


'''simple docstring'''



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    :    Optional[Any]			     =  logging.get_logger(__name__)


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

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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   int							= VOCAB_FILES_NAMES
				__magic_name__       :   List[Any]							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Any							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Any							= ["input_ids", "attention_mask"]
				__magic_name__       :   Tuple							= BlenderbotTokenizer




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

										"""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   ,					)

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

										UpperCAmelCase         =			add_prefix_space

										UpperCAmelCase         =			'''post_processor'''
										UpperCAmelCase         =			getattr(self.backend_tokenizer   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										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'''   ,					lowerCAmelCase   ) != add_prefix_space:
																						UpperCAmelCase         =			add_prefix_space
																						UpperCAmelCase         =			True

																if state.get('''trim_offsets'''   ,					lowerCAmelCase   ) != trim_offsets:
																						UpperCAmelCase         =			trim_offsets
																						UpperCAmelCase         =			True

																if changes_to_apply:
																						UpperCAmelCase         =			getattr(lowerCAmelCase   ,					state.pop('''type'''   )   )
																						UpperCAmelCase         =			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   a__(	self    :				int   )->  str:

										"""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    :				Union[str, Any]   ,					lowerCAmelCase    :				Dict   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			AddedToken(lowerCAmelCase   ,					lstrip=lowerCAmelCase   ,					rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) else value
										UpperCAmelCase         =			value




				def   a__(	self    :				str   ,					*lowerCAmelCase    :				Optional[Any]   ,					**lowerCAmelCase    :				Dict   )->  BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase         =			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   a__(	self    :				Tuple   ,					*lowerCAmelCase    :				Dict   ,					**lowerCAmelCase    :				Any   )->  BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase         =			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   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str] = None   )->  Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self._tokenizer.model.save(lowerCAmelCase   ,					name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[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    :				List[Any]   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[Any]:

										"""simple docstring"""




										return token_ids_a + [self.eos_token_id]





				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				"Conversation"   )->  List[int]:

										"""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(lowerCAmelCase   )

										UpperCAmelCase         =			'''  '''.join(lowerCAmelCase   )
										UpperCAmelCase         =			self.encode(lowerCAmelCase   )
										if len(lowerCAmelCase   ) > 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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





