






'''simple docstring'''




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

lowerCAmelCase__          =						logging.getLogger(__name__)







@dataclass
class       __lowercase					:
	_lowerCamelCase              =  field(
	    default=128   ,				metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={
	        '''help''': (
	            '''Whether to pad all samples to `max_seq_length`. '''
	            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
	        )
	    }   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
	            '''value if set.'''
	        )
	    }   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
	            '''value if set.'''
	        )
	    }   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
	            '''value if set.'''
	        )
	    }   ,				)







@dataclass
class       __lowercase					:
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}    )
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Train language if it is different from the evaluation language.'''}    )
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}   ,				)
	_lowerCamelCase              =  field(
	    default='''main'''   ,				metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={
	        '''help''': (
	            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
	            '''with private models).'''
	        )
	    }   ,				)
	_lowerCamelCase              =  field(
	    default=__lowerCamelCase   ,				metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}   ,				)







def       __UpperCAmelCase						(  )							->     int:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		UpperCamelCase__				:					Dict					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_xnli'     ,      lowerCamelCase_)

		# 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)]     ,      )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		UpperCamelCase__				:					List[Any]					=					training_args.get_process_log_level()
		logger.setLevel(lowerCamelCase_)
		datasets.utils.logging.set_verbosity(lowerCamelCase_)
		transformers.utils.logging.set_verbosity(lowerCamelCase_)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# 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}')
		logger.info(f'Training/evaluation parameters {training_args}')

		# Detecting last checkpoint.
		UpperCamelCase__				:					List[str]					=					None
		if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
				UpperCamelCase__				:					int					=					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.')

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

		# In distributed training, the load_dataset function guarantees that only one local process can concurrently
		# download the dataset.
		# Downloading and loading xnli dataset from the hub.
		if training_args.do_train:
				if model_args.train_language is None:
						UpperCamelCase__				:					Any					=					load_dataset(
						    'xnli'     ,      model_args.language     ,      split='train'     ,      cache_dir=model_args.cache_dir     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
				else:
						UpperCamelCase__				:					str					=					load_dataset(
						    'xnli'     ,      model_args.train_language     ,      split='train'     ,      cache_dir=model_args.cache_dir     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
				UpperCamelCase__				:					str					=					train_dataset.features['label'].names

		if training_args.do_eval:
				UpperCamelCase__				:					Tuple					=					load_dataset(
				    'xnli'     ,      model_args.language     ,      split='validation'     ,      cache_dir=model_args.cache_dir     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
				UpperCamelCase__				:					Tuple					=					eval_dataset.features['label'].names

		if training_args.do_predict:
				UpperCamelCase__				:					Optional[int]					=					load_dataset(
				    'xnli'     ,      model_args.language     ,      split='test'     ,      cache_dir=model_args.cache_dir     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
				UpperCamelCase__				:					int					=					predict_dataset.features['label'].names

		# Labels
		UpperCamelCase__				:					Tuple					=					len(lowerCamelCase_)

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		UpperCamelCase__				:					List[Any]					=					AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,      num_labels=lowerCamelCase_     ,      idalabel={str(lowerCamelCase_): label for i, label in enumerate(lowerCamelCase_)}     ,      labelaid={label: i for i, label in enumerate(lowerCamelCase_)}     ,      finetuning_task='xnli'     ,      cache_dir=model_args.cache_dir     ,      revision=model_args.model_revision     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
		UpperCamelCase__				:					int					=					AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,      do_lower_case=model_args.do_lower_case     ,      cache_dir=model_args.cache_dir     ,      use_fast=model_args.use_fast_tokenizer     ,      revision=model_args.model_revision     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
		UpperCamelCase__				:					Dict					=					AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path     ,      from_tf=bool('.ckpt' in model_args.model_name_or_path)     ,      config=lowerCamelCase_     ,      cache_dir=model_args.cache_dir     ,      revision=model_args.model_revision     ,      use_auth_token=True if model_args.use_auth_token else None     ,      ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,      )

