





import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
					from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
					UpperCamelCase__           =			True
					from torch.cuda.amp import autocast

UpperCamelCase__           =			logging.getLogger(__name__)





def      _UpperCamelCase  (a__ :int=None ,      a__ :Optional[Any]=None							):






							"""simple docstring"""





							return field(default_factory=lambda: default ,      metadata=lowerCAmelCase__							)

@dataclass
class 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   str      =							field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
							snake_case  :   Optional[str]      =							field(
							    default=_UpperCamelCase ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,		)
							snake_case  :   Optional[bool]      =							field(
							    default=_UpperCamelCase ,		metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}							)
							snake_case  :   Optional[float]      =							field(
							    default=0.1 ,		metadata={"""help""": """The dropout ratio for the attention probabilities."""}							)
							snake_case  :   Optional[float]      =							field(
							    default=0.1 ,		metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""}							)
							snake_case  :   Optional[float]      =							field(
							    default=0.1 ,		metadata={
							        """help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
							    } ,		)
							snake_case  :   Optional[float]      =							field(
							    default=0.1 ,		metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} ,		)
							snake_case  :   Optional[float]      =							field(
							    default=0.05 ,		metadata={
							        """help""": (
							            """Propability of each feature vector along the time axis to be chosen as the start of the vector"""
							            """span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
							            """vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
							        )
							    } ,		)
							snake_case  :   Optional[float]      =							field(default=0.0 ,		metadata={"""help""": """The LayerDrop probability."""}							)






@dataclass
class 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   Optional[str]      =							field(
							    default=_UpperCamelCase ,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
							snake_case  :   Optional[str]      =							field(
							    default="""train+validation""" ,		metadata={
							        """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
							    } ,		)
							snake_case  :   bool      =							field(
							    default=_UpperCamelCase ,		metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}							)
							snake_case  :   Optional[int]      =							field(
							    default=_UpperCamelCase ,		metadata={"""help""": """The number of processes to use for the preprocessing."""} ,		)
							snake_case  :   Optional[int]      =							field(
							    default=_UpperCamelCase ,		metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    } ,		)
							snake_case  :   Optional[int]      =							field(
							    default=_UpperCamelCase ,		metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of validation examples to this """
							            """value if set."""
							        )
							    } ,		)
							snake_case  :   List[str]      =							list_field(
							    default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] ,		metadata={"""help""": """A list of characters to remove from the transcripts."""} ,		)






@dataclass
class 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   WavaVecaProcessor
							snake_case  :   Union[bool, str]      =							True
							snake_case  :   Optional[int]      =							None
							snake_case  :   Optional[int]      =							None
							snake_case  :   Optional[int]      =							None
							snake_case  :   Optional[int]      =							None






							def __call__(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      [{"input_values": feature["input_values"]} for feature in features]
														UpperCamelCase__					  =      [{"input_ids": feature["labels"]} for feature in features]

														UpperCamelCase__					  =      self.processor.pad(
														    a_	,			padding=self.padding	,			max_length=self.max_length	,			pad_to_multiple_of=self.pad_to_multiple_of	,			return_tensors="""pt"""	,			)
														UpperCamelCase__					  =      self.processor.pad(
														    labels=a_	,			padding=self.padding	,			max_length=self.max_length_labels	,			pad_to_multiple_of=self.pad_to_multiple_of_labels	,			return_tensors="""pt"""	,			)

														# replace padding with -100 to ignore loss correctly
														UpperCamelCase__					  =      labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1	)	,			-100	)

														UpperCamelCase__					  =      labels

														return batch






class 							__SCREAMING_SNAKE_CASE       (  _UpperCamelCase							):






