






'''simple docstring'''



import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE						=	logging.get_logger('transformers.models.speecht5')

SCREAMING_SNAKE_CASE						=	{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
SCREAMING_SNAKE_CASE						=	{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
SCREAMING_SNAKE_CASE						=	{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
SCREAMING_SNAKE_CASE						=	{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
SCREAMING_SNAKE_CASE						=	{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
SCREAMING_SNAKE_CASE						=	{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
SCREAMING_SNAKE_CASE						=	{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
SCREAMING_SNAKE_CASE						=	{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
SCREAMING_SNAKE_CASE						=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE						=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE						=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE						=	[]
SCREAMING_SNAKE_CASE						=	[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
SCREAMING_SNAKE_CASE						=	IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
SCREAMING_SNAKE_CASE						=	IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
SCREAMING_SNAKE_CASE						=	IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]


def 							lowercase_				(    __A :       Any					,      __A :       List[str]					,      __A :       str					,      __A :       Optional[Any]					,      __A :       Optional[Any]			)       ->      str:



 """simple docstring"""




 for attribute in key.split('''.'''			):
  lowercase					:   Optional[Any]				  =getattr(a_					,      a_			)

 if weight_type is not None:
  lowercase					:   List[str]				  =getattr(a_					,      a_			).shape
 else:
  lowercase					:   List[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":
  lowercase					:   Optional[int]				  =value
 elif weight_type == "weight_g":
  lowercase					:   Union[str, Any]				  =value
 elif weight_type == "weight_v":
  lowercase					:   Any				  =value
 elif weight_type == "bias":
  lowercase					:   Tuple				  =value
 elif weight_type == "running_mean":
  lowercase					:   int				  =value
 elif weight_type == "running_var":
  lowercase					:   str				  =value
 elif weight_type == "num_batches_tracked":
  lowercase					:   Optional[Any]				  =value
 else:
  lowercase					:   List[Any]				  =value

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


def 							lowercase_				(    __A :       str					,      __A :       Optional[int]			)       ->      int:



 """simple docstring"""




 for key in ignore_keys:
  if key.endswith('''.*'''			):
   if name.startswith(key[:-1]			):
    return True
  elif ".*." in key:
   lowercase					:   Optional[Any]				  =key.split('''.*.'''			)
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False


def 							lowercase_				(    __A :       str					,      __A :       str					,      __A :       Union[str, Any]			)       ->      Dict:



 """simple docstring"""




 lowercase					:   List[Any]				  =[]

 if task == "s2t":
  lowercase					:   Optional[int]				  =hf_model.speechta.encoder.prenet.feature_encoder
  lowercase					:   int				  =MAPPING_S2T
  lowercase					:   List[str]				  =IGNORE_KEYS_S2T
 elif task == "t2s":
  lowercase					:   int				  =None
  lowercase					:   List[Any]				  =MAPPING_T2S
  lowercase					:   List[Any]				  =IGNORE_KEYS_T2S
 elif task == "s2s":
  lowercase					:   Dict				  =hf_model.speechta.encoder.prenet.feature_encoder
  lowercase					:   int				  =MAPPING_S2S
  lowercase					:   Dict				  =IGNORE_KEYS_S2S
 else:
  raise ValueError(F'Unsupported task: {task}'			)

 for name, value in fairseq_dict.items():
  if should_ignore(a_					,      a_			):
   logger.info(F'{name} was ignored'			)
   continue

  lowercase					:   int				  =False
  if "conv_layers" in name:
   load_conv_layer(
       a_					,      a_					,      a_					,      a_					,      hf_model.config.feat_extract_norm == '''group'''					,      )
   lowercase					:   str				  =True
  else:
   for key, mapped_key in MAPPING.items():
    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

    if "*" in key:
     lowercase					:   List[Any]				  =key.split('''.*.'''			)
     if prefix in name and suffix in name:
      lowercase					:   List[Any]				  =suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     lowercase					:   List[Any]				  =True
     if "*" in mapped_key:
      lowercase					:   Union[str, Any]				  =name.split(a_			)[0].split('''.'''			)[-2]
      lowercase					:   Optional[Any]				  =mapped_key.replace('''*'''					,      a_			)
     if "weight_g" in name:
      lowercase					:   Dict				  ='''weight_g'''
     elif "weight_v" in name:
      lowercase					:   List[str]				  ='''weight_v'''
     elif "bias" in name:
      lowercase					:   List[Any]				  ='''bias'''
     elif "weight" in name:
      lowercase					:   List[Any]				  ='''weight'''
     elif "running_mean" in name:
      lowercase					:   Tuple				  ='''running_mean'''
     elif "running_var" in name:
      lowercase					:   Tuple				  ='''running_var'''
     elif "num_batches_tracked" in name:
      lowercase					:   Tuple				  ='''num_batches_tracked'''
     else:
      lowercase					:   Union[str, Any]				  =None
     set_recursively(a_					,      a_					,      a_					,      a_					,      a_			)
    continue
  if not is_used:
   unused_weights.append(a_			)

