






"""simple docstring"""




import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {
    "post_extract_proj": "feature_projection.projection",
    "models.0.layer_norm": "feature_projection.layer_norm",
    "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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
lowerCamelCase__			=   [
    "lm_head",
]






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	int		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Tuple    ):
  for attribute in key.split("""."""    ):
    A__											=							getattr(UpperCamelCase		,				UpperCamelCase    )

  if weight_type is not None:
    A__											=							getattr(UpperCamelCase		,				UpperCamelCase    ).shape
  else:
    A__											=							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":
    A__											=							value
  elif weight_type == "weight_g":
    A__											=							value
  elif weight_type == "weight_v":
    A__											=							value
  elif weight_type == "bias":
    A__											=							value
  else:
    A__											=							value

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






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Any    ):
  A__											=							[]
  A__											=							fairseq_model.state_dict()

  if not is_headless:
    A__											=							hf_model.dataavec_audio.feature_extractor
    A__											=							hf_model.dataavec_audio.encoder.pos_conv_embed

  else:
    A__											=							hf_model.feature_extractor
    A__											=							hf_model.encoder.pos_conv_embed

  for name, value in fairseq_dict.items():
    A__											=							False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				)
      A__											=							True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				)
      A__											=							True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          A__											=							"""data2vec_audio.""" + 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]:
          A__											=							True
          if "*" in mapped_key:
            A__											=							name.split(UpperCamelCase    )[0].split("""."""    )[-2]
            A__											=							mapped_key.replace("""*"""		,				UpperCamelCase    )
          if "weight_g" in name:
            A__											=							"""weight_g"""
          elif "weight_v" in name:
            A__											=							"""weight_v"""
          elif "bias" in name:
            A__											=							"""bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            A__											=							"""weight"""
          else:
            A__											=							None
          set_recursively(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase    )
        continue
    if not is_used:
      unused_weights.append(UpperCamelCase    )

  logger.warning(F"""Unused weights: {unused_weights}"""    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple		,				UpperCamelCase     :	List[Any]    ):
  A__											=							path.split("""."""    )
  return reduce(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	List[str]    ):
  A__											=							access_by_string(UpperCamelCase		,				UpperCamelCase    )
  A__											=							hf_weight.data

  if fsq_value.shape != hf_value.shape:
    raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""    )
  A__											=							fsq_value
  logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	str    ):
  A__											=							full_name.split("""conv_layers."""    )[-1]
  A__											=							name.split("""."""    )
  A__											=							int(items[0]    )
  A__											=							int(items[1]    )

  A__											=							name.split("""."""    )[-1]
  if type_id == 0:
    A__											=							"""conv"""
  elif type_id == 2:
    A__											=							"""layer_norm"""
  else:
    unused_weights.append(UpperCamelCase    )
    return

  set_weights(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	str		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Tuple    ):
  A__											=							full_name.split("""pos_conv."""    )[-1]
  A__											=							name.split("""."""    )
  A__											=							int(items[0]    )
  A__											=							int(items[1]    )

  A__											=							name.split("""."""    )[-1]
  if type_id != 0:
    unused_weights.append(UpperCamelCase    )
    return
  else:
    A__											=							"""conv"""

  set_weights(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""    )






@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	str		,				UpperCamelCase     :	int=None		,				UpperCamelCase     :	List[str]=None		,				UpperCamelCase     :	str=True    ):
  if config_path is not None:
    A__											=							DataaVecAudioConfig.from_pretrained(UpperCamelCase    )
  else:
    A__											=							DataaVecAudioConfig()

  if not is_finetuned:
    # Modify final_proj layer name
    A__											=							DataaVecAudioModel(UpperCamelCase    )
    A__											=							os.path.dirname(UpperCamelCase    )

    A__											=							torch.load(UpperCamelCase    )
    A__											=							state_dict["""model"""].pop("""final_proj.0.weight"""    )
    A__											=							state_dict["""model"""].pop("""final_proj.0.bias"""    )
    A__											=							os.path.join(UpperCamelCase		,				"""converted.pt"""    )
    torch.save(UpperCamelCase		,				UpperCamelCase    )
  else:
    A__											=							DataaVecAudioForCTC(UpperCamelCase    )
    A__											=							checkpoint_path

  def load_dataavec(UpperCamelCase     :	List[str]    ):
    A__						,A__						,A__											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
    return model[0].eval()

