



"""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


A:   Dict								=		logging.get_logger(__name__)

A:   List[Any]								=		{
    "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       SCREAMING_SNAKE_CASE__  (					lowerCamelCase_ ):
   __lowerCAmelCase  :   Optional[Any]						=						'longformer'

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = 1      ,     _SCREAMING_SNAKE_CASE = 0      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = 30522      ,     _SCREAMING_SNAKE_CASE = 768      ,     _SCREAMING_SNAKE_CASE = 12      ,     _SCREAMING_SNAKE_CASE = 12      ,     _SCREAMING_SNAKE_CASE = 3072      ,     _SCREAMING_SNAKE_CASE = "gelu"      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = 0.02      ,     _SCREAMING_SNAKE_CASE = 1E-12      ,     _SCREAMING_SNAKE_CASE = False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Optional[Any]:





    '''simple docstring'''




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

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



class       SCREAMING_SNAKE_CASE__  (					lowerCamelCase_ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = "default"      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Dict:





    '''simple docstring'''




    super().__init__(__SCREAMING_SNAKE_CASE      ,     __SCREAMING_SNAKE_CASE      ,     __SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Union[str, Any]	=	True

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    return 1E-4

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    return max(super().default_onnx_opset      ,     14						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	super().generate_dummy_inputs(
        preprocessor=__SCREAMING_SNAKE_CASE      ,     batch_size=__SCREAMING_SNAKE_CASE      ,     seq_length=__SCREAMING_SNAKE_CASE      ,     is_pair=__SCREAMING_SNAKE_CASE      ,     framework=__SCREAMING_SNAKE_CASE						)
    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
    UpperCAmelCase	:					Union[str, Any]	=	torch.zeros_like(inputs["""input_ids"""]						)
    # make every second token global
    UpperCAmelCase	:					Optional[Any]	=	1

    return inputs


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Optional[int]    							=       ["Mask2FormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Mask2FormerForUniversalSegmentation",
							    "Mask2FormerModel",
							    "Mask2FormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_maskaformer import MaskaFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskaformer import (
														    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskaFormerForUniversalSegmentation,
														    MaskaFormerModel,
														    MaskaFormerPreTrainedModel,
														)


else:
							import sys

							_lowercase :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