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														model.train()
														UpperCamelCase__					  =      self._prepare_inputs(a_	)

														if self.use_amp:
																					with autocast():
																												UpperCamelCase__					  =      self.compute_loss(a_	,			a_	)
														else:
																					UpperCamelCase__					  =      self.compute_loss(a_	,			a_	)

														if self.args.n_gpu > 1:
																					if model.module.config.ctc_loss_reduction == "mean":
																												UpperCamelCase__					  =      loss.mean()
																					elif model.module.config.ctc_loss_reduction == "sum":
																												UpperCamelCase__					  =      loss.sum() / (inputs["labels"] >= 0).sum()
																					else:
																												raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""	)

														if self.args.gradient_accumulation_steps > 1:
																					UpperCamelCase__					  =      loss / self.args.gradient_accumulation_steps

														if self.use_amp:
																					self.scaler.scale(a_	).backward()
														elif self.use_apex:
																					with amp.scale_loss(a_	,			self.optimizer	) as scaled_loss:
																												scaled_loss.backward()
														elif self.deepspeed:
																					self.deepspeed.backward(a_	)
														else:
																					loss.backward()

														return loss.detach()






def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
							if len(sys.argv							) == 2 and sys.argv[1].endswith(""".json"""							):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														UpperCamelCase__					  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
							else:
														UpperCamelCase__					  =      parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							UpperCamelCase__					  =      None
							if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
														UpperCamelCase__					  =      get_last_checkpoint(training_args.output_dir							)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
																					raise ValueError(
																					    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																					    """Use --overwrite_output_dir to overcome."""							)
														elif last_checkpoint is not None:
																					logger.info(
																					    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																					    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""							)

    # Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,      datefmt="""%m/%d/%Y %H:%M:%S""" ,      handlers=[logging.StreamHandler(sys.stdout							)] ,      )
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

							# Log on each process the small summary:
							logger.warning(
							    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
							    + f"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank							):
														transformers.utils.logging.set_verbosity_info()
							logger.info("""Training/evaluation parameters %s""" ,      lowerCAmelCase__							)

							# Set seed before initializing model.
							set_seed(training_args.seed							)

							# Get the datasets:
							UpperCamelCase__					  =      datasets.load_dataset(
							    """common_voice""" ,      data_args.dataset_config_name ,      split=data_args.train_split_name							)
							UpperCamelCase__					  =      datasets.load_dataset("""common_voice""" ,      data_args.dataset_config_name ,      split="""test"""							)

							# Create and save tokenizer
							UpperCamelCase__					  =      f"""[{"".join(data_args.chars_to_ignore							)}]"""

							def remove_special_characters(a__ :Tuple							):
														UpperCamelCase__					  =      re.sub(lowerCAmelCase__ ,      """""" ,      batch["""sentence"""]							).lower() + " "
														return batch

							UpperCamelCase__					  =      train_dataset.map(lowerCAmelCase__ ,      remove_columns=["""sentence"""]							)
							UpperCamelCase__					  =      eval_dataset.map(lowerCAmelCase__ ,      remove_columns=["""sentence"""]							)

							def extract_all_chars(a__ :Optional[int]							):
														UpperCamelCase__					  =      " ".join(batch["""text"""]							)
														UpperCamelCase__					  =      list(set(lowerCAmelCase__							)							)
														return {"vocab": [vocab], "all_text": [all_text]}

							UpperCamelCase__					  =      train_dataset.map(
							    lowerCAmelCase__ ,      batched=lowerCAmelCase__ ,      batch_size=-1 ,      keep_in_memory=lowerCAmelCase__ ,      remove_columns=train_dataset.column_names ,      )
							UpperCamelCase__					  =      train_dataset.map(
							    lowerCAmelCase__ ,      batched=lowerCAmelCase__ ,      batch_size=-1 ,      keep_in_memory=lowerCAmelCase__ ,      remove_columns=eval_dataset.column_names ,      )