  A__											=							load_dataavec(UpperCamelCase    )

  recursively_load_weights(UpperCamelCase		,				UpperCamelCase		,				not is_finetuned    )

  A__											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""    )

  A__											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""		,				"""clean"""		,				split="""validation"""    )
  A__											=							[x["""array"""] for x in ds[:4]["""audio"""]]

  A__											=							processor(UpperCamelCase		,				return_tensors="""pt"""		,				padding=UpperCamelCase    )

  A__											=							inputs.input_values
  A__											=							inputs.attention_mask
  #    input_values = inputs.input_values[:, :-1]
  #    attention_mask = inputs.attention_mask[:, :-1]

  hf_wavavec.eval()
  model.eval()
  if is_finetuned:
    A__											=							model(source=UpperCamelCase		,				padding_mask=(1 - attention_mask)		,				mask=UpperCamelCase		,				features_only=UpperCamelCase    )[
        """encoder_out"""
    ].transpose(0		,				1    )
    A__											=							hf_wavavec(UpperCamelCase		,				attention_mask=UpperCamelCase    )["""logits"""]

    A__											=							torch.argmax(UpperCamelCase		,				dim=-1    )
    A__											=							processor.batch_decode(UpperCamelCase    )

    print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""    )
  else:
    A__											=							model(source=UpperCamelCase		,				padding_mask=(1 - attention_mask)		,				mask=UpperCamelCase		,				features_only=UpperCamelCase    )[
        """layer_results"""
    ][-1][0].transpose(0		,				1    )
    A__											=							hf_wavavec(UpperCamelCase		,				attention_mask=UpperCamelCase    )["""last_hidden_state"""]

  print(our_output.shape		,				their_output.shape    )
  A__											=							torch.max(torch.abs(our_output - their_output    )    ).item()
  print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
  A__											=							torch.allclose(UpperCamelCase		,				UpperCamelCase		,				atol=1e-3    )
  print("""Do both models output the same tensors?"""		,				"""🔥""" if success else """💩"""    )
  if not success:
    raise Exception("""Something went wRoNg"""    )

  hf_wavavec.save_pretrained(UpperCamelCase    )

  if is_finetuned:
    processor.save_pretrained(UpperCamelCase    )
  else:
    processor.feature_extractor.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   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__			=   parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )












"""simple docstring"""




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

import regex as re

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


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

lowerCamelCase__			=   {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowerCamelCase__			=   {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowerCamelCase__			=   {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






def 						_SCREAMING_SNAKE_CASE							( ):
  A__											=							(
      list(range(ord("""!"""    )		,				ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )		,				ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )		,				ord("""ÿ"""    ) + 1    )    )
  )
  A__											=							bs[:]
  A__											=							0
  for b in range(2**8    ):
    if b not in bs:
      bs.append(UpperCamelCase    )
      cs.append(2**8 + n    )
      n += 1
  A__											=							[chr(UpperCamelCase    ) for n in cs]
  return dict(zip(UpperCamelCase		,				UpperCamelCase    )    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str    ):
  A__											=							set()
  A__											=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    A__											=							char
  return pairs







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  VOCAB_FILES_NAMES
    __lowerCamelCase         =  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase         =  ["input_ids", "attention_mask", "token_type_ids"]






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__="replace"						,      lowerCamelCase__="[CLS]"						,      lowerCamelCase__="[SEP]"						,      lowerCamelCase__="[SEP]"						,      lowerCamelCase__="[CLS]"						,      lowerCamelCase__="[UNK]"						,      lowerCamelCase__="[PAD]"						,      lowerCamelCase__="[MASK]"						,      lowerCamelCase__=False						,      lowerCamelCase__=False						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else bos_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else eos_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else sep_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else cls_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else unk_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else pad_token

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

      super().__init__(
          errors=lowerCamelCase__						,      bos_token=lowerCamelCase__						,      eos_token=lowerCamelCase__						,      unk_token=lowerCamelCase__						,      sep_token=lowerCamelCase__						,      cls_token=lowerCamelCase__						,      pad_token=lowerCamelCase__						,      mask_token=lowerCamelCase__						,      add_prefix_space=lowerCamelCase__						,      add_bos_token=lowerCamelCase__						,      **lowerCamelCase__						,      )
      A__											=							add_bos_token

