






"""simple docstring"""



import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


A__	:    Union[str, Any]			 = logging.get_logger(__name__)

A__	:    Tuple			 = {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Dict             =			"detr"
			_UpperCAmelCase		:str             =			["past_key_values"]
			_UpperCAmelCase		:List[Any]             =			{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(				self					:  Optional[Any]       ,	snake_case__					:  List[Any]=True       ,	snake_case__					:  str=None       ,	snake_case__					:  Optional[Any]=3       ,	snake_case__					:  Optional[Any]=100       ,	snake_case__					:  Union[str, Any]=6       ,	snake_case__					:  List[str]=2048       ,	snake_case__					:  Optional[Any]=8       ,	snake_case__					:  Optional[int]=6       ,	snake_case__					:  Any=2048       ,	snake_case__					:  Optional[int]=8       ,	snake_case__					:  Optional[Any]=0.0       ,	snake_case__					:  Optional[Any]=0.0       ,	snake_case__					:  Tuple=True       ,	snake_case__					:  str="relu"       ,	snake_case__					:  Union[str, Any]=256       ,	snake_case__					:  List[Any]=0.1       ,	snake_case__					:  Union[str, Any]=0.0       ,	snake_case__					:  Dict=0.0       ,	snake_case__					:  Any=0.02       ,	snake_case__					:  Optional[int]=1.0       ,	snake_case__					:  Tuple=False       ,	snake_case__					:  List[Any]="sine"       ,	snake_case__					:  List[str]="resnet50"       ,	snake_case__					:  Optional[Any]=True       ,	snake_case__					:  Any=False       ,	snake_case__					:  Optional[int]=1       ,	snake_case__					:  Union[str, Any]=5       ,	snake_case__					:  Tuple=2       ,	snake_case__					:  Optional[int]=1       ,	snake_case__					:  List[Any]=1       ,	snake_case__					:  Optional[int]=5       ,	snake_case__					:  List[str]=2       ,	snake_case__					:  Tuple=0.1       ,	**snake_case__					:  List[str]       ,	):
										if backbone_config is not None and use_timm_backbone:
																	raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )

										if not use_timm_backbone:
																	if backbone_config is None:
																								logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																								lowerCamelCase_	:							Any									=CONFIG_MAPPING["resnet"](out_features=["stage4"] )
																	elif isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							str									=backbone_config.get("model_type" )
																								lowerCamelCase_	:							Union[str, Any]									=CONFIG_MAPPING[backbone_model_type]
																								lowerCamelCase_	:							Dict									=config_class.from_dict(snake_case__ )
																	# set timm attributes to None
																	lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							str									=None, None, None

										lowerCamelCase_	:							Dict									=use_timm_backbone
										lowerCamelCase_	:							int									=backbone_config
										lowerCamelCase_	:							List[Any]									=num_channels
										lowerCamelCase_	:							Any									=num_queries
										lowerCamelCase_	:							List[str]									=d_model
										lowerCamelCase_	:							Dict									=encoder_ffn_dim
										lowerCamelCase_	:							Dict									=encoder_layers
										lowerCamelCase_	:							List[str]									=encoder_attention_heads
										lowerCamelCase_	:							str									=decoder_ffn_dim
										lowerCamelCase_	:							Optional[Any]									=decoder_layers
										lowerCamelCase_	:							str									=decoder_attention_heads
										lowerCamelCase_	:							str									=dropout
										lowerCamelCase_	:							Optional[int]									=attention_dropout
										lowerCamelCase_	:							Dict									=activation_dropout
										lowerCamelCase_	:							List[Any]									=activation_function
										lowerCamelCase_	:							List[str]									=init_std
										lowerCamelCase_	:							Optional[int]									=init_xavier_std
										lowerCamelCase_	:							Optional[int]									=encoder_layerdrop
										lowerCamelCase_	:							Optional[int]									=decoder_layerdrop
										lowerCamelCase_	:							Any									=encoder_layers
										lowerCamelCase_	:							Union[str, Any]									=auxiliary_loss
										lowerCamelCase_	:							Dict									=position_embedding_type
										lowerCamelCase_	:							str									=backbone
										lowerCamelCase_	:							Tuple									=use_pretrained_backbone
										lowerCamelCase_	:							Optional[int]									=dilation
										# Hungarian matcher
										lowerCamelCase_	:							Optional[Any]									=class_cost
										lowerCamelCase_	:							str									=bbox_cost
										lowerCamelCase_	:							Tuple									=giou_cost
										# Loss coefficients
										lowerCamelCase_	:							Union[str, Any]									=mask_loss_coefficient
										lowerCamelCase_	:							str									=dice_loss_coefficient
										lowerCamelCase_	:							Tuple									=bbox_loss_coefficient
										lowerCamelCase_	:							str									=giou_loss_coefficient
										lowerCamelCase_	:							Tuple									=eos_coefficient
										super().__init__(is_encoder_decoder=snake_case__       ,	**snake_case__ )


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return self.encoder_attention_heads


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										return self.d_model


			@classmethod
			def      UpperCAmelCase__						(				cls					:  List[Any]       ,	snake_case__					:  PretrainedConfig       ,	**snake_case__					:  Union[str, Any] ):
										return cls(backbone_config=snake_case__       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							str									=copy.deepcopy(self.__dict__ )
										if output["backbone_config"] is not None:
																	lowerCamelCase_	:							List[str]									=self.backbone_config.to_dict()
										lowerCamelCase_	:							List[str]									=self.__class__.model_type
										return output







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Dict             =			version.parse("1.11"       )


			@property
			def      UpperCAmelCase__						(				self					:  List[str] ):
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										        ("pixel_mask", {0: "batch"}),
										    ] )


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return 1E-5


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										return 12











"""simple docstring"""



from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


A__	:    Dict			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A__	:    Optional[int]			 = {
    'facebook/blenderbot_small-90M': 512,
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			BlenderbotSmallTokenizer


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  str=False       ,	snake_case__					:  int=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(
										    ByteLevelBPETokenizer(
										        vocab=snake_case__       ,	merges=snake_case__       ,	add_prefix_space=snake_case__       ,	trim_offsets=snake_case__       ,	)       ,	bos_token=snake_case__       ,	eos_token=snake_case__       ,	unk_token=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Optional[int]									=add_prefix_space


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None ):
										lowerCamelCase_	:							Optional[Any]									=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																	return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							int									=[self.sep_token_id]
										lowerCamelCase_	:							List[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]