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


def 							lowercase_				(    __A :       int					,      __A :       str					,      __A :       Union[str, Any]					,      __A :       List[Any]					,      __A :       int			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   List[Any]				  =full_name.split('''conv_layers.'''			)[-1]
 lowercase					:   int				  =name.split('''.'''			)
 lowercase					:   Optional[Any]				  =int(items[0]			)
 lowercase					:   Union[str, 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.'			)
   lowercase					:   Optional[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.'			)
   lowercase					:   Optional[int]				  =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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'			)
   lowercase					:   Union[str, Any]				  =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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'			)
   lowercase					:   List[str]				  =value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
 else:
  unused_weights.append(a_			)


@torch.no_grad()
def 							lowercase_				(    __A :       str					,      __A :       str					,      __A :       Tuple					,      __A :       Union[str, Any]=None					,      __A :       Any=None					,      __A :       Union[str, Any]=None					,      )       ->      Optional[Any]:



 """simple docstring"""




 if config_path is not None:
  lowercase					:   int				  =SpeechTaConfig.from_pretrained(a_			)
 else:
  lowercase					:   List[str]				  =SpeechTaConfig()

 if task == "s2t":
  lowercase					:   Any				  =config.max_text_positions
  lowercase					:   Optional[Any]				  =SpeechTaForSpeechToText(a_			)
 elif task == "t2s":
  lowercase					:   Dict				  =1_8_7_6
  lowercase					:   str				  =6_0_0
  lowercase					:   str				  =config.max_speech_positions
  lowercase					:   List[Any]				  =SpeechTaForTextToSpeech(a_			)
 elif task == "s2s":
  lowercase					:   List[Any]				  =1_8_7_6
  lowercase					:   Optional[int]				  =config.max_speech_positions
  lowercase					:   Optional[int]				  =SpeechTaForSpeechToSpeech(a_			)
 else:
  raise ValueError(F'Unknown task name: {task}'			)

 if vocab_path:
  lowercase					:   Any				  =SpeechTaTokenizer(a_					,      model_max_length=config.max_text_positions			)

  # Mask token behaves like a normal word, i.e. include the space before it
  lowercase					:   Optional[Any]				  =AddedToken('''<mask>'''					,      lstrip=a_					,      rstrip=a_			)
  lowercase					:   int				  =mask_token
  tokenizer.add_special_tokens({'''mask_token''': mask_token}			)
  tokenizer.add_tokens(['''<ctc_blank>''']			)

 lowercase					:   int				  =SpeechTaFeatureExtractor()
 lowercase					:   Dict				  =SpeechTaProcessor(tokenizer=a_					,      feature_extractor=a_			)
 processor.save_pretrained(a_			)

 lowercase					:   str				  =torch.load(a_			)
 recursively_load_weights(fairseq_checkpoint['''model''']					,      a_					,      a_			)

 model.save_pretrained(a_			)

 if repo_id:
  print('''Pushing to the hub...'''			)
  processor.push_to_hub(a_			)
  model.push_to_hub(a_			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	argparse.ArgumentParser()
      parser.add_argument(
          '--task',
          default='s2t',
          type=str,
          help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
      )
      parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
      parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
      )

      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_speechta_checkpoint(
          args.task,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.vocab_path,
          args.push_to_hub,
      )











'''simple docstring'''



import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						=	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE						=	{
    'allenai/led-base-16384': 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 							lowercase_				(    )       ->      Any:



 """simple docstring"""




 lowercase					:   int				  =(
     list(range(ord('''!'''			)					,      ord('''~'''			) + 1			)			) + list(range(ord('''¡'''			)					,      ord('''¬'''			) + 1			)			) + list(range(ord('''®'''			)					,      ord('''ÿ'''			) + 1			)			)
 )
 lowercase					:   Union[str, Any]				  =bs[:]
 lowercase					:   Tuple				  =0
 for b in range(2**8			):
  if b not in bs:
   bs.append(__A			)
   cs.append(2**8 + n			)
   n += 1
 lowercase					:   Optional[Any]				  =[chr(__A			) for n in cs]
 return dict(zip(__A					,      __A			)			)


def 							lowercase_				(    __A :       str			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =set()
 lowercase					:   Tuple				  =word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)			)
  lowercase					:   List[str]				  =char
 return pairs





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			['''input_ids''', '''attention_mask''']






      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		str="replace"     ,      UpperCAmelCase			:		int="<s>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		List[Any]="<s>"     ,      UpperCAmelCase			:		str="<unk>"     ,      UpperCAmelCase			:		Dict="<pad>"     ,      UpperCAmelCase			:		Union[str, Any]="<mask>"     ,      UpperCAmelCase			:		str=False     ,      **UpperCAmelCase			:		int     ,      )  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else bos_token
       lowercase					:   Union[str, Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else eos_token
       lowercase					:   str				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else sep_token
       lowercase					:   Optional[int]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else cls_token
       lowercase					:   Union[str, Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else unk_token
       lowercase					:   List[Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else pad_token

