'''simple docstring'''




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=True				,  UpperCamelCase__="pt"				)	->    List[Any]:
    __lowerCamelCase		=  {'''add_prefix_space''': True} if isinstance(UpperCamelCase__				,  UpperCamelCase__				) and not line.startswith(''' '''				) else {}
    __lowerCamelCase		=  padding_side
    return tokenizer(
        [line]				,  max_length=UpperCamelCase__				,  padding='''max_length''' if pad_to_max_length else None				,  truncation=UpperCamelCase__				,  return_tensors=UpperCamelCase__				,  add_special_tokens=UpperCamelCase__				,  **UpperCamelCase__				,  )






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=None				,  )	->    Tuple:
    __lowerCamelCase		=  input_ids.ne(UpperCamelCase__				).any(dim=0				)
    if attention_mask is None:
        return input_ids[:, keep_column_mask]
    else:
        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		Dict  ,				a		:		Optional[Any]  ,				a		:		int  ,				a		:		Optional[int]  ,				a		:		Union[str, Any]  ,				a		:		Optional[int]="train"  ,				a		:		List[str]=None  ,				a		:		List[str]=None  ,				a		:		List[str]=None  ,				a		:		Dict=""  ,				):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase		=  Path(a    ).joinpath(type_path + '''.source'''    )
         __lowerCamelCase		=  Path(a    ).joinpath(type_path + '''.target'''    )
         __lowerCamelCase		=  self.get_char_lens(self.src_file    )
         __lowerCamelCase		=  max_source_length
         __lowerCamelCase		=  max_target_length
         assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
         __lowerCamelCase		=  tokenizer
         __lowerCamelCase		=  prefix
         if n_obs is not None:
             __lowerCamelCase		=  self.src_lens[:n_obs]
         __lowerCamelCase		=  src_lang
         __lowerCamelCase		=  tgt_lang






     def __len__( self		:		Dict    ):





         """simple docstring"""



         return len(self.src_lens    )






     def __getitem__( self		:		int  ,				a		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  index + 1  # linecache starts at 1
         __lowerCamelCase		=  self.prefix + linecache.getline(str(self.src_file    )  ,				a    ).rstrip('''\n'''    )
         __lowerCamelCase		=  linecache.getline(str(self.tgt_file    )  ,				a    ).rstrip('''\n'''    )
         assert source_line, f"""empty source line for index {index}"""
         assert tgt_line, f"""empty tgt line for index {index}"""

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer  ,				a    ):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         __lowerCamelCase		=  (
             self.tokenizer.question_encoder if isinstance(self.tokenizer  ,				a    ) else self.tokenizer
         )
         __lowerCamelCase		=  self.tokenizer.generator if isinstance(self.tokenizer  ,				a    ) else self.tokenizer

         __lowerCamelCase		=  encode_line(a  ,				a  ,				self.max_source_length  ,				'''right'''    )
         __lowerCamelCase		=  encode_line(a  ,				a  ,				self.max_target_length  ,				'''right'''    )

         __lowerCamelCase		=  source_inputs['''input_ids'''].squeeze()
         __lowerCamelCase		=  target_inputs['''input_ids'''].squeeze()
         __lowerCamelCase		=  source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }






     @staticmethod
     def    SCREAMING_SNAKE_CASE__   ( a		:		Any    ):





         """simple docstring"""



         return [len(a    ) for x in Path(a    ).open().readlines()]






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Optional[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.stack([x['''input_ids'''] for x in batch]    )
         __lowerCamelCase		=  torch.stack([x['''attention_mask'''] for x in batch]    )
         __lowerCamelCase		=  torch.stack([x['''decoder_input_ids'''] for x in batch]    )
         __lowerCamelCase		=  (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer  ,				a    )
             else self.tokenizer.pad_token_id
         )
         __lowerCamelCase		=  (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer  ,				a    )
             else self.tokenizer.pad_token_id
         )
         __lowerCamelCase		=  trim_batch(a  ,				a    )
         __lowerCamelCase	,  __lowerCamelCase		=  trim_batch(a  ,				a  ,				attention_mask=a    )
         __lowerCamelCase		=  {
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch
__UpperCAmelCase     							=getLogger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Dict:
    return list(itertools.chain.from_iterable(UpperCamelCase__				)				)






