






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCamelCase	:							int				=logging.get_logger(__name__)

lowerCamelCase	:							List[str]				={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowerCamelCase	:							Union[str, Any]				=[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     for attribute in key.split("."			):
          UpperCamelCase__			:   Optional[int]       				=  getattr(__lowerCAmelCase					,    __lowerCAmelCase			)

     if weight_type is not None:
          UpperCamelCase__			:   Optional[int]       				=  getattr(__lowerCAmelCase					,    __lowerCAmelCase			).shape
     else:
          UpperCamelCase__			:   Any       				=  hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
              f' {value.shape} for {full_name}'			)

     if weight_type == "weight":
          UpperCamelCase__			:   Dict       				=  value
     elif weight_type == "weight_g":
          UpperCamelCase__			:   List[str]       				=  value
     elif weight_type == "weight_v":
          UpperCamelCase__			:   Any       				=  value
     elif weight_type == "bias":
          UpperCamelCase__			:   List[str]       				=  value
     else:
          UpperCamelCase__			:   int       				=  value

     logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Optional[Any]:
     UpperCamelCase__			:   int       				=  []
     UpperCamelCase__			:   List[Any]       				=  fairseq_model.state_dict()

     UpperCamelCase__			:   Union[str, Any]       				=  hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCamelCase__			:   Union[str, Any]       				=  False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    hf_model.config.feat_extract_norm == "group"					,    )
               UpperCamelCase__			:   List[str]       				=  True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCamelCase__			:   Dict       				=  "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split("."			)[:-1]			) != key):
                              # special case since naming is very similar
                              continue
                         UpperCamelCase__			:   List[str]       				=  True
                         if "*" in mapped_key:
                              UpperCamelCase__			:   Dict       				=  name.split(__lowerCAmelCase			)[0].split("."			)[-2]
                              UpperCamelCase__			:   Optional[int]       				=  mapped_key.replace("*"					,    __lowerCAmelCase			)
                         if "weight_g" in name:
                              UpperCamelCase__			:   Optional[Any]       				=  "weight_g"
                         elif "weight_v" in name:
                              UpperCamelCase__			:   Any       				=  "weight_v"
                         elif "bias" in name:
                              UpperCamelCase__			:   int       				=  "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              UpperCamelCase__			:   Tuple       				=  "weight"
                         else:
                              UpperCamelCase__			:   List[Any]       				=  None
                         set_recursively(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
                    continue
          if not is_used:
               unused_weights.append(__lowerCAmelCase			)

     logger.warning(f'Unused weights: {unused_weights}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Dict       				=  full_name.split("conv_layers."			)[-1]
     UpperCamelCase__			:   List[str]       				=  name.split("."			)
     UpperCamelCase__			:   List[Any]       				=  int(items[0]			)
     UpperCamelCase__			:   Optional[Any]       				=  int(items[1]			)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'			)
               UpperCamelCase__			:   Any       				=  value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'			)
               UpperCamelCase__			:   Dict       				=  value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'			)
               UpperCamelCase__			:   str       				=  value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'			)
               UpperCamelCase__			:   List[Any]       				=  value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
     else:
          unused_weights.append(__lowerCAmelCase			)







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase=None					,    __lowerCAmelCase=None					,    __lowerCAmelCase=True			) ->	Optional[Any]:
     if config_path is not None:
          UpperCamelCase__			:   str       				=  UniSpeechSatConfig.from_pretrained(__lowerCAmelCase			)
     else:
          UpperCamelCase__			:   List[str]       				=  UniSpeechSatConfig()

     UpperCamelCase__			:   Optional[Any]       				=  ""

     if is_finetuned:
          UpperCamelCase__			:   str       				=  UniSpeechSatForCTC(__lowerCAmelCase			)
     else:
          UpperCamelCase__			:   Optional[Any]       				=  UniSpeechSatForPreTraining(__lowerCAmelCase			)

     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]					,    arg_overrides={"data": "/".join(dict_path.split("/"			)[:-1]			)}			)
     UpperCamelCase__			:   Union[str, Any]       				=  model[0].eval()

     recursively_load_weights(__lowerCAmelCase					,    __lowerCAmelCase			)

     hf_wavavec.save_pretrained(__lowerCAmelCase			)


if __name__ == "__main__":
 lowerCamelCase	:							int				=argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 lowerCamelCase	:							Dict				=parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowerCamelCase	:							Union[str, Any]				=logging.get_logger(__name__)