							UpperCamelCase__					  =      list(set(vocab_train["""vocab"""][0]							) | set(vocab_test["""vocab"""][0]							)							)
							UpperCamelCase__					  =      {v: k for k, v in enumerate(lowerCAmelCase__							)}
							UpperCamelCase__					  =      vocab_dict[" "]
							del vocab_dict[" "]
							UpperCamelCase__					  =      len(lowerCAmelCase__							)
							UpperCamelCase__					  =      len(lowerCAmelCase__							)

							with open("""vocab.json""" ,      """w"""							) as vocab_file:
														json.dump(lowerCAmelCase__ ,      lowerCAmelCase__							)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							UpperCamelCase__					  =      WavaVecaCTCTokenizer(
							    """vocab.json""" ,      unk_token="""[UNK]""" ,      pad_token="""[PAD]""" ,      word_delimiter_token="""|""" ,      )
							UpperCamelCase__					  =      WavaVecaFeatureExtractor(
							    feature_size=1 ,      sampling_rate=1_6000 ,      padding_value=0.0 ,      do_normalize=lowerCAmelCase__ ,      return_attention_mask=lowerCAmelCase__							)
							UpperCamelCase__					  =      WavaVecaProcessor(feature_extractor=lowerCAmelCase__ ,      tokenizer=lowerCAmelCase__							)
							UpperCamelCase__					  =      WavaVecaForCTC.from_pretrained(
							    model_args.model_name_or_path ,      cache_dir=model_args.cache_dir ,      activation_dropout=model_args.activation_dropout ,      attention_dropout=model_args.attention_dropout ,      hidden_dropout=model_args.hidden_dropout ,      feat_proj_dropout=model_args.feat_proj_dropout ,      mask_time_prob=model_args.mask_time_prob ,      gradient_checkpointing=training_args.gradient_checkpointing ,      layerdrop=model_args.layerdrop ,      ctc_loss_reduction="""mean""" ,      pad_token_id=processor.tokenizer.pad_token_id ,      vocab_size=len(processor.tokenizer							) ,      )

							if data_args.max_train_samples is not None:
														UpperCamelCase__					  =      min(len(lowerCAmelCase__							) ,      data_args.max_train_samples							)
														UpperCamelCase__					  =      train_dataset.select(range(lowerCAmelCase__							)							)

							if data_args.max_val_samples is not None:
														UpperCamelCase__					  =      eval_dataset.select(range(data_args.max_val_samples							)							)

							UpperCamelCase__					  =      torchaudio.transforms.Resample(4_8000 ,      1_6000							)

							# Preprocessing the datasets.
							# We need to read the aduio files as arrays and tokenize the targets.
							def speech_file_to_array_fn(a__ :Dict							):
														UpperCamelCase__					  =      torchaudio.load(batch["""path"""]							)
														UpperCamelCase__					  =      resampler(lowerCAmelCase__							).squeeze().numpy()
														UpperCamelCase__					  =      1_6000
														UpperCamelCase__					  =      batch["text"]
														return batch

							UpperCamelCase__					  =      train_dataset.map(
							    lowerCAmelCase__ ,      remove_columns=train_dataset.column_names ,      num_proc=data_args.preprocessing_num_workers ,      )
							UpperCamelCase__					  =      eval_dataset.map(
							    lowerCAmelCase__ ,      remove_columns=eval_dataset.column_names ,      num_proc=data_args.preprocessing_num_workers ,      )

							def prepare_dataset(a__ :List[str]							):
														# check that all files have the correct sampling rate
														assert (
														    len(set(batch["""sampling_rate"""]							)							) == 1
														), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

														UpperCamelCase__					  =      processor(
														    audio=batch["""speech"""] ,      text=batch["""target_text"""] ,      sampling_rate=batch["""sampling_rate"""][0]							)
														batch.update(lowerCAmelCase__							)
														return batch

							UpperCamelCase__					  =      train_dataset.map(
							    lowerCAmelCase__ ,      remove_columns=train_dataset.column_names ,      batch_size=training_args.per_device_train_batch_size ,      batched=lowerCAmelCase__ ,      num_proc=data_args.preprocessing_num_workers ,      )
							UpperCamelCase__					  =      eval_dataset.map(
							    lowerCAmelCase__ ,      remove_columns=eval_dataset.column_names ,      batch_size=training_args.per_device_train_batch_size ,      batched=lowerCAmelCase__ ,      num_proc=data_args.preprocessing_num_workers ,      )

