


"""simple docstring"""






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"conditional_detr"
				_lowerCAmelCase      :      Dict      							=					["past_key_values"]
				_lowerCAmelCase      :      Union[str, Any]      							=					{
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}



				def __init__(						self       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=300       ,		UpperCAmelCase__=6       ,		UpperCAmelCase__=2048       ,		UpperCAmelCase__=8       ,		UpperCAmelCase__=6       ,		UpperCAmelCase__=2048       ,		UpperCAmelCase__=8       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__="relu"       ,		UpperCAmelCase__=256       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=1.0       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__="sine"       ,		UpperCAmelCase__="resnet50"       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=5       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=1       ,		UpperCAmelCase__=1       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=5       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=0.25       ,		**UpperCAmelCase__       ,		):
								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."     )
																SCREAMING_SNAKE_CASE__     							=				CONFIG_MAPPING["resnet"](out_features=["stage4"]     )
												elif isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
																SCREAMING_SNAKE_CASE__     							=				backbone_config.get("model_type"     )
																SCREAMING_SNAKE_CASE__     							=				CONFIG_MAPPING[backbone_model_type]
																SCREAMING_SNAKE_CASE__     							=				config_class.from_dict(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				use_timm_backbone
								SCREAMING_SNAKE_CASE__     							=				backbone_config
								SCREAMING_SNAKE_CASE__     							=				num_channels
								SCREAMING_SNAKE_CASE__     							=				num_queries
								SCREAMING_SNAKE_CASE__     							=				d_model
								SCREAMING_SNAKE_CASE__     							=				encoder_ffn_dim
								SCREAMING_SNAKE_CASE__     							=				encoder_layers
								SCREAMING_SNAKE_CASE__     							=				encoder_attention_heads
								SCREAMING_SNAKE_CASE__     							=				decoder_ffn_dim
								SCREAMING_SNAKE_CASE__     							=				decoder_layers
								SCREAMING_SNAKE_CASE__     							=				decoder_attention_heads
								SCREAMING_SNAKE_CASE__     							=				dropout
								SCREAMING_SNAKE_CASE__     							=				attention_dropout
								SCREAMING_SNAKE_CASE__     							=				activation_dropout
								SCREAMING_SNAKE_CASE__     							=				activation_function
								SCREAMING_SNAKE_CASE__     							=				init_std
								SCREAMING_SNAKE_CASE__     							=				init_xavier_std
								SCREAMING_SNAKE_CASE__     							=				encoder_layerdrop
								SCREAMING_SNAKE_CASE__     							=				decoder_layerdrop
								SCREAMING_SNAKE_CASE__     							=				encoder_layers
								SCREAMING_SNAKE_CASE__     							=				auxiliary_loss
								SCREAMING_SNAKE_CASE__     							=				position_embedding_type
								SCREAMING_SNAKE_CASE__     							=				backbone
								SCREAMING_SNAKE_CASE__     							=				use_pretrained_backbone
								SCREAMING_SNAKE_CASE__     							=				dilation
								# Hungarian matcher
								SCREAMING_SNAKE_CASE__     							=				class_cost
								SCREAMING_SNAKE_CASE__     							=				bbox_cost
								SCREAMING_SNAKE_CASE__     							=				giou_cost
								# Loss coefficients
								SCREAMING_SNAKE_CASE__     							=				mask_loss_coefficient
								SCREAMING_SNAKE_CASE__     							=				dice_loss_coefficient
								SCREAMING_SNAKE_CASE__     							=				cls_loss_coefficient
								SCREAMING_SNAKE_CASE__     							=				bbox_loss_coefficient
								SCREAMING_SNAKE_CASE__     							=				giou_loss_coefficient
								SCREAMING_SNAKE_CASE__     							=				focal_alpha
								super().__init__(is_encoder_decoder=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				@property
				def   lowerCAmelCase__		(						self     ):
								return self.encoder_attention_heads



				@property
				def   lowerCAmelCase__		(						self     ):
								return self.d_model






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				copy.deepcopy(self.__dict__     )
								if self.backbone_config is not None:
												SCREAMING_SNAKE_CASE__     							=				self.backbone_config.to_dict()
								SCREAMING_SNAKE_CASE__     							=				self.__class__.model_type
								return output


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					version.parse("1.11"   )



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



				@property
				def   lowerCAmelCase__		(						self     ):
								return 1e-5






				@property
				def   lowerCAmelCase__		(						self     ):
								return 12




"""simple docstring"""






import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__ = "▁"       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = "<unk>"       ,		UpperCAmelCase__ = "</s>"       ,		UpperCAmelCase__ = "<pad>"       ,		):
								SCREAMING_SNAKE_CASE__     							=				{
								    "pad": {"id": 0, "token": pad_token},
								    "eos": {"id": 1, "token": eos_token},
								    "unk": {"id": 2, "token": unk_token},
								}

								SCREAMING_SNAKE_CASE__     							=				[None] * len(self.special_tokens     )
								for token_dict in self.special_tokens.values():
												SCREAMING_SNAKE_CASE__     							=				token_dict["token"]

								SCREAMING_SNAKE_CASE__     							=				Tokenizer(Unigram()     )

								SCREAMING_SNAKE_CASE__     							=				normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(" {2,}"     )       ,		" "     ),
								        normalizers.Lowercase(),
								    ]     )
								SCREAMING_SNAKE_CASE__     							=				pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=UpperCAmelCase__       ,		add_prefix_space=UpperCAmelCase__     ),
								        pre_tokenizers.Digits(individual_digits=UpperCAmelCase__     ),
								        pre_tokenizers.Punctuation(),
								    ]     )
								SCREAMING_SNAKE_CASE__     							=				decoders.Metaspace(replacement=UpperCAmelCase__       ,		add_prefix_space=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				TemplateProcessing(
								    single=f'''$A {self.special_tokens["eos"]["token"]}'''       ,		special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]       ,		)

								SCREAMING_SNAKE_CASE__     							=				{
								    "model": "SentencePieceUnigram",
								    "replacement": replacement,
								    "add_prefix_space": add_prefix_space,
								}

								super().__init__(UpperCAmelCase__       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 8000       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				trainers.UnigramTrainer(
								    vocab_size=UpperCAmelCase__       ,		special_tokens=self.special_tokens_list       ,		show_progress=UpperCAmelCase__       ,		)

								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				[files]
								self._tokenizer.train(UpperCAmelCase__       ,		trainer=UpperCAmelCase__     )

								self.add_unk_id()



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 8000       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				trainers.UnigramTrainer(
								    vocab_size=UpperCAmelCase__       ,		special_tokens=self.special_tokens_list       ,		show_progress=UpperCAmelCase__       ,		)

								self._tokenizer.train_from_iterator(UpperCAmelCase__       ,		trainer=UpperCAmelCase__     )

								self.add_unk_id()






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				json.loads(self._tokenizer.to_str()     )

								SCREAMING_SNAKE_CASE__     							=				self.special_tokens["unk"]["id"]

								SCREAMING_SNAKE_CASE__     							=				Tokenizer.from_str(json.dumps(UpperCAmelCase__     )     )