		# Preprocessing the datasets
		# Padding strategy
		if data_args.pad_to_max_length:
				UpperCamelCase__				:					Tuple					=					'max_length'
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				UpperCamelCase__				:					List[Any]					=					False

		def preprocess_function(lowerCamelCase_):
				# Tokenize the texts
				return tokenizer(
				    examples['premise']     ,      examples['hypothesis']     ,      padding=lowerCamelCase_     ,      max_length=data_args.max_seq_length     ,      truncation=lowerCamelCase_     ,      )

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						UpperCamelCase__				:					List[str]					=					min(len(lowerCamelCase_)     ,      data_args.max_train_samples)
						UpperCamelCase__				:					List[str]					=					train_dataset.select(range(lowerCamelCase_))
				with training_args.main_process_first(desc='train dataset map pre-processing'):
						UpperCamelCase__				:					Tuple					=					train_dataset.map(
						    lowerCamelCase_     ,      batched=lowerCamelCase_     ,      load_from_cache_file=not data_args.overwrite_cache     ,      desc='Running tokenizer on train dataset'     ,      )
				# Log a few random samples from the training set:
				for index in random.sample(range(len(lowerCamelCase_))     ,      3):
						logger.info(f'Sample {index} of the training set: {train_dataset[index]}.')

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						UpperCamelCase__				:					Dict					=					min(len(lowerCamelCase_)     ,      data_args.max_eval_samples)
						UpperCamelCase__				:					Any					=					eval_dataset.select(range(lowerCamelCase_))
				with training_args.main_process_first(desc='validation dataset map pre-processing'):
						UpperCamelCase__				:					str					=					eval_dataset.map(
						    lowerCamelCase_     ,      batched=lowerCamelCase_     ,      load_from_cache_file=not data_args.overwrite_cache     ,      desc='Running tokenizer on validation dataset'     ,      )

		if training_args.do_predict:
				if data_args.max_predict_samples is not None:
						UpperCamelCase__				:					Dict					=					min(len(lowerCamelCase_)     ,      data_args.max_predict_samples)
						UpperCamelCase__				:					Any					=					predict_dataset.select(range(lowerCamelCase_))
				with training_args.main_process_first(desc='prediction dataset map pre-processing'):
						UpperCamelCase__				:					Any					=					predict_dataset.map(
						    lowerCamelCase_     ,      batched=lowerCamelCase_     ,      load_from_cache_file=not data_args.overwrite_cache     ,      desc='Running tokenizer on prediction dataset'     ,      )

    # Get the metric function
		UpperCamelCase__				:					str					=					evaluate.load('xnli')

		# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(lowerCamelCase_):
				UpperCamelCase__				:					Tuple					=					p.predictions[0] if isinstance(p.predictions     ,      lowerCamelCase_) else p.predictions
				UpperCamelCase__				:					Any					=					np.argmax(lowerCamelCase_     ,      axis=1)
				return metric.compute(predictions=lowerCamelCase_     ,      references=p.label_ids)

		# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
		if data_args.pad_to_max_length:
				UpperCamelCase__				:					str					=					default_data_collator
		elif training_args.fpaa:
				UpperCamelCase__				:					Optional[Any]					=					DataCollatorWithPadding(lowerCamelCase_     ,      pad_to_multiple_of=8)
		else:
				UpperCamelCase__				:					int					=					None

		# Initialize our Trainer
		UpperCamelCase__				:					Any					=					Trainer(
		    model=lowerCamelCase_     ,      args=lowerCamelCase_     ,      train_dataset=train_dataset if training_args.do_train else None     ,      eval_dataset=eval_dataset if training_args.do_eval else None     ,      compute_metrics=lowerCamelCase_     ,      tokenizer=lowerCamelCase_     ,      data_collator=lowerCamelCase_     ,      )