							# Metric
							UpperCamelCase__					  =      datasets.load_metric("""wer"""							)

							def compute_metrics(a__ :List[Any]							):
														UpperCamelCase__					  =      pred.predictions
														UpperCamelCase__					  =      np.argmax(lowerCAmelCase__ ,      axis=-1							)

														UpperCamelCase__					  =      processor.tokenizer.pad_token_id

														UpperCamelCase__					  =      processor.batch_decode(lowerCAmelCase__							)
														# we do not want to group tokens when computing the metrics
														UpperCamelCase__					  =      processor.batch_decode(pred.label_ids ,      group_tokens=lowerCAmelCase__							)

														UpperCamelCase__					  =      wer_metric.compute(predictions=lowerCAmelCase__ ,      references=lowerCAmelCase__							)

														return {"wer": wer}

							if model_args.freeze_feature_extractor:
														model.freeze_feature_extractor()

							# Data collator
							UpperCamelCase__					  =      DataCollatorCTCWithPadding(processor=lowerCAmelCase__ ,      padding=lowerCAmelCase__							)

							# Initialize our Trainer
							UpperCamelCase__					  =      CTCTrainer(
							    model=lowerCAmelCase__ ,      data_collator=lowerCAmelCase__ ,      args=lowerCAmelCase__ ,      compute_metrics=lowerCAmelCase__ ,      train_dataset=train_dataset if training_args.do_train else None ,      eval_dataset=eval_dataset if training_args.do_eval else None ,      tokenizer=processor.feature_extractor ,      )

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					UpperCamelCase__					  =      last_checkpoint
														elif os.path.isdir(model_args.model_name_or_path							):
																					UpperCamelCase__					  =      model_args.model_name_or_path
														else:
																					UpperCamelCase__					  =      None

														# Save the feature_extractor and the tokenizer
														if is_main_process(training_args.local_rank							):
																					processor.save_pretrained(training_args.output_dir							)

														UpperCamelCase__					  =      trainer.train(resume_from_checkpoint=lowerCAmelCase__							)
														trainer.save_model()

														UpperCamelCase__					  =      train_result.metrics
														UpperCamelCase__					  =      (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__							)
														)
														UpperCamelCase__					  =      min(lowerCAmelCase__ ,      len(lowerCAmelCase__							)							)

														trainer.log_metrics("""train""" ,      lowerCAmelCase__							)
														trainer.save_metrics("""train""" ,      lowerCAmelCase__							)
														trainer.save_state()

							# Evaluation
							UpperCamelCase__					  =      {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""							)
														UpperCamelCase__					  =      trainer.evaluate()
														UpperCamelCase__					  =      data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCAmelCase__							)
														UpperCamelCase__					  =      min(lowerCAmelCase__ ,      len(lowerCAmelCase__							)							)

														trainer.log_metrics("""eval""" ,      lowerCAmelCase__							)
														trainer.save_metrics("""eval""" ,      lowerCAmelCase__							)

							return results


if __name__ == "__main__":
					main()











"""simple docstring"""






import json
import os
from functools import lru_cache
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

__UpperCAmelCase						=			logging.get_logger(__name__)


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

__UpperCAmelCase						=			{
    '''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'''
    },
}

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






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowercase__ (					)    ->			Any:



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






def      lowercase__ (					lowerCAmelCase__   :			List[str]			)    ->			Any:



			'''simple docstring'''
			a__  :	Tuple		 =			set()
			a__  :	Union[str, Any]		 =			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						a__  :	Tuple		 =			char
			return pairs





class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	int				     =				VOCAB_FILES_NAMES
				__lowerCamelCase				:	List[Any]				     =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase				:	Optional[int]				     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase				:	List[str]				     =				["input_ids", "attention_mask"]
				def __init__(		self    :			str	, a_    :			Optional[Any]	, a_    :			Union[str, Any]	, a_    :			int="replace"	, a_    :			Optional[Any]="<s>"	, a_    :			Optional[Any]="</s>"	, a_    :			Any="</s>"	, a_    :			Dict="<s>"	, a_    :			Optional[int]="<unk>"	, a_    :			Optional[int]="<pad>"	, a_    :			Any="<mask>"	, a_    :			Optional[Any]=False	, **a_    :			str	, )	->	Tuple:



