


'''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   :   str												=   logging.get_logger(__name__)





def   _a		(lowercase__			:   Tuple						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)
     if "model" in sd.keys():
          __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)['model']

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

     __snake_case           =						{
         '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           =						sd.pop(lowercase__						)

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

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

               __snake_case           =						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           =						torch.split(lowercase__							,   depth // 3							,   dim=0						)

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

     return sd





@torch.no_grad()
def   _a		(lowercase__			:   str							,   lowercase__			:   Tuple							,   lowercase__			:   List[Any]=None						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						load_checkpoint(lowercase__						)

     if config is not None:
          __snake_case           =						OPTConfig.from_pretrained(lowercase__						)
     else:
          __snake_case           =						OPTConfig()

     __snake_case           =						OPTModel(lowercase__						).half().eval()
     model.load_state_dict(lowercase__						)

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


if __name__ == "__main__":
       _a   :   List[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.")
       _a   :   List[Any]												=   parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


_UpperCamelCase											=		'docs/source/en/_toctree.yml'

def 			a_	(				_lowerCAmelCase			) -> Any:
  __lowerCamelCase			:			Optional[int]       			=					defaultdict(_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[Any]       			=					[]
  __lowerCamelCase			:			Union[str, Any]       			=					[]
  for doc in doc_list:
    if "local" in doc:
      counts[doc["local"]] += 1

    if doc["title"].lower() == "overview":
      overview_doc.append({'local': doc['local'], 'title': doc['title']}			)
    else:
      new_doc_list.append(_lowerCAmelCase			)

  __lowerCamelCase			:			Dict       			=					new_doc_list
  __lowerCamelCase			:			Optional[Any]       			=					[key for key, value in counts.items() if value > 1]

  __lowerCamelCase			:			int       			=					[]
  for duplicate_key in duplicates:
    __lowerCamelCase			:			int       			=					list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}			)
    if len(_lowerCAmelCase			) > 1:
      raise ValueError(
          F'{duplicate_key} is present several times in the documentation table of content at '
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'			)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}			)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]			)
  __lowerCamelCase			:			Dict       			=					sorted(_lowerCAmelCase			,key=lambda _lowerCAmelCase			: s["title"].lower()			)

  # "overview" gets special treatment and is always first
  if len(_lowerCAmelCase			) > 1:
    raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'			)

  overview_doc.extend(_lowerCAmelCase			)

  # Sort
  return overview_doc

def 			a_	(				_lowerCAmelCase=False			) -> Optional[Any]:
  with open(_lowerCAmelCase			,encoding='utf-8'			) as f:
    __lowerCamelCase			:			str       			=					yaml.safe_load(f.read()			)

  # Get to the API doc
  __lowerCamelCase			:			Union[str, Any]       			=					0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  __lowerCamelCase			:			List[str]       			=					content[api_idx]['sections']

  # Then to the model doc
  __lowerCamelCase			:			List[Any]       			=					0
  while api_doc[scheduler_idx]["title"] != "Schedulers":
    scheduler_idx += 1

  __lowerCamelCase			:			List[Any]       			=					api_doc[scheduler_idx]['sections']
  __lowerCamelCase			:			Optional[Any]       			=					clean_doc_toc(_lowerCAmelCase			)

  __lowerCamelCase			:			str       			=					False
  if new_scheduler_doc != scheduler_doc:
    __lowerCamelCase			:			int       			=					True
    if overwrite:
      __lowerCamelCase			:			Any       			=					new_scheduler_doc

  if diff:
    if overwrite:
      __lowerCamelCase			:			Tuple       			=					api_doc
      with open(_lowerCAmelCase			,'w'			,encoding='utf-8'			) as f:
        f.write(yaml.dump(_lowerCAmelCase			,allow_unicode=_lowerCAmelCase			)			)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'			)

def 			a_	(				_lowerCAmelCase=False			) -> List[Any]:
  with open(_lowerCAmelCase			,encoding='utf-8'			) as f:
    __lowerCamelCase			:			List[str]       			=					yaml.safe_load(f.read()			)

  # Get to the API doc
  __lowerCamelCase			:			List[Any]       			=					0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  __lowerCamelCase			:			Optional[Any]       			=					content[api_idx]['sections']

  # Then to the model doc
  __lowerCamelCase			:			List[Any]       			=					0
  while api_doc[pipeline_idx]["title"] != "Pipelines":
    pipeline_idx += 1

  __lowerCamelCase			:			int       			=					False
  __lowerCamelCase			:			str       			=					api_doc[pipeline_idx]['sections']
  __lowerCamelCase			:			Optional[Any]       			=					[]

  # sort sub pipeline docs
  for pipeline_doc in pipeline_docs:
    if "section" in pipeline_doc:
      __lowerCamelCase			:			str       			=					pipeline_doc['section']
      __lowerCamelCase			:			Optional[Any]       			=					clean_doc_toc(_lowerCAmelCase			)
      if overwrite:
        __lowerCamelCase			:			Union[str, Any]       			=					new_sub_pipeline_doc
    new_pipeline_docs.append(_lowerCAmelCase			)

  # sort overall pipeline doc
  __lowerCamelCase			:			int       			=					clean_doc_toc(_lowerCAmelCase			)

  if new_pipeline_docs != pipeline_docs:
    __lowerCamelCase			:			Tuple       			=					True
    if overwrite:
      __lowerCamelCase			:			int       			=					new_pipeline_docs

  if diff:
    if overwrite:
      __lowerCamelCase			:			Tuple       			=					api_doc
      with open(_lowerCAmelCase			,'w'			,encoding='utf-8'			) as f:
        f.write(yaml.dump(_lowerCAmelCase			,allow_unicode=_lowerCAmelCase			)			)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'			)


if __name__ == "__main__":
   _UpperCamelCase											=		argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase											=		parser.parse_args()

   check_scheduler_doc(args.fix_and_overwrite)
   check_pipeline_doc(args.fix_and_overwrite)