		# Training
		if training_args.do_train:
				UpperCamelCase__				:					Optional[Any]					=					None
				if training_args.resume_from_checkpoint is not None:
						UpperCamelCase__				:					int					=					training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						UpperCamelCase__				:					Any					=					last_checkpoint
				UpperCamelCase__				:					Dict					=					trainer.train(resume_from_checkpoint=lowerCamelCase_)
				UpperCamelCase__				:					Optional[int]					=					train_result.metrics
				UpperCamelCase__				:					Optional[int]					=					(
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_)
				)
				UpperCamelCase__				:					str					=					min(lowerCamelCase_     ,      len(lowerCamelCase_))

				trainer.save_model()  # Saves the tokenizer too for easy upload

				trainer.log_metrics('train'     ,      lowerCamelCase_)
				trainer.save_metrics('train'     ,      lowerCamelCase_)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				logger.info('*** Evaluate ***')
				UpperCamelCase__				:					Optional[int]					=					trainer.evaluate(eval_dataset=lowerCamelCase_)

				UpperCamelCase__				:					str					=					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_)
				UpperCamelCase__				:					str					=					min(lowerCamelCase_     ,      len(lowerCamelCase_))

				trainer.log_metrics('eval'     ,      lowerCamelCase_)
				trainer.save_metrics('eval'     ,      lowerCamelCase_)

		# Prediction
		if training_args.do_predict:
				logger.info('*** Predict ***')
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					trainer.predict(lowerCamelCase_     ,      metric_key_prefix='predict')

				UpperCamelCase__				:					List[str]					=					(
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase_)
				)
				UpperCamelCase__				:					str					=					min(lowerCamelCase_     ,      len(lowerCamelCase_))

				trainer.log_metrics('predict'     ,      lowerCamelCase_)
				trainer.save_metrics('predict'     ,      lowerCamelCase_)

				UpperCamelCase__				:					Optional[int]					=					np.argmax(lowerCamelCase_     ,      axis=1)
				UpperCamelCase__				:					Optional[int]					=					os.path.join(training_args.output_dir     ,      'predictions.txt')
				if trainer.is_world_process_zero():
						with open(lowerCamelCase_     ,      'w') as writer:
								writer.write('index\tprediction\n')
								for index, item in enumerate(lowerCamelCase_):
										UpperCamelCase__				:					str					=					label_list[item]
										writer.write(f'{index}\t{item}\n')


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

lowerCAmelCase__          =						logging.get_logger(__name__)


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

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







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCAmelCase						(  )							->     Union[str, Any]:
		UpperCamelCase__				:					Optional[Any]					=					(
		    list(range(ord('!')     ,      ord('~') + 1)) + list(range(ord('¡')     ,      ord('¬') + 1)) + list(range(ord('®')     ,      ord('ÿ') + 1))
		)
		UpperCamelCase__				:					List[Any]					=					bs[:]
		UpperCamelCase__				:					Optional[int]					=					0
		for b in range(2**8):
				if b not in bs:
						bs.append(lowerCamelCase_)
						cs.append(2**8 + n)
						n += 1
		UpperCamelCase__				:					Union[str, Any]					=					[chr(lowerCamelCase_) for n in cs]
		return dict(zip(lowerCamelCase_     ,      lowerCamelCase_))







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Any					=					set()
		UpperCamelCase__				:					Dict					=					word[0]
		for char in word[1:]:
				pairs.add((prev_char, char))
				UpperCamelCase__				:					str					=					char
		return pairs







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase              =  ['''input_ids''', '''attention_mask''']

