

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						List[Any]					=					[
          '''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 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						int					=					list(s_dict.keys()						)
      for key in keys:
            if "transformer_layers" in key:
                  __lowerCAmelCase					:						List[str]					=					s_dict.pop(lowercase__						)
            elif "subsample" in key:
                  __lowerCAmelCase					:						Any					=					s_dict.pop(lowercase__						)







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase,							__lowerCAmelCase					:						List[Any]					=					emb.weight.shape
      __lowerCAmelCase					:						int					=					nn.Linear(lowercase__   ,	lowercase__   ,	bias=lowercase__						)
      __lowerCAmelCase					:						str					=					emb.weight.data
      return lin_layer







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Optional[Any]					=					torch.load(lowercase__   ,	map_location='''cpu'''						)
      __lowerCAmelCase					:						int					=					mam_aaa['''args''']
      __lowerCAmelCase					:						Optional[Any]					=					mam_aaa['''model''']
      __lowerCAmelCase					:						List[str]					=					state_dict['''decoder.output_projection.weight''']

      remove_ignore_keys_(lowercase__						)
      rename_keys(lowercase__						)

      __lowerCAmelCase					:						Any					=					state_dict['''decoder.embed_tokens.weight'''].shape[0]

      __lowerCAmelCase					:						List[str]					=					args.share_decoder_input_output_embed

      __lowerCAmelCase					:						Any					=					[int(lowercase__						) for i in args.conv_kernel_sizes.split(''','''						)]
      __lowerCAmelCase					:						Union[str, Any]					=					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=2_0_0   ,	use_cache=lowercase__   ,	decoder_start_token_id=2   ,	early_stopping=lowercase__   ,	)

      __lowerCAmelCase					:						Tuple					=					SpeechaTextForConditionalGeneration(lowercase__						)
      __lowerCAmelCase,							__lowerCAmelCase					:						Tuple					=					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:
            __lowerCAmelCase					:						int					=					make_linear_from_emb(model.model.decoder.embed_tokens						)
      else:
            __lowerCAmelCase					:						str					=					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)





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_UpperCamelCase 			=		logging.get_logger(__name__)
_UpperCamelCase 			=		{"vocab_file": "spiece.model"}

_UpperCamelCase 			=		{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_UpperCamelCase 			=		{
    "AI-Sweden/gpt-sw3-126m": 2048,
    "AI-Sweden/gpt-sw3-350m": 2048,
    "AI-Sweden/gpt-sw3-1.6b": 2048,
    "AI-Sweden/gpt-sw3-6.7b": 2048,
    "AI-Sweden/gpt-sw3-20b": 2048,
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  VOCAB_FILES_NAMES
 _UpperCamelCase			  =  PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase			  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase			  =  ["""input_ids""", """attention_mask"""]
 def __init__(							self      ,							A_      ,							A_=False      ,							A_=False      ,							A_=False      ,							A_=None      ,							A_=None      ,							A_=None      ,							A_=None      ,							A_ = None      ,							**A_      ,							)					->None:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					{} if sp_model_kwargs is None else sp_model_kwargs

       __lowerCAmelCase					:						int					=					kwargs.get('''name_or_path'''					)
       if name_or_path is None:
             logger.warning(
                 '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
                 ''' you are testing the model, this can safely be ignored'''					)
             __lowerCAmelCase					:						Union[str, Any]					=					'''None'''

       # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
       __lowerCAmelCase					:						str					=					'''<|endoftext|>''' if eos_token is None else eos_token
       __lowerCAmelCase					:						Any					=					'''<unk>''' if unk_token is None else unk_token
       if "gpt-sw3-7b" in name_or_path:
             __lowerCAmelCase					:						Dict					=					unk_token if pad_token is None else pad_token
             __lowerCAmelCase					:						int					=					eos_token if bos_token is None else bos_token
       else:
             __lowerCAmelCase					:						Optional[int]					=					'''<pad>''' if pad_token is None else pad_token
             __lowerCAmelCase					:						List[str]					=					'''<s>''' if bos_token is None else bos_token

       super().__init__(
           do_lower_case=A_      ,							remove_space=A_      ,							keep_accents=A_      ,							bos_token=A_      ,							eos_token=A_      ,							unk_token=A_      ,							pad_token=A_      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**A_      ,							)