def __lowerCAmelCase (							UpperCamelCase__				)	->    None:
    __lowerCamelCase		=  get_git_info()
    save_json(UpperCamelCase__				,  os.path.join(UpperCamelCase__				,  '''git_log.json'''				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=4				,  **UpperCamelCase__				)	->    Tuple:
    with open(UpperCamelCase__				,  '''w'''				) as f:
        json.dump(UpperCamelCase__				,  UpperCamelCase__				,  indent=UpperCamelCase__				,  **UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Optional[Any]:
    with open(UpperCamelCase__				) as f:
        return json.load(UpperCamelCase__				)






def __lowerCAmelCase (							)	->    Union[str, Any]:
    __lowerCamelCase		=  git.Repo(search_parent_directories=UpperCamelCase__				)
    __lowerCamelCase		=  {
        '''repo_id''': str(UpperCamelCase__				),
        '''repo_sha''': str(repo.head.object.hexsha				),
        '''repo_branch''': str(repo.active_branch				),
        '''hostname''': str(socket.gethostname()				),
    }
    return repo_infos






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    List:
    return list(map(UpperCamelCase__				,  UpperCamelCase__				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    with open(UpperCamelCase__				,  '''wb'''				) as f:
        return pickle.dump(UpperCamelCase__				,  UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Dict:

    def remove_articles(UpperCamelCase__				):
        return re.sub(r'''\b(a|an|the)\b'''				,  ''' '''				,  UpperCamelCase__				)

    def white_space_fix(UpperCamelCase__				):
        return " ".join(text.split()				)

    def remove_punc(UpperCamelCase__				):
        __lowerCamelCase		=  set(string.punctuation				)
        return "".join(ch for ch in text if ch not in exclude				)

    def lower(UpperCamelCase__				):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__				)				)				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  normalize_answer(UpperCamelCase__				).split()
    __lowerCamelCase		=  normalize_answer(UpperCamelCase__				).split()
    __lowerCamelCase		=  Counter(UpperCamelCase__				) & Counter(UpperCamelCase__				)
    __lowerCamelCase		=  sum(common.values()				)
    if num_same == 0:
        return 0
    __lowerCamelCase		=  1.0 * num_same / len(UpperCamelCase__				)
    __lowerCamelCase		=  1.0 * num_same / len(UpperCamelCase__				)
    __lowerCamelCase		=  (2 * precision * recall) / (precision + recall)
    return fa






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Optional[Any]:
    return normalize_answer(UpperCamelCase__				) == normalize_answer(UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    assert len(UpperCamelCase__				) == len(UpperCamelCase__				)
    __lowerCamelCase		=  0
    for hypo, pred in zip(UpperCamelCase__				,  UpperCamelCase__				):
        em += exact_match_score(UpperCamelCase__				,  UpperCamelCase__				)
    if len(UpperCamelCase__				) > 0:
        em /= len(UpperCamelCase__				)
    return {"em": em}






def __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
    return model_prefix.startswith('''rag'''				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Union[str, Any]:
    __lowerCamelCase		=  {p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    __lowerCamelCase		=  '''dropout_rate'''
    for p in extra_params:
        if getattr(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				):
            if not hasattr(UpperCamelCase__				,  UpperCamelCase__				) and not hasattr(UpperCamelCase__				,  equivalent_param[p]				):
                logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCamelCase__				)				)
                delattr(UpperCamelCase__				,  UpperCamelCase__				)
                continue
            __lowerCamelCase		=  p if hasattr(UpperCamelCase__				,  UpperCamelCase__				) else equivalent_param[p]
            setattr(UpperCamelCase__				,  UpperCamelCase__				,  getattr(UpperCamelCase__				,  UpperCamelCase__				)				)
            delattr(UpperCamelCase__				,  UpperCamelCase__				)
    return hparams, config






"""simple docstring"""




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """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:
									lowerCamelCase			      =					sd.pop(snake_case__				)

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

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					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
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

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

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

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


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



