






"""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


_UpperCamelCase    :   Dict   			=						logging.get_logger(__name__)

_UpperCamelCase    :   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 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''longformer'''





    def __init__(							self     : Optional[Any]    ,		A     : Union[List[int], int] = 5_1_2    ,		A     : int = 2    ,		A     : int = 1    ,		A     : int = 0    ,		A     : int = 2    ,		A     : int = 3_0_5_2_2    ,		A     : int = 7_6_8    ,		A     : int = 1_2    ,		A     : int = 1_2    ,		A     : int = 3_0_7_2    ,		A     : str = "gelu"    ,		A     : float = 0.1    ,		A     : float = 0.1    ,		A     : int = 5_1_2    ,		A     : int = 2    ,		A     : float = 0.02    ,		A     : float = 1E-12    ,		A     : bool = False    ,		**A     : Any    ,		):


      '''simple docstring'''




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

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






class 						snake_case  (  UpperCAmelCase	):





    def __init__(							self     : Optional[int]    ,		A     : "PretrainedConfig"    ,		A     : str = "default"    ,		A     : "List[PatchingSpec]" = None      ):


      '''simple docstring'''




      super().__init__(A    ,		A    ,		A      )
      a					:      List[Any]    =       True





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      if self.task == "multiple-choice":
        a					:      List[Any]    =       {0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
        a					:      List[Any]    =       {0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('global_attention_mask', dynamic_axis),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       super().outputs
      if self.task == "default":
        a					:      Tuple    =       {0: 'batch'}
      return outputs





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return 1E-4





    @property
    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      return max(super().default_onnx_opset    ,		1_4      )





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : "PreTrainedTokenizerBase"    ,		A     : int = -1    ,		A     : int = -1    ,		A     : bool = False    ,		A     : Optional[TensorType] = None    ,		):


      '''simple docstring'''




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

      return inputs












"""simple docstring"""


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


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

_UpperCamelCase    :   List[Any]   			=						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''beit'''





    def __init__(							self     : int    ,		A     : int=8_1_9_2    ,		A     : List[Any]=7_6_8    ,		A     : str=1_2    ,		A     : str=1_2    ,		A     : Dict=3_0_7_2    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : Union[str, Any]=0.0    ,		A     : Optional[Any]=0.02    ,		A     : Optional[int]=1E-12    ,		A     : Dict=2_2_4    ,		A     : str=1_6    ,		A     : Optional[Any]=3    ,		A     : List[Any]=False    ,		A     : Union[str, Any]=False    ,		A     : Optional[Any]=False    ,		A     : int=False    ,		A     : List[str]=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : str=True    ,		A     : Tuple=[3, 5, 7, 1_1]    ,		A     : List[str]=[1, 2, 3, 6]    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=0.4    ,		A     : Any=2_5_6    ,		A     : List[Any]=1    ,		A     : Optional[Any]=False    ,		A     : Any=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Optional[int]    =       vocab_size
      a					:      Dict    =       hidden_size
      a					:      Optional[int]    =       num_hidden_layers
      a					:      Tuple    =       num_attention_heads
      a					:      Optional[int]    =       intermediate_size
      a					:      Optional[Any]    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Optional[Any]    =       initializer_range
      a					:      Union[str, Any]    =       layer_norm_eps

      a					:      Union[str, Any]    =       image_size
      a					:      str    =       patch_size
      a					:      Optional[Any]    =       num_channels
      a					:      List[str]    =       use_mask_token
      a					:      Optional[Any]    =       use_absolute_position_embeddings
      a					:      Any    =       use_relative_position_bias
      a					:      Any    =       use_shared_relative_position_bias
      a					:      Dict    =       layer_scale_init_value
      a					:      Optional[int]    =       drop_path_rate
      a					:      Dict    =       use_mean_pooling
      # decode head attributes (semantic segmentation)
      a					:      Optional[Any]    =       out_indices
      a					:      List[str]    =       pool_scales
      # auxiliary head attributes (semantic segmentation)
      a					:      Tuple    =       use_auxiliary_head
      a					:      Dict    =       auxiliary_loss_weight
      a					:      Any    =       auxiliary_channels
      a					:      Dict    =       auxiliary_num_convs
      a					:      List[str]    =       auxiliary_concat_input
      a					:      List[Any]    =       semantic_loss_ignore_index






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return 1E-4




