


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

SCREAMING_SNAKE_CASE			: Dict				   =					{
    "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 _lowerCamelCase(		_a  ):
       lowercase_					:      List[str]       					=     """longformer"""
       def __init__(   self, lowerCamelCase = 5_12, lowerCamelCase = 2, lowerCamelCase = 1, lowerCamelCase = 0, lowerCamelCase = 2, lowerCamelCase = 3_05_22, lowerCamelCase = 7_68, lowerCamelCase = 12, lowerCamelCase = 12, lowerCamelCase = 30_72, lowerCamelCase = "gelu", lowerCamelCase = 0.1, lowerCamelCase = 0.1, lowerCamelCase = 5_12, lowerCamelCase = 2, lowerCamelCase = 0.0_2, lowerCamelCase = 1E-12, lowerCamelCase = False, **lowerCamelCase, )			->  Optional[Any]:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase, **lowerCamelCase)

           _lowercase			:				List[Any] 						=						attention_window
           _lowercase			:				Any 						=						sep_token_id
           _lowercase			:				Any 						=						bos_token_id
           _lowercase			:				int 						=						eos_token_id
           _lowercase			:				str 						=						vocab_size
           _lowercase			:				Optional[Any] 						=						hidden_size
           _lowercase			:				Union[str, Any] 						=						num_hidden_layers
           _lowercase			:				Any 						=						num_attention_heads
           _lowercase			:				Dict 						=						hidden_act
           _lowercase			:				int 						=						intermediate_size
           _lowercase			:				str 						=						hidden_dropout_prob
           _lowercase			:				Optional[Any] 						=						attention_probs_dropout_prob
           _lowercase			:				str 						=						max_position_embeddings
           _lowercase			:				Optional[int] 						=						type_vocab_size
           _lowercase			:				Optional[int] 						=						initializer_range
           _lowercase			:				Any 						=						layer_norm_eps
           _lowercase			:				Tuple 						=						onnx_export






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase = "default", lowerCamelCase = None)			->  Dict:




           """simple docstring"""

           super().__init__(lowerCamelCase, lowerCamelCase, lowerCamelCase)
           _lowercase			:				Any 						=						True
       @property
       def      UpperCamelCase	(   self)			->  Mapping[str, Mapping[int, str]]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						super().outputs
           if self.task == "default":
               _lowercase			:				int 						=						{0: 'batch'}
           return outputs
       @property
       def      UpperCamelCase	(   self)			->  float:




           """simple docstring"""

           return 1E-4
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return max(super().default_onnx_opset, 14)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = -1, lowerCamelCase = -1, lowerCamelCase = False, lowerCamelCase = None, )			->  Mapping[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						super().generate_dummy_inputs(
               preprocessor=lowerCamelCase, batch_size=lowerCamelCase, seq_length=lowerCamelCase, is_pair=lowerCamelCase, framework=lowerCamelCase)
           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
           _lowercase			:				Dict 						=						torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _lowercase			:				List[str] 						=						1

           return inputs






import copy

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


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Any				   =					{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     """deta"""
       lowercase_					:      Union[str, Any]       					=     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(   self, lowerCamelCase=None, lowerCamelCase=9_00, lowerCamelCase=20_48, lowerCamelCase=6, lowerCamelCase=20_48, lowerCamelCase=8, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=0.0, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=2_56, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=1.0, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase="sine", lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=True, lowerCamelCase=3_00, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, **lowerCamelCase, )			->  Any:




           """simple docstring"""

           if backbone_config is None:
               logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
               _lowercase			:				List[Any] 						=						CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
           else:
               if isinstance(lowerCamelCase, lowerCamelCase):
                   _lowercase			:				Dict 						=						backbone_config.pop('model_type')
                   _lowercase			:				int 						=						CONFIG_MAPPING[backbone_model_type]
                   _lowercase			:				Union[str, Any] 						=						config_class.from_dict(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						backbone_config
           _lowercase			:				Any 						=						num_queries
           _lowercase			:				Union[str, Any] 						=						max_position_embeddings
           _lowercase			:				Union[str, Any] 						=						d_model
           _lowercase			:				Optional[int] 						=						encoder_ffn_dim
           _lowercase			:				Optional[int] 						=						encoder_layers
           _lowercase			:				Optional[Any] 						=						encoder_attention_heads
           _lowercase			:				Optional[Any] 						=						decoder_ffn_dim
           _lowercase			:				Dict 						=						decoder_layers
           _lowercase			:				Tuple 						=						decoder_attention_heads
           _lowercase			:				Union[str, Any] 						=						dropout
           _lowercase			:				Optional[Any] 						=						attention_dropout
           _lowercase			:				int 						=						activation_dropout
           _lowercase			:				Tuple 						=						activation_function
           _lowercase			:				List[Any] 						=						init_std
           _lowercase			:				Union[str, Any] 						=						init_xavier_std
           _lowercase			:				int 						=						encoder_layerdrop
           _lowercase			:				Optional[int] 						=						auxiliary_loss
           _lowercase			:				Dict 						=						position_embedding_type
           # deformable attributes
           _lowercase			:				Any 						=						num_feature_levels
           _lowercase			:				str 						=						encoder_n_points
           _lowercase			:				Any 						=						decoder_n_points
           _lowercase			:				List[str] 						=						two_stage
           _lowercase			:				Dict 						=						two_stage_num_proposals
           _lowercase			:				Any 						=						with_box_refine
           _lowercase			:				List[Any] 						=						assign_first_stage
           if two_stage is True and with_box_refine is False:
               raise ValueError('If two_stage is True, with_box_refine must be True.')
           # Hungarian matcher
           _lowercase			:				List[Any] 						=						class_cost
           _lowercase			:				Optional[int] 						=						bbox_cost
           _lowercase			:				str 						=						giou_cost
           # Loss coefficients
           _lowercase			:				Optional[int] 						=						mask_loss_coefficient
           _lowercase			:				int 						=						dice_loss_coefficient
           _lowercase			:				List[Any] 						=						bbox_loss_coefficient
           _lowercase			:				Optional[Any] 						=						giou_loss_coefficient
           _lowercase			:				str 						=						eos_coefficient
           _lowercase			:				int 						=						focal_alpha
           super().__init__(is_encoder_decoder=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

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




           """simple docstring"""

           return self.d_model




       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

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


