






'''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  _a(  UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)
      if "model" in sd.keys():
            SCREAMING_SNAKE_CASE__     :				int									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)['''model''']

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

      SCREAMING_SNAKE_CASE__     :				Tuple									={
          '''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:
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=sd.pop(UpperCamelCase__							)

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

                  SCREAMING_SNAKE_CASE__     :				List[Any]									=key.replace('''.qkv_proj.''',	'''.q_proj.'''							)
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=key.replace('''.qkv_proj.''',	'''.k_proj.'''							)
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=key.replace('''.qkv_proj.''',	'''.v_proj.'''							)

                  SCREAMING_SNAKE_CASE__     :				List[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
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.split(UpperCamelCase__,	depth // 3,	dim=0							)

                  SCREAMING_SNAKE_CASE__     :				Any									=q
                  SCREAMING_SNAKE_CASE__     :				Any									=k
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=v
                  del sd[key]

      return sd





@torch.no_grad()
def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Union[str, Any]=None							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Dict									=load_checkpoint(UpperCamelCase__							)

      if config is not None:
            SCREAMING_SNAKE_CASE__     :				Tuple									=OPTConfig.from_pretrained(UpperCamelCase__							)
      else:
            SCREAMING_SNAKE_CASE__     :				str									=OPTConfig()

      SCREAMING_SNAKE_CASE__     :				Dict									=OPTModel(UpperCamelCase__							).half().eval()
      model.load_state_dict(UpperCamelCase__							)

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


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)







'''simple docstring'''
def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=len(UpperCamelCase__							) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(UpperCamelCase__							):
                  return None

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left
                        SCREAMING_SNAKE_CASE__     :				Optional[Any]									=point
                  elif point > right:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									=right
                        SCREAMING_SNAKE_CASE__     :				Tuple									=point
                  else:
                        if item < current_item:
                              SCREAMING_SNAKE_CASE__     :				str									=point - 1
                        else:
                              SCREAMING_SNAKE_CASE__     :				Tuple									=point + 1
      return None





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      SCREAMING_SNAKE_CASE__     :				Dict									=left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(UpperCamelCase__							):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      elif point > right:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	point - 1							)
            else:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	point + 1,	UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      if collection != sorted(UpperCamelCase__							):
            raise ValueError('''Collection must be ascending sorted'''							)
      return True


if __name__ == "__main__":
  import sys
  a_					= 0
  if debug == 1:
    a_					= [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit('Sequence must be ascending sorted to apply interpolation search')
    a_					= 6_7

  a_					= interpolation_search(collection, target)
  if result is not None:
    print(F'''{target} found at positions: {result}''')
  else:
    print('Not found')