


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      :    str		=					logging.get_logger(__name__)


A      :    Union[str, Any]		=					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A      :    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"
    },
}

A      :    List[str]		=					{"facebook/blenderbot-3B": 128}







class 			_lowercase (							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							:			Union[str, Any]					,      __lowerCamelCase							:			Dict=None					,      __lowerCamelCase							:			Union[str, Any]=None					,      __lowerCamelCase							:			Optional[int]=None					,      __lowerCamelCase							:			List[Any]="replace"					,      __lowerCamelCase							:			Optional[int]="<s>"					,      __lowerCamelCase							:			str="</s>"					,      __lowerCamelCase							:			Optional[Any]="</s>"					,      __lowerCamelCase							:			Union[str, Any]="<s>"					,      __lowerCamelCase							:			Any="<unk>"					,      __lowerCamelCase							:			Dict="<pad>"					,      __lowerCamelCase							:			Tuple="<mask>"					,      __lowerCamelCase							:			str=False					,      __lowerCamelCase							:			Optional[int]=True					,      **__lowerCamelCase							:			Optional[Any]					,      ):


             '''simple docstring'''



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

             lowerCamelCase__		:				str    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
             if pre_tok_state.get("add_prefix_space"					,      __lowerCamelCase       ) != add_prefix_space:
                   lowerCamelCase__		:				Optional[Any]    =  getattr(__lowerCamelCase					,      pre_tok_state.pop("type"       )       )
                   lowerCamelCase__		:				Dict    =  add_prefix_space
                   lowerCamelCase__		:				List[str]    =  pre_tok_class(**__lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  add_prefix_space

             lowerCamelCase__		:				int    =  "post_processor"
             lowerCamelCase__		:				Optional[Any]    =  getattr(self.backend_tokenizer					,      __lowerCamelCase					,      __lowerCamelCase       )
             if tokenizer_component_instance:
                   lowerCamelCase__		:				List[Any]    =  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:
                         lowerCamelCase__		:				Dict    =  tuple(state["sep"]       )
                   if "cls" in state:
                         lowerCamelCase__		:				Optional[Any]    =  tuple(state["cls"]       )

                   lowerCamelCase__		:				Dict    =  False

                   if state.get("add_prefix_space"					,      __lowerCamelCase       ) != add_prefix_space:
                         lowerCamelCase__		:				Dict    =  add_prefix_space
                         lowerCamelCase__		:				List[str]    =  True

                   if state.get("trim_offsets"					,      __lowerCamelCase       ) != trim_offsets:
                         lowerCamelCase__		:				int    =  trim_offsets
                         lowerCamelCase__		:				str    =  True

                   if changes_to_apply:
                         lowerCamelCase__		:				Optional[Any]    =  getattr(__lowerCamelCase					,      state.pop("type"       )       )
                         lowerCamelCase__		:				Optional[Any]    =  component_class(**__lowerCamelCase       )
                         setattr(self.backend_tokenizer					,      __lowerCamelCase					,      __lowerCamelCase       )





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


             '''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							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else value
             lowerCamelCase__		:				Optional[int]    =  value





       def 						lowerCAmelCase     (   self							:			Any					,      *__lowerCamelCase							:			Optional[int]					,      **__lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  kwargs.get("is_split_into_words"					,      __lowerCamelCase       )
             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(*__lowerCamelCase					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      *__lowerCamelCase							:			Optional[int]					,      **__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  kwargs.get("is_split_into_words"					,      __lowerCamelCase       )

             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(*__lowerCamelCase					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			List[Any] = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self._tokenizer.model.save(__lowerCamelCase					,      name=__lowerCamelCase       )
             return tuple(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Dict = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  [self.sep_token_id]
             lowerCamelCase__		:				Union[str, Any]    =  [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							:			int					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Optional[int] = None       ):


             '''simple docstring'''



             return token_ids_a + [self.eos_token_id]





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				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(__lowerCamelCase       )

             lowerCamelCase__		:				str    =  "  ".join(__lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  self.encode(__lowerCamelCase       )
             if len(__lowerCamelCase       ) > self.model_max_length:
                   lowerCamelCase__		:				Optional[int]    =  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

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_attention_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

								snake_case_				=							None
								if self.use_token_type_ids:
															snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size					)

								snake_case_				=							RobertaConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			vocab_size=2					)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






@require_flax
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





