
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__							:							List[str]													=	logging.get_logger(__name__)


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

lowercase__							:							List[Any]													=	{
    "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__							:							Optional[int]													=	{"facebook/blenderbot-3B": 1_2_8}






class 				SCREAMING_SNAKE_CASE__			(							SCREAMING_SNAKE_CASE_			):

							"""simple docstring"""






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



							def __init__(     self   ,		SCREAMING_SNAKE_CASE_=None   ,		SCREAMING_SNAKE_CASE_=None   ,		SCREAMING_SNAKE_CASE_=None   ,		SCREAMING_SNAKE_CASE_="replace"   ,		SCREAMING_SNAKE_CASE_="<s>"   ,		SCREAMING_SNAKE_CASE_="</s>"   ,		SCREAMING_SNAKE_CASE_="</s>"   ,		SCREAMING_SNAKE_CASE_="<s>"   ,		SCREAMING_SNAKE_CASE_="<unk>"   ,		SCREAMING_SNAKE_CASE_="<pad>"   ,		SCREAMING_SNAKE_CASE_="<mask>"   ,		SCREAMING_SNAKE_CASE_=False   ,		SCREAMING_SNAKE_CASE_=True   ,		**SCREAMING_SNAKE_CASE_   ,		)->      Union[str, Any]:





													'''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   ,		)

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

													__UpperCamelCase        =	add_prefix_space

													__UpperCamelCase        =	'post_processor'
													__UpperCamelCase        =	getattr(self.backend_tokenizer   ,		__UpperCAmelCase   ,		__UpperCAmelCase	)
													if tokenizer_component_instance:
																			__UpperCamelCase        =	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:
																									__UpperCamelCase        =	tuple(state['''sep''']	)
																			if "cls" in state:
																									__UpperCamelCase        =	tuple(state['''cls''']	)

																			__UpperCamelCase        =	False

																			if state.get('''add_prefix_space'''   ,		__UpperCAmelCase	) != add_prefix_space:
																									__UpperCamelCase        =	add_prefix_space
																									__UpperCamelCase        =	True

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

																			if changes_to_apply:
																									__UpperCamelCase        =	getattr(__UpperCAmelCase   ,		state.pop('''type'''	)	)
																									__UpperCamelCase        =	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 			A__						(     self	)->      Union[str, Any]:





													'''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 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      List[str]:





													'''simple docstring'''






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



							def 			A__						(     self   ,		*SCREAMING_SNAKE_CASE_   ,		**SCREAMING_SNAKE_CASE_	)->      Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase        =	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 			A__						(     self   ,		*SCREAMING_SNAKE_CASE_   ,		**SCREAMING_SNAKE_CASE_	)->      List[Any]:





													'''simple docstring'''






													__UpperCamelCase        =	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 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None	)->      List[Any]:





													'''simple docstring'''






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



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None	)->      Optional[int]:





													'''simple docstring'''






													__UpperCamelCase        =	[self.sep_token_id]
													__UpperCamelCase        =	[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   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None	)->      List[Any]:





													'''simple docstring'''






													return token_ids_a + [self.eos_token_id]



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Any:





													'''simple docstring'''






													__UpperCamelCase        =	[]
													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        =	'  '.join(__UpperCAmelCase	)
													__UpperCamelCase        =	self.encode(__UpperCAmelCase	)
													if len(__UpperCAmelCase	) > self.model_max_length:
																			__UpperCamelCase        =	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











"""simple docstring"""





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
  import torch






@require_torch
@require_optimum
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

         self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							model.reverse_bettertransformer()

         self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

           self.assertFalse(
               any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__UpperCAmelCase      ):
             model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
