




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase       :       List[Any]        =    logging.get_logger(__name__)

lowerCamelCase       :       List[Any]        =    {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 						__lowercase		(__snake_case							):




      """simple docstring"""





      _snake_case							     =						"""dpt"""

      def __init__(      self				,      A=7_6_8				,      A=1_2				,      A=1_2				,      A=3_0_7_2				,      A="gelu"				,      A=0.0				,      A=0.0				,      A=0.02				,      A=1e-1_2				,      A=3_8_4				,      A=1_6				,      A=3				,      A=False				,      A=True				,      A=[2, 5, 8, 1_1]				,      A="project"				,      A=[4, 2, 1, 0.5]				,      A=[9_6, 1_9_2, 3_8_4, 7_6_8]				,      A=2_5_6				,      A=-1				,      A=False				,      A=True				,      A=0.4				,      A=2_5_5				,      A=0.1				,      A=[1, 1_0_2_4, 2_4, 2_4]				,      A=[0, 1]				,      A=None				,      **A				,      )		->				Tuple:
         super().__init__(**UpperCamelCase__	)

         snake_case      :				Dict				=       hidden_size
         snake_case      :				Optional[Any]				=       is_hybrid

         if self.is_hybrid:
            if backbone_config is None:
               logger.info("""Initializing the config with a `BiT` backbone."""	)
               snake_case      :				List[Any]				=       {
                   "global_padding": "same",
                   "layer_type": "bottleneck",
                   "depths": [3, 4, 9],
                   "out_features": ["stage1", "stage2", "stage3"],
                   "embedding_dynamic_padding": True,
               }
               snake_case      :				Any				=       BitConfig(**UpperCamelCase__	)
            elif isinstance(UpperCamelCase__				,      UpperCamelCase__	):
               logger.info("""Initializing the config with a `BiT` backbone."""	)
               snake_case      :				Union[str, Any]				=       BitConfig(**UpperCamelCase__	)
            elif isinstance(UpperCamelCase__				,      UpperCamelCase__	):
               snake_case      :				List[Any]				=       backbone_config
            else:
               raise ValueError(
                   f"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""	)

            snake_case      :				str				=       backbone_featmap_shape
            snake_case      :				Optional[int]				=       neck_ignore_stages

            if readout_type != "project":
               raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""	)
         else:
            snake_case      :				int				=       None
            snake_case      :				Union[str, Any]				=       None
            snake_case      :				str				=       []

         snake_case      :				str				=       num_hidden_layers
         snake_case      :				Tuple				=       num_attention_heads
         snake_case      :				Optional[Any]				=       intermediate_size
         snake_case      :				Optional[Any]				=       hidden_act
         snake_case      :				List[str]				=       hidden_dropout_prob
         snake_case      :				List[str]				=       attention_probs_dropout_prob
         snake_case      :				Optional[int]				=       initializer_range
         snake_case      :				Tuple				=       layer_norm_eps
         snake_case      :				List[Any]				=       image_size
         snake_case      :				List[Any]				=       patch_size
         snake_case      :				List[Any]				=       num_channels
         snake_case      :				Any				=       qkv_bias
         snake_case      :				Any				=       backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
            raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""	)
         snake_case      :				List[Any]				=       readout_type
         snake_case      :				Dict				=       reassemble_factors
         snake_case      :				int				=       neck_hidden_sizes
         snake_case      :				Optional[Any]				=       fusion_hidden_size
         snake_case      :				int				=       head_in_index
         snake_case      :				List[str]				=       use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         snake_case      :				str				=       use_auxiliary_head
         snake_case      :				List[Any]				=       auxiliary_loss_weight
         snake_case      :				Optional[int]				=       semantic_loss_ignore_index
         snake_case      :				List[Any]				=       semantic_classifier_dropout

      def 	UpperCAmelCase      (      self	)		->				List[str]:
         snake_case      :				Tuple				=       copy.deepcopy(self.__dict__	)

         if output["backbone_config"] is not None:
            snake_case      :				Optional[int]				=       self.backbone_config.to_dict()

         snake_case      :				Dict				=       self.__class__.model_type
         return output








"""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

__A						   =			logging.get_logger(__name__)


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

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





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      str         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  BlenderbotTokenizer

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



						'''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__     ,  )

						__lowerCAmelCase:       Any          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       int          =			add_prefix_space
								__lowerCAmelCase:       str          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			add_prefix_space

						__lowerCAmelCase:       Union[str, Any]          =			"post_processor"
						__lowerCAmelCase:       Tuple          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						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:       str          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       Any          =			tuple(state["cls"])

								__lowerCAmelCase:       int          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Dict          =			add_prefix_space
										__lowerCAmelCase:       Optional[Any]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Dict          =			trim_offsets
										__lowerCAmelCase:       Tuple          =			True

								if changes_to_apply:
										__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       str          =			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 							lowercase_     (	self							:				Optional[Any])->   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 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       Any          =			value

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[int])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			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 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			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 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			[self.sep_token_id]
						__lowerCAmelCase:       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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[Any]:



						'''simple docstring'''


						return token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				"Conversation")->   List[int]:



						'''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(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"  ".join(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.encode(UpperCamelCase__)
						if len(UpperCamelCase__) > self.model_max_length:
								__lowerCAmelCase:       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


