






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

from tokenizers import pre_tokenizers, processors

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

lowercase  							=					logging.get_logger(__name__)


lowercase  							=					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase  							=					{
    "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"
    },
}

lowercase  							=					{"facebook/blenderbot-3B": 128}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= ['''input_ids''', '''attention_mask''']
	lowerCAmelCase													= BlenderbotTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=None       ,			a="replace"       ,			a="<s>"       ,			a="</s>"       ,			a="</s>"       ,			a="<s>"       ,			a="<unk>"       ,			a="<pad>"       ,			a="<mask>"       ,			a=False       ,			a=True       ,			**a       ,			)       ->    int:
								super().__init__(
								    a       ,			a       ,			tokenizer_file=a       ,			errors=a       ,			bos_token=a       ,			eos_token=a       ,			sep_token=a       ,			cls_token=a       ,			unk_token=a       ,			pad_token=a       ,			mask_token=a       ,			add_prefix_space=a       ,			trim_offsets=a       ,			**a       ,			)

								snake_case_				=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
								if pre_tok_state.get('add_prefix_space'       ,			a					) != add_prefix_space:
															snake_case_				=							getattr(a       ,			pre_tok_state.pop('type'					)					)
															snake_case_				=							add_prefix_space
															snake_case_				=							pre_tok_class(**a					)

								snake_case_				=							add_prefix_space

								snake_case_				=							'post_processor'
								snake_case_				=							getattr(self.backend_tokenizer       ,			a       ,			a					)
								if tokenizer_component_instance:
															snake_case_				=							json.loads(tokenizer_component_instance.__getstate__()					)

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

															snake_case_				=							False

															if state.get('add_prefix_space'       ,			a					) != add_prefix_space:
																						snake_case_				=							add_prefix_space
																						snake_case_				=							True

															if state.get('trim_offsets'       ,			a					) != trim_offsets:
																						snake_case_				=							trim_offsets
																						snake_case_				=							True

															if changes_to_apply:
																						snake_case_				=							getattr(a       ,			state.pop('type'					)					)
																						snake_case_				=							component_class(**a					)
																						setattr(self.backend_tokenizer       ,			a       ,			a					)




	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def 							_UpperCamelCase			(       self					)       ->    str:
								if self._mask_token is None:
															if self.verbose:
																						logger.error('Using mask_token, but it is not set yet.'					)
															return None
								return str(self._mask_token					)




	@mask_token.setter
	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else value
								snake_case_				=							value




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    BatchEncoding:
								snake_case_				=							kwargs.get('is_split_into_words'       ,			a					)
								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

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




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    BatchEncoding:
								snake_case_				=							kwargs.get('is_split_into_words'       ,			a					)

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

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




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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       ,			a       ,			a = None					)       ->    Union[str, Any]:
								return token_ids_a + [self.eos_token_id]






	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[int]:
								snake_case_				=							[]
								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					)

								snake_case_				=							'  '.join(a					)
								snake_case_				=							self.encode(a					)
								if len(a					) > self.model_max_length:
															snake_case_				=							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













import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowercase  							=					logging.getLogger(__name__)




@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=0.0       ,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''whether to use adafactor'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default='''linear'''       ,					metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}       ,					)