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

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

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

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       lowercase					:   str				  =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.bart.tokenization_bart.BartTokenizer.vocab_size
      def 				A__      ( self			:		Union[str, Any]		)  ->  Optional[int]:


       '''simple docstring'''

       return len(self.encoder		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       return dict(self.encoder     ,      **self.added_tokens_encoder		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       if token in self.cache:
        return self.cache[token]
       lowercase					:   List[str]				  =tuple(UpperCAmelCase		)
       lowercase					:   List[str]				  =get_pairs(UpperCAmelCase		)

       if not pairs:
        return token

       while True:
        lowercase					:   Tuple				  =min(UpperCAmelCase     ,      key=lambda UpperCAmelCase		: self.bpe_ranks.get(UpperCAmelCase     ,      float('''inf'''		)		)		)
        if bigram not in self.bpe_ranks:
         break
        lowercase  ,		lowercase					:   Optional[int]				  =bigram
        lowercase					:   Union[str, Any]				  =[]
        lowercase					:   Optional[Any]				  =0
        while i < len(UpperCAmelCase		):
         try:
          lowercase					:   Dict				  =word.index(UpperCAmelCase     ,      UpperCAmelCase		)
         except ValueError:
          new_word.extend(word[i:]		)
          break
         else:
          new_word.extend(word[i:j]		)
          lowercase					:   Optional[int]				  =j

         if word[i] == first and i < len(UpperCAmelCase		) - 1 and word[i + 1] == second:
          new_word.append(first + second		)
          i += 2
         else:
          new_word.append(word[i]		)
          i += 1
        lowercase					:   List[str]				  =tuple(UpperCAmelCase		)
        lowercase					:   str				  =new_word
        if len(UpperCAmelCase		) == 1:
         break
        else:
         lowercase					:   Optional[Any]				  =get_pairs(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =''' '''.join(UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =word
       return word






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Dict				  =[]
       for token in re.findall(self.pat     ,      UpperCAmelCase		):
        lowercase					:   Optional[int]				  =''''''.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(UpperCAmelCase		).split(''' '''		)		)
       return bpe_tokens






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  List[str]:


       '''simple docstring'''

       return self.encoder.get(UpperCAmelCase     ,      self.encoder.get(self.unk_token		)		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


       '''simple docstring'''

       return self.decoder.get(UpperCAmelCase		)






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		List[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =''''''.join(UpperCAmelCase		)
       lowercase					:   Dict				  =bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''     ,      errors=self.errors		)
       return text






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Optional[str] = None		)  ->  Tuple[str]:


       '''simple docstring'''

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

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

       lowercase					:   List[str]				  =0
       with open(UpperCAmelCase     ,      '''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 UpperCAmelCase		: 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!'''		)
          lowercase					:   Any				  =token_index
         writer.write(''' '''.join(UpperCAmelCase		) + '''\n'''		)
         index += 1

       return vocab_file, merge_file






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase					:   Optional[int]				  =[self.cls_token_id]
       lowercase					:   List[Any]				  =[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None     ,      UpperCAmelCase			:		bool = False		)  ->  List[int]:


       '''simple docstring'''

       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=UpperCAmelCase     ,      token_ids_a=UpperCAmelCase     ,      already_has_special_tokens=UpperCAmelCase		)

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






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       lowercase					:   Dict				  =[self.sep_token_id]
       lowercase					:   Optional[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 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]=False     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Tuple				  =kwargs.pop('''add_prefix_space'''     ,      self.add_prefix_space		)
       if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase		) > 0 and not text[0].isspace()):
        lowercase					:   Union[str, Any]				  =''' ''' + text
       return (text, kwargs)






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Union[Dict[str, EncodedInput], BatchEncoding]     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      )  ->  dict:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =super()._pad(
           encoded_inputs=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      padding_strategy=UpperCAmelCase     ,      pad_to_multiple_of=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      )

       # Load from model defaults
       if return_attention_mask is None:
        lowercase					:   Tuple				  ='''attention_mask''' in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        lowercase					:   Optional[Any]				  =encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        lowercase					:   str				  =len(encoded_inputs['''global_attention_mask''']		) != len(UpperCAmelCase		)

        if needs_to_be_padded:
         lowercase					:   Tuple				  =len(UpperCAmelCase		) - len(encoded_inputs['''global_attention_mask''']		)

         if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          lowercase					:   List[str]				  =(
              encoded_inputs['''global_attention_mask'''] + [-1] * difference
          )
         elif self.padding_side == "left":
          lowercase					:   Any				  =[-1] * difference + encoded_inputs[
              '''global_attention_mask'''
          ]
         else:
          raise ValueError('''Invalid padding strategy:''' + str(self.padding_side		)		)

       return encoded_inputs