      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as vocab_handle:
        A__											=							json.load(lowerCamelCase__   )
      A__											=							{v: k for k, v in self.encoder.items()}
      A__											=							errors  # how to handle errors in decoding
      A__											=							bytes_to_unicode()
      A__											=							{v: k for k, v in self.byte_encoder.items()}
      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as merges_handle:
        A__											=							merges_handle.read().split("""\n"""   )[1:-1]
      A__											=							[tuple(merge.split()   ) for merge in bpe_merges]
      A__											=							dict(zip(lowerCamelCase__						,      range(len(lowerCamelCase__   )   )   )   )
      A__											=							{}
      A__											=							add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      A__											=							re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )






    @property
    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
    def  A    (self   ):


      """simple docstring"""
      return len(self.encoder   )






    def  A    (self   ):


      """simple docstring"""
      return dict(self.encoder						,      **self.added_tokens_encoder   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      if token in self.cache:
        return self.cache[token]
      A__											=							tuple(lowerCamelCase__   )
      A__											=							get_pairs(lowerCamelCase__   )

      if not pairs:
        return token

      while True:
        A__											=							min(lowerCamelCase__						,      key=lambda lowerCamelCase__   : self.bpe_ranks.get(lowerCamelCase__						,      float("""inf"""   )   )   )
        if bigram not in self.bpe_ranks:
          break
        A__						,A__											=							bigram
        A__											=							[]
        A__											=							0
        while i < len(lowerCamelCase__   ):
          try:
            A__											=							word.index(lowerCamelCase__						,      lowerCamelCase__   )
          except ValueError:
            new_word.extend(word[i:]   )
            break
          else:
            new_word.extend(word[i:j]   )
            A__											=							j

          if word[i] == first and i < len(lowerCamelCase__   ) - 1 and word[i + 1] == second:
            new_word.append(first + second   )
            i += 2
          else:
            new_word.append(word[i]   )
            i += 1
        A__											=							tuple(lowerCamelCase__   )
        A__											=							new_word
        if len(lowerCamelCase__   ) == 1:
          break
        else:
          A__											=							get_pairs(lowerCamelCase__   )
      A__											=							""" """.join(lowerCamelCase__   )
      A__											=							word
      return word






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      A__											=							[self.cls_token_id]
      A__											=							[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = False   ):


      """simple docstring"""
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCamelCase__						,      token_ids_a=lowerCamelCase__						,      already_has_special_tokens=lowerCamelCase__   )

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






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      A__											=							[self.sep_token_id]
      A__											=							[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  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for token in re.findall(self.pat						,      lowerCamelCase__   ):
        A__											=							"""""".join(
            self.byte_encoder[b] for b in token.encode("""utf-8"""   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__   ).split(""" """   )   )
      return bpe_tokens






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.encoder.get(lowerCamelCase__						,      self.encoder.get(self.unk_token   )   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.decoder.get(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							"""""".join(lowerCamelCase__   )
      A__											=							bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""						,      errors=self.errors   )
      return text






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if not os.path.isdir(lowerCamelCase__   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

      with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""   ) as f:
        f.write(json.dumps(self.encoder						,      indent=2						,      sort_keys=lowerCamelCase__						,      ensure_ascii=lowerCamelCase__   ) + """\n"""   )

      A__											=							0
      with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""   ) as writer:
        writer.write("""#version: 0.2\n"""   )
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,      key=lambda lowerCamelCase__   : kv[1]   ):
          if index != token_index:
            logger.warning(
                F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                """ Please check that the tokenizer is not corrupted!"""   )
            A__											=							token_index
          writer.write(""" """.join(lowerCamelCase__   ) + """\n"""   )
          index += 1

      return vocab_file, merge_file






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=False						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							kwargs.pop("""add_prefix_space"""						,      self.add_prefix_space   )
      if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__   ) > 0 and not text[0].isspace()):
        A__											=							""" """ + text
      return (text, kwargs)






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(lowerCamelCase__						,      add_special_tokens=lowerCamelCase__   ) + [self.eos_token_id]   )
      if len(lowerCamelCase__   ) > self.model_max_length:
        A__											=							input_ids[-self.model_max_length :]
      return input_ids




