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

_UpperCAmelCase            =		logging.get_logger(__name__)


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

_UpperCAmelCase            =		{
    """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"""
    },
}

_UpperCAmelCase            =		{"""facebook/blenderbot-3B""": 128}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				lowerCamelCase_    = BlenderbotTokenizer
				def __init__(						self		,     lowercase=None		,     lowercase=None		,     lowercase=None		,     lowercase="replace"		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=False		,     lowercase=True		,     **lowercase		,     ):



						"""simple docstring"""





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

						A_		:    List[Any]         =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
						if pre_tok_state.get('add_prefix_space'		,     lowercase ) != add_prefix_space:
								A_		:    Tuple         =				getattr(lowercase		,     pre_tok_state.pop('type' ) )
								A_		:    List[str]         =				add_prefix_space
								A_		:    Dict         =				pre_tok_class(**lowercase )

						A_		:    Dict         =				add_prefix_space

						A_		:    Dict         =				'post_processor'
						A_		:    Optional[Any]         =				getattr(self.backend_tokenizer		,     lowercase		,     lowercase )
						if tokenizer_component_instance:
								A_		:    Tuple         =				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_		:    str         =				tuple(state['sep'] )
								if "cls" in state:
										A_		:    Optional[Any]         =				tuple(state['cls'] )

								A_		:    Tuple         =				False

								if state.get('add_prefix_space'		,     lowercase ) != add_prefix_space:
										A_		:    Optional[Any]         =				add_prefix_space
										A_		:    List[Any]         =				True

								if state.get('trim_offsets'		,     lowercase ) != trim_offsets:
										A_		:    Optional[int]         =				trim_offsets
										A_		:    Tuple         =				True

								if changes_to_apply:
										A_		:    List[Any]         =				getattr(lowercase		,     state.pop('type' ) )
										A_		:    List[str]         =				component_class(**lowercase )
										setattr(self.backend_tokenizer		,     lowercase		,     lowercase )
				@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		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else value
						A_		:    Optional[int]         =				value
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				kwargs.get('is_split_into_words'		,     lowercase )
						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(*lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				kwargs.get('is_split_into_words'		,     lowercase )

						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(*lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    str         =				self._tokenizer.model.save(lowercase		,     name=lowercase )
						return tuple(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				[self.sep_token_id]
						A_		:    Tuple         =				[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		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						return token_ids_a + [self.eos_token_id]







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				[]
						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(lowercase )

						A_		:    Any         =				'  '.join(lowercase )
						A_		:    int         =				self.encode(lowercase )
						if len(lowercase ) > self.model_max_length:
								A_		:    Union[str, Any]         =				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 copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCAmelCase            =		{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''mask2former'''
				lowerCamelCase_    = ['''swin''']
				lowerCamelCase_    = {'''hidden_size''': '''hidden_dim'''}
				def __init__(						self		,     lowercase = None		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 1_0_2_4		,     lowercase = "relu"		,     lowercase = 6		,     lowercase = 1_0		,     lowercase = 8		,     lowercase = 0.0		,     lowercase = 2_0_4_8		,     lowercase = False		,     lowercase = False		,     lowercase = 4		,     lowercase = 2_5_5		,     lowercase = 1_0_0		,     lowercase = 0.1		,     lowercase = 2.0		,     lowercase = 5.0		,     lowercase = 5.0		,     lowercase = 1_2_5_4_4		,     lowercase = 3.0		,     lowercase = 0.75		,     lowercase = 0.02		,     lowercase = 1.0		,     lowercase = True		,     lowercase = [4, 8, 1_6, 3_2]		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
								A_		:    List[str]         =				CONFIG_MAPPING['swin'](
								    image_size=2_2_4		,     in_channels=3		,     patch_size=4		,     embed_dim=9_6		,     depths=[2, 2, 1_8, 2]		,     num_heads=[3, 6, 1_2, 2_4]		,     window_size=7		,     drop_path_rate=0.3		,     use_absolute_embeddings=lowercase		,     out_features=['stage1', 'stage2', 'stage3', 'stage4']		,     )

						if isinstance(lowercase		,     lowercase ):
								A_		:    str         =				backbone_config.pop('model_type' )
								A_		:    List[str]         =				CONFIG_MAPPING[backbone_model_type]
								A_		:    Tuple         =				config_class.from_dict(lowercase )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
								logger.warning_once(
								    F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
								    F'''Supported model types: {','.join(self.backbones_supported )}''' )

						A_		:    List[Any]         =				backbone_config
						A_		:    Optional[Any]         =				feature_size
						A_		:    int         =				mask_feature_size
						A_		:    Tuple         =				hidden_dim
						A_		:    Dict         =				encoder_feedforward_dim
						A_		:    int         =				activation_function
						A_		:    str         =				encoder_layers
						A_		:    Tuple         =				decoder_layers
						A_		:    Tuple         =				num_attention_heads
						A_		:    str         =				dropout
						A_		:    List[str]         =				dim_feedforward
						A_		:    List[str]         =				pre_norm
						A_		:    Tuple         =				enforce_input_projection
						A_		:    Dict         =				common_stride
						A_		:    Union[str, Any]         =				ignore_value
						A_		:    List[Any]         =				num_queries
						A_		:    List[Any]         =				no_object_weight
						A_		:    int         =				class_weight
						A_		:    int         =				mask_weight
						A_		:    Optional[Any]         =				dice_weight
						A_		:    int         =				train_num_points
						A_		:    Optional[int]         =				oversample_ratio
						A_		:    Tuple         =				importance_sample_ratio
						A_		:    Union[str, Any]         =				init_std
						A_		:    List[Any]         =				init_xavier_std
						A_		:    Optional[Any]         =				use_auxiliary_loss
						A_		:    Dict         =				feature_strides
						A_		:    List[Any]         =				output_auxiliary_logits
						A_		:    Optional[int]         =				decoder_layers

						super().__init__(**lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						return cls(
						    backbone_config=lowercase		,     **lowercase		,     )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				copy.deepcopy(self.__dict__ )
						A_		:    Optional[int]         =				self.backbone_config.to_dict()
						A_		:    Dict         =				self.__class__.model_type
						return output






