



'''simple docstring'''





from __future__ import annotations

from collections import deque






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      List[Any]     ,      _a :      list[str]  )   ->   Dict:
    __lowerCamelCase			:			Union[str, Any]       			=					[]
    self.adlist.append(
        {'value': '', 'next_states': [], 'fail_state': 0, 'output': []}  )

    for keyword in keywords:
      self.add_keyword(_SCREAMING_SNAKE_CASE  )
    self.set_fail_transitions()




  def 		_lowercase	(					self :      Any     ,      _a :      int     ,      _a :      str  )   ->   Any:




    for state in self.adlist[current_state]["next_states"]:
      if char == self.adlist[state]["value"]:
        return state
    return None




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      str  )   ->   Optional[int]:
    __lowerCamelCase			:			Any       			=					0
    for character in keyword:
      __lowerCamelCase			:			Optional[int]       			=					self.find_next_state(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  )
      if next_state is None:
        self.adlist.append(
            {
                'value': character,
                'next_states': [],
                'fail_state': 0,
                'output': [],
            }  )
        self.adlist[current_state]["next_states"].append(len(self.adlist  ) - 1  )
        __lowerCamelCase			:			List[str]       			=					len(self.adlist  ) - 1
      else:
        __lowerCamelCase			:			Optional[Any]       			=					next_state
    self.adlist[current_state]["output"].append(_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      Any  )   ->   List[Any]:
    __lowerCamelCase			:			Any       			=					deque()
    for node in self.adlist[0]["next_states"]:
      q.append(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase			:			str       			=					0
    while q:
      __lowerCamelCase			:			Optional[Any]       			=					q.popleft()
      for child in self.adlist[r]["next_states"]:
        q.append(_SCREAMING_SNAKE_CASE  )
        __lowerCamelCase			:			List[str]       			=					self.adlist[r]['fail_state']
        while (
            self.find_next_state(_SCREAMING_SNAKE_CASE     ,      self.adlist[child]['value']  ) is None
            and state != 0
        ):
          __lowerCamelCase			:			Dict       			=					self.adlist[state]['fail_state']
        __lowerCamelCase			:			Tuple       			=					self.find_next_state(
            _SCREAMING_SNAKE_CASE     ,      self.adlist[child]['value']  )
        if self.adlist[child]["fail_state"] is None:
          __lowerCamelCase			:			Union[str, Any]       			=					0
        __lowerCamelCase			:			Any       			=					(
            self.adlist[child]['output']
            + self.adlist[self.adlist[child]['fail_state']]['output']
        )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      str  )   ->   int:
    __lowerCamelCase			:			Any       			=					{}  # returns a dict with keywords and list of its occurrences
    __lowerCamelCase			:			List[str]       			=					0
    for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
      while (
          self.find_next_state(_SCREAMING_SNAKE_CASE     ,      string[i]  ) is None
          and current_state != 0
      ):
        __lowerCamelCase			:			Optional[Any]       			=					self.adlist[current_state]['fail_state']
      __lowerCamelCase			:			List[Any]       			=					self.find_next_state(_SCREAMING_SNAKE_CASE     ,      string[i]  )
      if next_state is None:
        __lowerCamelCase			:			int       			=					0
      else:
        __lowerCamelCase			:			Any       			=					next_state
        for key in self.adlist[current_state]["output"]:
          if key not in result:
            __lowerCamelCase			:			Optional[int]       			=					[]
          result[key].append(i - len(_SCREAMING_SNAKE_CASE  ) + 1  )
    return result


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)



