





"""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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

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

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'conditional_detr'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(       self      ,						lowercase=True      ,						lowercase=None      ,						lowercase=3      ,						lowercase=300      ,						lowercase=6      ,						lowercase=2_048      ,						lowercase=8      ,						lowercase=6      ,						lowercase=2_048      ,						lowercase=8      ,						lowercase=0.0      ,						lowercase=0.0      ,						lowercase=True      ,						lowercase="relu"      ,						lowercase=256      ,						lowercase=0.1      ,						lowercase=0.0      ,						lowercase=0.0      ,						lowercase=0.02      ,						lowercase=1.0      ,						lowercase=False      ,						lowercase="sine"      ,						lowercase="resnet50"      ,						lowercase=True      ,						lowercase=False      ,						lowercase=2      ,						lowercase=5      ,						lowercase=2      ,						lowercase=1      ,						lowercase=1      ,						lowercase=2      ,						lowercase=5      ,						lowercase=2      ,						lowercase=0.25      ,						**lowercase      ,						)		->     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  						=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
                  elif isinstance(lowercase      ,						lowercase ):
                        lowerCAmelCase  						=       backbone_config.get("""model_type""" )
                        lowerCAmelCase  						=       CONFIG_MAPPING[backbone_model_type]
                        lowerCAmelCase  						=       config_class.from_dict(lowercase )

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





      @property
      def     _snake_case			(       self )		->     int:
            return self.encoder_attention_heads





      @property
      def     _snake_case			(       self )		->     int:
            return self.d_model





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       copy.deepcopy(self.__dict__ )
            if self.backbone_config is not None:
                  lowerCAmelCase  						=       self.backbone_config.to_dict()
            lowerCAmelCase  						=       self.__class__.model_type
            return output







class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      version.parse('1.11'					)





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ("""pixel_mask""", {0: """batch"""}),
                ] )





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-5





      @property
      def     _snake_case			(       self )		->     int:
            return 12













"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'longformer'





      def __init__(       self      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 1      ,						lowercase = 0      ,						lowercase = 2      ,						lowercase = 30_522      ,						lowercase = 768      ,						lowercase = 12      ,						lowercase = 12      ,						lowercase = 3_072      ,						lowercase = "gelu"      ,						lowercase = 0.1      ,						lowercase = 0.1      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 0.02      ,						lowercase = 1e-12      ,						lowercase = False      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(pad_token_id=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       attention_window
            lowerCAmelCase  						=       sep_token_id
            lowerCAmelCase  						=       bos_token_id
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       layer_norm_eps
            lowerCAmelCase  						=       onnx_export







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase = "default"      ,						lowercase = None )		->     Tuple:
            super().__init__(lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       True





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  lowerCAmelCase  						=       {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""global_attention_mask""", dynamic_axis),
                ] )





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            lowerCAmelCase  						=       super().outputs
            if self.task == "default":
                  lowerCAmelCase  						=       {0: """batch"""}
            return outputs





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-4





      @property
      def     _snake_case			(       self )		->     int:
            # needs to be >= 14 to support tril operator
            return max(super().default_onnx_opset      ,						14 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = -1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       super().generate_dummy_inputs(
                preprocessor=lowercase      ,						batch_size=lowercase      ,						seq_length=lowercase      ,						is_pair=lowercase      ,						framework=lowercase )
            import torch

            # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
            # makes the export fail randomly
            lowerCAmelCase  						=       torch.zeros_like(inputs["""input_ids"""] )
            # make every second token global
            lowerCAmelCase  						=       1

            return inputs