	def __init__(      self   :						Tuple  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Dict="replace"  ,					UpperCAmelCase_   :						int="<s>"  ,					UpperCAmelCase_   :						Tuple="</s>"  ,					UpperCAmelCase_   :						Any="</s>"  ,					UpperCAmelCase_   :						List[Any]="<s>"  ,					UpperCAmelCase_   :						List[str]="<unk>"  ,					UpperCAmelCase_   :						Any="<pad>"  ,					UpperCAmelCase_   :						Optional[Any]="<mask>"  ,					UpperCAmelCase_   :						str=False  ,					**UpperCAmelCase_   :						List[Any]  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else bos_token
			UpperCamelCase__				:					List[str]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else eos_token
			UpperCamelCase__				:					Optional[Any]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else sep_token
			UpperCamelCase__				:					int					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else cls_token
			UpperCamelCase__				:					Tuple					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else unk_token
			UpperCamelCase__				:					Optional[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
			UpperCamelCase__				:					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:
					UpperCamelCase__				:					Any					=					json.load(UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					{v: k for k, v in self.encoder.items()}
			UpperCamelCase__				:					Any					=					errors  # how to handle errors in decoding
			UpperCamelCase__				:					Tuple					=					bytes_to_unicode()
			UpperCamelCase__				:					Union[str, Any]					=					{v: k for k, v in self.byte_encoder.items()}
			with open(UpperCAmelCase_  ,					encoding='utf-8') as merges_handle:
					UpperCamelCase__				:					List[Any]					=					merges_handle.read().split('\n')[1:-1]
			UpperCamelCase__				:					List[Any]					=					[tuple(merge.split()) for merge in bpe_merges]
			UpperCamelCase__				:					Any					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					Dict					=					{}
			UpperCamelCase__				:					Dict					=					add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			UpperCamelCase__				:					Any					=					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   :						Tuple):
			return len(self.encoder)

	def      __UpperCamelCase							(      self   :						Tuple):
			return dict(self.encoder  ,					**self.added_tokens_encoder)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Union[str, Any]):
			if token in self.cache:
					return self.cache[token]
			UpperCamelCase__				:					Optional[int]					=					tuple(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					get_pairs(UpperCAmelCase_)

			if not pairs:
					return token

			while True:
					UpperCamelCase__				:					Tuple					=					min(UpperCAmelCase_  ,					key=lambda UpperCAmelCase_: self.bpe_ranks.get(UpperCAmelCase_  ,					float('inf')))
					if bigram not in self.bpe_ranks:
							break
					UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					bigram
					UpperCamelCase__				:					Dict					=					[]
					UpperCamelCase__				:					Optional[int]					=					0
					while i < len(UpperCAmelCase_):
							try:
									UpperCamelCase__				:					Tuple					=					word.index(UpperCAmelCase_  ,					UpperCAmelCase_)
							except ValueError:
									new_word.extend(word[i:])
									break
							else:
									new_word.extend(word[i:j])
									UpperCamelCase__				:					Any					=					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
					UpperCamelCase__				:					List[str]					=					tuple(UpperCAmelCase_)
					UpperCamelCase__				:					Dict					=					new_word
					if len(UpperCAmelCase_) == 1:
							break
					else:
							UpperCamelCase__				:					Optional[int]					=					get_pairs(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[Any]					=					' '.join(UpperCAmelCase_)
			UpperCamelCase__				:					List[Any]					=					word
			return word

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					[]
			for token in re.findall(self.pat  ,					UpperCAmelCase_):
					UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Optional[Any]):
			return self.encoder.get(UpperCAmelCase_  ,					self.encoder.get(self.unk_token))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						Optional[int]):
			return self.decoder.get(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					int					=					''.join(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'  ,					errors=self.errors)
			return text

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
			if not os.path.isdir(UpperCAmelCase_):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory')
					return
			UpperCamelCase__				:					str					=					os.path.join(
			    UpperCAmelCase_  ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					Optional[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')

			UpperCamelCase__				:					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!')
									UpperCamelCase__				:					List[Any]					=					token_index
							writer.write(' '.join(UpperCAmelCase_) + '\n')
							index += 1

			return vocab_file, merge_file

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None  ,					UpperCAmelCase_   :						bool = False):
			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      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			UpperCamelCase__				:					Any					=					[self.sep_token_id]
			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						str=False  ,					**UpperCAmelCase_   :						Optional[Any]):
			UpperCamelCase__				:					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()):
					UpperCamelCase__				:					str					=					' ' + text
			return (text, kwargs)

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			return token_ids_a + [self.eos_token_id]





	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						"Conversation"):
			UpperCamelCase__				:					List[str]					=					[]
			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(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'  '.join(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					self.encode(UpperCAmelCase_)
			if len(UpperCAmelCase_) > self.model_max_length:
					UpperCamelCase__				:					Optional[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