							'''simple docstring'''



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

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

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

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

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							a__  :	List[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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
				def       UpperCAmelCase      (		self    :			Any    )	->	Optional[int]:



							'''simple docstring'''



							return len(self.encoder    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							return dict(self.encoder	, **self.added_tokens_encoder    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			str    )	->	int:



							'''simple docstring'''



							if token in self.cache:
										return self.cache[token]
							a__  :	List[str]		 =			tuple(a_    )
							a__  :	Dict		 =			get_pairs(a_    )

							if not pairs:
										return token

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

													if word[i] == first and i < len(a_    ) - 1 and word[i + 1] == second:
																new_word.append(first + second    )
																i += 2
													else:
																new_word.append(word[i]    )
																i += 1
										a__  :	List[Any]		 =			tuple(a_    )
										a__  :	str		 =			new_word
										if len(a_    ) == 1:
													break
										else:
													a__  :	Optional[int]		 =			get_pairs(a_    )
							a__  :	Union[str, Any]		 =			" ".join(a_    )
							a__  :	str		 =			word
							return word
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			Union[str, Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	List[Any]		 =			[]
							for token in re.findall(self.pat	, a_    ):
										a__  :	Dict		 =			"".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(a_    ).split(" "    )    )
							return bpe_tokens
				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.encoder.get(a_	, self.encoder.get(self.unk_token    )    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			Optional[Any]    )	->	Optional[Any]:



							'''simple docstring'''



							return self.decoder.get(a_    )
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	str		 =			"".join(a_    )
							a__  :	List[str]		 =			bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"	, errors=self.errors    )
							return text
				def       UpperCAmelCase      (		self    :			Any	, a_    :			str	, a_    :			Optional[str] = None    )	->	Tuple[str]:



							'''simple docstring'''



							if not os.path.isdir(a_    ):
										logger.error(F"Vocabulary path ({save_directory}) should be a directory"    )
										return
							a__  :	Dict		 =			os.path.join(
							    a_	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
							a__  :	List[str]		 =			os.path.join(
							    a_	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

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

							a__  :	Dict		 =			0
							with open(a_	, "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 a_    : 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__  :	Optional[Any]		 =			token_index
													writer.write(" ".join(a_    ) + "\n"    )
													index += 1

							return vocab_file, merge_file
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None	, a_    :			bool = False    )	->	List[int]:



							'''simple docstring'''



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

							if token_ids_a is None:
										return [1] + ([0] * len(a_    )) + [1]
							return [1] + ([0] * len(a_    )) + [1, 1] + ([0] * len(a_    )) + [1]
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	List[int]:



							'''simple docstring'''



							a__  :	Dict		 =			[self.sep_token_id]
							a__  :	Tuple		 =			[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       UpperCAmelCase      (		self    :			Any	, a_    :			Optional[Any]	, a_    :			Union[str, Any]=False	, **a_    :			Dict    )	->	Dict:



							'''simple docstring'''



							a__  :	int		 =			kwargs.pop("add_prefix_space"	, self.add_prefix_space    )
							if (is_split_into_words or add_prefix_space) and (len(a_    ) > 0 and not text[0].isspace()):
										a__  :	int		 =			" " + text
							return (text, kwargs)
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	Optional[int]:



							'''simple docstring'''



							return token_ids_a + [self.eos_token_id]






				def       UpperCAmelCase      (		self    :			str	, a_    :			"Conversation"    )	->	List[int]:



							'''simple docstring'''



							a__  :	Union[str, 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(a_    )

							a__  :	Dict		 =			"  ".join(a_    )
							a__  :	List[str]		 =			self.encode(a_    )
							if len(a_    ) > self.model_max_length:
										a__  :	Union[str, Any]		 =			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