

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

A									=  logging.get_logger(__name__)


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

A									=  {
    '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'
    },
}

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


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	VOCAB_FILES_NAMES
			__A    	=	PRETRAINED_VOCAB_FILES_MAP
			__A    	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A    	=	["""input_ids""", """attention_mask"""]
			__A    	=	BlenderbotTokenizer


			def __init__(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase="replace"			,					__UpperCamelCase="<s>"			,					__UpperCamelCase="</s>"			,					__UpperCamelCase="</s>"			,					__UpperCamelCase="<s>"			,					__UpperCamelCase="<unk>"			,					__UpperCamelCase="<pad>"			,					__UpperCamelCase="<mask>"			,					__UpperCamelCase=False			,					__UpperCamelCase=True			,					**__UpperCamelCase			,					):

				"""simple docstring"""


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

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

				snake_case_   		=			add_prefix_space

				snake_case_   		=			'post_processor'
				snake_case_   		=			getattr(self.backend_tokenizer			,					__UpperCamelCase			,					__UpperCamelCase				)
				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'			,					__UpperCamelCase				) != add_prefix_space:
						snake_case_   		=			add_prefix_space
						snake_case_   		=			True

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

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


			@property
			# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			AddedToken(__UpperCamelCase			,					lstrip=__UpperCamelCase			,					rstrip=__UpperCamelCase				) if isinstance(__UpperCamelCase			,					__UpperCamelCase				) else value
				snake_case_   		=			value


			def       __lowerCAmelCase			(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			kwargs.get('is_split_into_words'			,					__UpperCamelCase				)
				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(*__UpperCamelCase			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			kwargs.get('is_split_into_words'			,					__UpperCamelCase				)

				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(*__UpperCamelCase			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				snake_case_   		=			self._tokenizer.model.save(__UpperCamelCase			,					name=__UpperCamelCase				)
				return tuple(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None				):

				"""simple docstring"""


				return token_ids_a + [self.eos_token_id]







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				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(__UpperCamelCase				)

				snake_case_   		=			'  '.join(__UpperCamelCase				)
				snake_case_   		=			self.encode(__UpperCamelCase				)
				if len(__UpperCamelCase				) > 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 unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script


@require_cpu
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				debug_launcher(test_script.main				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				debug_launcher(test_ops.main				)