lowerCamelCase	:							str				={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase	:							str				={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCamelCase	:							str				={'''facebook/blenderbot-3B''': 128}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Union[str, Any]        = VOCAB_FILES_NAMES
 _lowerCAmelCase       :       Dict        = PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase       :       List[Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase       :       int        = ['''input_ids''', '''attention_mask''']
 _lowerCAmelCase       :       Tuple        = BlenderbotTokenizer







 def __init__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Dict=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None							,   SCREAMING_SNAKE_CASE					:   Tuple="replace"							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]="<s>"							,   SCREAMING_SNAKE_CASE					:   str="</s>"							,   SCREAMING_SNAKE_CASE					:   str="</s>"							,   SCREAMING_SNAKE_CASE					:   Tuple="<s>"							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]="<unk>"							,   SCREAMING_SNAKE_CASE					:   str="<pad>"							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]="<mask>"							,   SCREAMING_SNAKE_CASE					:   Any=False							,   SCREAMING_SNAKE_CASE					:   List[Any]=True							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      super().__init__(
          SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   tokenizer_file=SCREAMING_SNAKE_CASE							,   errors=SCREAMING_SNAKE_CASE							,   bos_token=SCREAMING_SNAKE_CASE							,   eos_token=SCREAMING_SNAKE_CASE							,   sep_token=SCREAMING_SNAKE_CASE							,   cls_token=SCREAMING_SNAKE_CASE							,   unk_token=SCREAMING_SNAKE_CASE							,   pad_token=SCREAMING_SNAKE_CASE							,   mask_token=SCREAMING_SNAKE_CASE							,   add_prefix_space=SCREAMING_SNAKE_CASE							,   trim_offsets=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )

      UpperCamelCase__			:   List[Any]       				=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
      if pre_tok_state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
           UpperCamelCase__			:   str       				=  getattr(SCREAMING_SNAKE_CASE							,   pre_tok_state.pop("type"						)						)
           UpperCamelCase__			:   Union[str, Any]       				=  add_prefix_space
           UpperCamelCase__			:   Union[str, Any]       				=  pre_tok_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  add_prefix_space

      UpperCamelCase__			:   Optional[int]       				=  "post_processor"
      UpperCamelCase__			:   Union[str, Any]       				=  getattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      if tokenizer_component_instance:
           UpperCamelCase__			:   Tuple       				=  json.loads(tokenizer_component_instance.__getstate__()						)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                UpperCamelCase__			:   Tuple       				=  tuple(state["sep"]						)
           if "cls" in state:
                UpperCamelCase__			:   Optional[int]       				=  tuple(state["cls"]						)

           UpperCamelCase__			:   List[Any]       				=  False

           if state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
                UpperCamelCase__			:   str       				=  add_prefix_space
                UpperCamelCase__			:   Optional[Any]       				=  True

           if state.get("trim_offsets"							,   SCREAMING_SNAKE_CASE						) != trim_offsets:
                UpperCamelCase__			:   Optional[Any]       				=  trim_offsets
                UpperCamelCase__			:   Tuple       				=  True

           if changes_to_apply:
                UpperCamelCase__			:   Optional[int]       				=  getattr(SCREAMING_SNAKE_CASE							,   state.pop("type"						)						)
                UpperCamelCase__			:   Dict       				=  component_class(**SCREAMING_SNAKE_CASE						)
                setattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      if self._mask_token is None:
           if self.verbose:
                logger.error("Using mask_token, but it is not set yet."						)
           return None
      return str(self._mask_token						)







 @mask_token.setter
 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  AddedToken(SCREAMING_SNAKE_CASE							,   lstrip=SCREAMING_SNAKE_CASE							,   rstrip=SCREAMING_SNAKE_CASE						) if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) else value
      UpperCamelCase__			:   str       				=  value







 def 					__lowercase					(		self					:   Any							,   *SCREAMING_SNAKE_CASE					:   List[str]							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)
      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   *SCREAMING_SNAKE_CASE					:   int							,   **SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)

      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[str] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE							,   name=SCREAMING_SNAKE_CASE						)
      return tuple(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  [self.sep_token_id]
      UpperCamelCase__			:   int       				=  [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 + sep + token_ids_a + sep						) * [0]







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      return token_ids_a + [self.eos_token_id]







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   "Conversation"						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  []
      for is_user, text in conversation.iter_texts():
           if is_user:
                # We need to space prefix as it's being done within blenderbot
                inputs.append(" " + text						)
           else:
                # Generated responses should contain them already.
                inputs.append(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  "  ".join(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  self.encode(SCREAMING_SNAKE_CASE						)
      if len(SCREAMING_SNAKE_CASE						) > self.model_max_length:
           UpperCamelCase__			:   Dict       				=  input_ids[-self.model_max_length :]
           logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'						)
      return input_ids