




'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration





def      __a     (       _UpperCamelCase:   Any   )						->			Dict:

     """simple docstring"""




     _snake_case							=				[
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "decoder.output_projection.weight",
         "_float_tensor",
         "encoder.embed_positions._float_tensor",
         "decoder.embed_positions._float_tensor",
     ]
     for k in ignore_keys:
          state_dict.pop(_lowercase ,						_lowercase   )





def      __a     (       _UpperCamelCase:   str   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				list(s_dict.keys()   )
     for key in keys:
          if "transformer_layers" in key:
               _snake_case							=				s_dict.pop(_lowercase   )
          elif "subsample" in key:
               _snake_case							=				s_dict.pop(_lowercase   )





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				emb.weight.shape
     _snake_case							=				nn.Linear(_lowercase ,						_lowercase ,						bias=_lowercase   )
     _snake_case							=				emb.weight.data
     return lin_layer





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Union[str, Any]   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				torch.load(_lowercase ,						map_location="cpu"   )
     _snake_case							=				mam_aaa["args"]
     _snake_case							=				mam_aaa["model"]
     _snake_case							=				state_dict["decoder.output_projection.weight"]

     remove_ignore_keys_(_lowercase   )
     rename_keys(_lowercase   )

     _snake_case							=				state_dict["decoder.embed_tokens.weight"].shape[0]

     _snake_case							=				args.share_decoder_input_output_embed

     _snake_case							=				[int(_lowercase   ) for i in args.conv_kernel_sizes.split(","   )]
     _snake_case							=				SpeechaTextConfig(
         vocab_size=_lowercase ,						max_source_positions=args.max_source_positions ,						max_target_positions=args.max_target_positions ,						encoder_layers=args.encoder_layers ,						decoder_layers=args.decoder_layers ,						encoder_attention_heads=args.encoder_attention_heads ,						decoder_attention_heads=args.decoder_attention_heads ,						encoder_ffn_dim=args.encoder_ffn_embed_dim ,						decoder_ffn_dim=args.decoder_ffn_embed_dim ,						d_model=args.encoder_embed_dim ,						dropout=args.dropout ,						attention_dropout=args.attention_dropout ,						activation_dropout=args.activation_dropout ,						activation_function="relu" ,						num_conv_layers=len(_lowercase   ) ,						conv_channels=args.conv_channels ,						conv_kernel_sizes=_lowercase ,						input_feat_per_channel=args.input_feat_per_channel ,						input_channels=args.input_channels ,						tie_word_embeddings=_lowercase ,						num_beams=5 ,						max_length=200 ,						use_cache=_lowercase ,						decoder_start_token_id=2 ,						early_stopping=_lowercase ,						)

     _snake_case							=				SpeechaTextForConditionalGeneration(_lowercase   )
     _snake_case							=				model.model.load_state_dict(_lowercase ,						strict=_lowercase   )
     if len(_lowercase   ) > 0 and not set(_lowercase   ) <= {
         "encoder.embed_positions.weights",
         "decoder.embed_positions.weights",
     }:
          raise ValueError(
              "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
              F""" but all the following weights are missing {missing}"""   )

     if tie_embeds:
          _snake_case							=				make_linear_from_emb(model.model.decoder.embed_tokens   )
     else:
          _snake_case							=				lm_head_weights

     model.save_pretrained(_lowercase   )


if __name__ == "__main__":
   UpperCamelCase_       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   UpperCamelCase_       =						parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)








'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_big_bird import BigBirdTokenizer
else:
   UpperCamelCase_						:      List[Any]       =						None

UpperCamelCase_						:      Tuple       =						logging.get_logger(__name__)
UpperCamelCase_						:      Any       =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_						:      Any       =						{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_						:      Optional[int]       =						{
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}


UpperCamelCase_						:      List[str]       =						'''▁'''

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   Any       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   str       =						BigBirdTokenizer
     SCREAMING_SNAKE_CASE_							:   Tuple       =						["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_							:   List[int]       =						[]






     def __init__(			self ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="[SEP]" ,_SCREAMING_SNAKE_CASE="[MASK]" ,_SCREAMING_SNAKE_CASE="[CLS]" ,**_SCREAMING_SNAKE_CASE ,)							->      Dict:
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else bos_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else eos_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else unk_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else pad_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else cls_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else mask_token

          super().__init__(
              _SCREAMING_SNAKE_CASE ,tokenizer_file=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,cls_token=_SCREAMING_SNAKE_CASE ,mask_token=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)

          _snake_case							=				vocab_file
          _snake_case							=				False if not self.vocab_file else True






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False   )							->      List[int]:

          if already_has_special_tokens:
               if token_ids_a is not None:
                    raise ValueError(
                        "You should not supply a second sequence if the provided sequence of "
                        "ids is already formatted with special tokens for the model."   )
               return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is None:
               return [1] + ([0] * len(_SCREAMING_SNAKE_CASE   )) + [1]
          return [1] + ([0] * len(_SCREAMING_SNAKE_CASE   )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE   )) + [1]






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:
          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."   )

          if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(_SCREAMING_SNAKE_CASE   ):
               copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE   )

          return (out_vocab_file,)


