




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


a_   		=     logging.get_logger(__name__)

a_   		=     {"vocab_file": "vocab.txt"}

a_   		=     {
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

a_   		=     {
    "YituTech/conv-bert-base": 512,
    "YituTech/conv-bert-medium-small": 512,
    "YituTech/conv-bert-small": 512,
}


a_   		=     {
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					VOCAB_FILES_NAMES
      lowerCAmelCase__			: int    =					PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__			: int    =					PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__			: str    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__			: Any    =					ConvBertTokenizer







      def __init__(     self					: List[Any] ,       snake_case					: Optional[int]=None ,       snake_case					: Tuple=None ,       snake_case					: Tuple=True ,       snake_case					: Union[str, Any]="[UNK]" ,       snake_case					: List[Any]="[SEP]" ,       snake_case					: int="[PAD]" ,       snake_case					: Any="[CLS]" ,       snake_case					: Union[str, Any]="[MASK]" ,       snake_case					: List[Any]=True ,       snake_case					: Union[str, Any]=None ,       **snake_case					: int ,       ):
          super().__init__(
              snake_case ,       tokenizer_file=snake_case ,       do_lower_case=snake_case ,       unk_token=snake_case ,       sep_token=snake_case ,       pad_token=snake_case ,       cls_token=snake_case ,       mask_token=snake_case ,       tokenize_chinese_chars=snake_case ,       strip_accents=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
          if (
              normalizer_state.get('''lowercase''' ,       snake_case				) != do_lower_case
              or normalizer_state.get('''strip_accents''' ,       snake_case				) != strip_accents
              or normalizer_state.get('''handle_chinese_chars''' ,       snake_case				) != tokenize_chinese_chars
          ):
              __UpperCamelCase  		=						getattr(snake_case ,       normalizer_state.pop('''type'''				)				)
              __UpperCamelCase  		=						do_lower_case
              __UpperCamelCase  		=						strip_accents
              __UpperCamelCase  		=						tokenize_chinese_chars
              __UpperCamelCase  		=						normalizer_class(**snake_case				)

          __UpperCamelCase  		=						do_lower_case







      def       snake_case	(     self					: List[Any] ,       snake_case					: Dict ,       snake_case					: Optional[int]=None				):
          __UpperCamelCase  		=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output







      def       snake_case	(     self					: Any ,       snake_case					: List[int] ,       snake_case					: Optional[List[int]] = None				):
          __UpperCamelCase  		=						[self.sep_token_id]
          __UpperCamelCase  		=						[self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]







      def       snake_case	(     self					: int ,       snake_case					: str ,       snake_case					: Optional[str] = None				):
          __UpperCamelCase  		=						self._tokenizer.model.save(snake_case ,       name=snake_case				)
          return tuple(snake_case				)












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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     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(lowercase_      )

    __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(lowercase_      )

    __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(lowercase_					,     depth // 3					,     dim=0      )

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

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						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_   		=     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)






