





import argparse
from pathlib import Path

import torch

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


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				str			      =	torch.load(_lowerCAmelCase  ,map_location="""cpu"""     )
   if "model" in sd.keys():
      A_     :				Tuple			      =	torch.load(_lowerCAmelCase  ,map_location="""cpu"""     )["""model"""]

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

   A_     :				Optional[Any]			      =	{
       """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:
         A_     :				Dict			      =	sd.pop(_lowerCAmelCase     )

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

         A_     :				Dict			      =	key.replace(""".qkv_proj."""  ,""".q_proj."""     )
         A_     :				List[str]			      =	key.replace(""".qkv_proj."""  ,""".k_proj."""     )
         A_     :				List[str]			      =	key.replace(""".qkv_proj."""  ,""".v_proj."""     )

         A_     :				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
         A_     :				Dict			      =	torch.split(_lowerCAmelCase  ,depth // 3  ,dim=0     )

         A_     :				Tuple			      =	q
         A_     :				Optional[int]			      =	k
         A_     :				Dict			      =	v
         del sd[key]

   return sd



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None     ):
   '''simple docstring'''


   A_     :				List[Any]			      =	load_checkpoint(_lowerCAmelCase     )

   if config is not None:
      A_     :				List[Any]			      =	OPTConfig.from_pretrained(_lowerCAmelCase     )
   else:
      A_     :				Dict			      =	OPTConfig()

   A_     :				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__":
      _lowerCAmelCase 				=     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.""")
      _lowerCAmelCase 				=     parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)













import datasets

from .evaluate import evaluate


_lowerCAmelCase 				=     """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

_lowerCAmelCase 				=     """
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

_lowerCAmelCase 				=     """
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION     )
class    _UpperCAmelCase							(		datasets.Metric     ):
      def 		_lowerCamelCase      (		self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION      ,       citation=_CITATION      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
                 {
                     """predictions""": {"""id""": datasets.Value("""string"""       ), """prediction_text""": datasets.Value("""string"""       )},
                     """references""": {
                         """id""": datasets.Value("""string"""       ),
                         """answers""": datasets.features.Sequence(
                             {
                                 """text""": datasets.Value("""string"""       ),
                                 """answer_start""": datasets.Value("""int32"""       ),
                             }       ),
                     },
                 }       )      ,       codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]      ,       reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]      ,       )






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
         A_     :				Union[str, Any]			      =	{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
         A_     :				Optional[Any]			      =	[
             {
                 """paragraphs""": [
                     {
                         """qas""": [
                             {
                                 """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                 """id""": ref["""id"""],
                             }
                             for ref in references
                         ]
                     }
                 ]
             }
         ]
         A_     :				Union[str, Any]			      =	evaluate(dataset=a__      ,       predictions=a__       )
         return score






