Any    						=      key.replace(""".qkv_proj."""     ,   """.k_proj."""		)
                  snake_case__  :    int    						=      key.replace(""".qkv_proj."""     ,   """.v_proj."""		)

                  snake_case__  :    str    						=      value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  snake_case__						,				snake_case__						,				snake_case__  :    Dict    						=      torch.split(_lowerCAmelCase     ,   depth // 3     ,   dim=0		)

                  snake_case__  :    int    						=      q
                  snake_case__  :    List[Any]    						=      k
                  snake_case__  :    Any    						=      v
                  del sd[key]

      return sd





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=None		)		-> Any:
      snake_case__  :    Any    						=      load_checkpoint(_lowerCAmelCase		)

      if config is not None:
            snake_case__  :    Any    						=      OPTConfig.from_pretrained(_lowerCAmelCase		)
      else:
            snake_case__  :    Union[str, Any]    						=      OPTConfig()

      snake_case__  :    List[Any]    						=      OPTModel(_lowerCAmelCase		).half().eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check results
      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      model.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--fairseq_path",
           type=str,
           help=(
               "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
               " https://huggingface.co/models?other=opt_metasq"
           ),
       )
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
       __a          =		parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =data
     a__  =[0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]



   @staticmethod
   def  _UpperCAmelCase (				lowercase_,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     return ((n << b) | (n >> (32 - b))) & 0Xffffffff



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =b'''\x80''' + b'''\x00''' * (63 - (len(self.data       ) + 8) % 64)
     a__  =self.data + padding + struct.pack('''>Q''',    8 * len(self.data       )       )
     return padded_data



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     return [
         self.padded_data[i : i + 64] for i in range(0,    len(self.padded_data       ),    64       )
     ]



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[Any]:






     """simple docstring"""






     a__  =list(struct.unpack('''>16L''',    lowercase_       )       ) + [0] * 64
     for i in range(16,    80       ):
       a__  =self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),    1       )
     return w



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.padding()
     a__  =self.split_blocks()
     for block in self.blocks:
       a__  =self.expand_block(lowercase_       )
       a__,      a__,      a__,      a__,      a__  =self.h
       for i in range(0,    80       ):
         if 0 <= i < 20:
           a__  =(b & c) | ((~b) & d)
           a__  =0X5a827999
         elif 20 <= i < 40:
           a__  =b ^ c ^ d
           a__  =0X6ed9eba1
         elif 40 <= i < 60:
           a__  =(b & c) | (b & d) | (c & d)
           a__  =0X8f1bbcdc
         elif 60 <= i < 80:
           a__  =b ^ c ^ d
           a__  =0Xca62c1d6
         a__,      a__,      a__,      a__,      a__  =(
             self.rotate(lowercase_,    5       ) + f + e + k + expanded_block[i] & 0Xffffffff,
             a,
             self.rotate(lowercase_,    30       ),
             c,
             d,
         )
       a__  =(
           self.h[0] + a & 0Xffffffff,
           self.h[1] + b & 0Xffffffff,
           self.h[2] + c & 0Xffffffff,
           self.h[3] + d & 0Xffffffff,
           self.h[4] + e & 0Xffffffff,
       )
     return ("{:08x}" * 5).format(*self.h       )





def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =b'''Test String'''
  assert SHAaHash(_A							).final_hash() == hashlib.shaa(_A							).hexdigest()  # noqa: S324




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =argparse.ArgumentParser(description='''Process some strings or files'''							)
  parser.add_argument(
      '''--string'''	,      dest='''input_string'''	,      default='''Hello World!! Welcome to Cryptography'''	,      help='''Hash the string'''	,      )
  parser.add_argument('''--file'''	,      dest='''input_file'''	,      help='''Hash contents of a file'''							)
  a__  =parser.parse_args()
  a__  =args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file	,      '''rb'''							) as f:
      a__  =f.read()
  else:
    a__  =bytes(_A	,      '''utf-8'''							)
  print(SHAaHash(_A							).final_hash()							)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod()
