






def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count < 0:
            raise ValueError('The given input must be positive')

      # get the generated string sequence
      UpperCamelCase					       =  gray_code_sequence_string(_UpperCamelCase)
      #
      # convert them to integers
      for i in range(len(_UpperCamelCase)):
            UpperCamelCase					       =  int(sequence[i]       ,							2)

      return sequence
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count == 0:
            return ["0"]

      if bit_count == 1:
            return ["0", "1"]

      UpperCamelCase					       =  1 << bit_count  # defines the length of the sequence
      # 1<< n is equivalent to 2^n

      # recursive answer will generate answer for n-1 bits
      UpperCamelCase					       =  gray_code_sequence_string(bit_count - 1)

      UpperCamelCase					       =  []

      # append 0 to first half of the smaller sequence generated
      for i in range(seq_len // 2):
            UpperCamelCase					       =  '0' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      # append 1 to second half ... start from the end of the list
      for i in reversed(range(seq_len // 2)):
            UpperCamelCase					       =  '1' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()











import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          '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:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

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

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  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
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

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


if __name__ == "__main__":
       __magic_name__   :  Any        =							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.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