       __lowerCAmelCase					:						Union[str, Any]					=					do_lower_case
       __lowerCAmelCase					:						Union[str, Any]					=					remove_space
       __lowerCAmelCase					:						int					=					keep_accents
       __lowerCAmelCase					:						Union[str, Any]					=					vocab_file

       __lowerCAmelCase					:						int					=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.Load(A_					)

       # Used for whitespace normalization in input texts
       # fmt : off
       __lowerCAmelCase					:						List[Any]					=					{''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
       # fmt : on

       # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
       __lowerCAmelCase					:						int					=					re.compile(
           f"""[{"".join(map(A_      ,							list(range(0      ,							9					)					) + list(range(11      ,							32					)					) + list(range(127      ,							160					)					) + [160, 173, 8203]					)					)}]"""					)
 def __getstate__(							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					self.__dict__.copy()
       __lowerCAmelCase					:						List[Any]					=					None
       return state
 def __setstate__(							self      ,							A_					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					d

       # for backward compatibility
       if not hasattr(self      ,							'''sp_model_kwargs'''					):
             __lowerCAmelCase					:						List[Any]					=					{}

       __lowerCAmelCase					:						List[str]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.Load(self.vocab_file					)
 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return len(self.sp_model					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.non_printing_characters_re.sub(''''''      ,							A_					)

       # Normalize whitespaces
       __lowerCAmelCase					:						List[str]					=					''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]					)

       # NFC Unicode normalization
       __lowerCAmelCase					:						Tuple					=					unicodedata.normalize('''NFC'''      ,							A_					)
       return text
 def 	UpperCamelCase__  (							self      ,							A_      ,							**A_					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.preprocess_text(A_					)
       return self.sp_model.encode(A_      ,							out_type=A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->int:



       '''simple docstring'''





       return self.sp_model.PieceToId(A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       return self.sp_model.IdToPiece(A_					)
 @staticmethod
 def 	UpperCamelCase__  (							A_					)					->str:



       '''simple docstring'''





       return out_string
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					[]
       __lowerCAmelCase					:						Tuple					=					''''''
       __lowerCAmelCase					:						int					=					False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                   if not prev_is_special:
                         out_string += " "

                   out_string += self.sp_model.decode(A_					) + token
                   __lowerCAmelCase					:						Optional[Any]					=					True
                   __lowerCAmelCase					:						Optional[int]					=					[]
             else:
                   current_sub_tokens.append(A_					)
                   __lowerCAmelCase					:						str					=					False
       out_string += self.sp_model.decode(A_					)

       return out_string
 def 	UpperCamelCase__  (							self					)					->Dict[str, int]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
       vocab.update(self.added_tokens_encoder					)
       return vocab
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->Tuple[str]:



       '''simple docstring'''





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

       if os.path.abspath(self.vocab_file					) != os.path.abspath(A_					) and os.path.isfile(self.vocab_file					):
             copyfile(self.vocab_file      ,							A_					)
       elif not os.path.isfile(self.vocab_file					):
             with open(A_      ,							'''wb'''					) as fi:
                   __lowerCAmelCase					:						Dict					=					self.sp_model.serialized_model_proto()
                   fi.write(A_					)

       return (out_vocab_file,)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = False					)					->Union[List[int], List[List[int]], "torch.Tensor"]:



       '''simple docstring'''





       if isinstance(A_      ,							A_					):
             __lowerCAmelCase					:						Optional[Any]					=					self.preprocess_text(A_					)
             __lowerCAmelCase					:						Dict					=					self.sp_model.encode(A_					)
       else:
             __lowerCAmelCase					:						Dict					=					[self.preprocess_text(A_					) for t in text]
             __lowerCAmelCase					:						Optional[int]					=					self.sp_model.encode(A_					)

       if return_tensors is True or return_tensors == "pt":
             __lowerCAmelCase					:						Tuple					=					torch.tensor(A_					)

       return token_ids
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       return self.sp_model.decode(A_					)





 def 	UpperCamelCase__  (							self      ,							A_					)					->List[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					[f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
       __lowerCAmelCase					:						Any					=					(
           f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(A_					) + f"""{self.bos_token}Bot:"""
       )
       return self.encode(text=A_					)


