




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

__lowerCAmelCase	:       str     =	logging.get_logger(__name__)


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

__lowerCAmelCase	:       Tuple     =	{
    '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	:       str     =	{'facebook/blenderbot-3B': 128}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							Dict											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_ :							Dict											=      BlenderbotTokenizer





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

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

							a					=    add_prefix_space

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

										a					=    False

										if state.get("add_prefix_space"      ,   __lowerCamelCase							) != add_prefix_space:
													a					=    add_prefix_space
													a					=    True

										if state.get("trim_offsets"      ,   __lowerCamelCase							) != trim_offsets:
													a					=    trim_offsets
													a					=    True

										if changes_to_apply:
													a					=    getattr(__lowerCamelCase      ,   state.pop("type"							)							)
													a					=    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 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  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       :  str      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Union[str, Any]:
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else value
							a					=    value





				def 							__UpperCAmelCase							(   self       :  Any      ,   *__lowerCamelCase       :  Union[str, Any]      ,   **__lowerCamelCase       :  List[str]							)      ->  BatchEncoding:
							a					=    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 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   *__lowerCamelCase       :  List[str]      ,   **__lowerCamelCase       :  Tuple							)      ->  BatchEncoding:
							a					=    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 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							a					=    self._tokenizer.model.save(__lowerCamelCase      ,   name=__lowerCamelCase							)
							return tuple(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  List[int]:
							a					=    [self.sep_token_id]
							a					=    [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       :  List[str]      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  str:
							return token_ids_a + [self.eos_token_id]





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  "Conversation"							)      ->  List[int]:
							a					=    []
							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							)

							a					=    "  ".join(__lowerCamelCase							)
							a					=    self.encode(__lowerCamelCase							)
							if len(__lowerCamelCase							) > self.model_max_length:
										a					=    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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






