
"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__      :    Optional[int]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Optional[Any]    							= {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   '''dpt'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=3_84   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=[2, 5, 8, 11]   ,       SCREAMING_SNAKE_CASE__="project"   ,       SCREAMING_SNAKE_CASE__=[4, 2, 1, 0.5]   ,       SCREAMING_SNAKE_CASE__=[96, 1_92, 3_84, 7_68]   ,       SCREAMING_SNAKE_CASE__=2_56   ,       SCREAMING_SNAKE_CASE__=-1   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=0.4   ,       SCREAMING_SNAKE_CASE__=2_55   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=[1, 10_24, 24, 24]   ,       SCREAMING_SNAKE_CASE__=[0, 1]   ,       SCREAMING_SNAKE_CASE__=None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   is_hybrid

											if self.is_hybrid:
																		if backbone_config is None:
																									logger.info("""Initializing the config with a `BiT` backbone."""    )
																									SCREAMING_SNAKE_CASE__						:       Any           =   {
																									    """global_padding""": """same""",
																									    """layer_type""": """bottleneck""",
																									    """depths""": [3, 4, 9],
																									    """out_features""": ["""stage1""", """stage2""", """stage3"""],
																									    """embedding_dynamic_padding""": True,
																									}
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   BitConfig(**SCREAMING_SNAKE_CASE__    )
																		elif isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									logger.info("""Initializing the config with a `BiT` backbone."""    )
																									SCREAMING_SNAKE_CASE__						:       str           =   BitConfig(**SCREAMING_SNAKE_CASE__    )
																		elif isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   backbone_config
																		else:
																									raise ValueError(
																									    F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''    )

																		SCREAMING_SNAKE_CASE__						:       Dict           =   backbone_featmap_shape
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   neck_ignore_stages

																		if readout_type != "project":
																									raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""    )
											else:
																		SCREAMING_SNAKE_CASE__						:       str           =   None
																		SCREAMING_SNAKE_CASE__						:       int           =   None
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   []

											SCREAMING_SNAKE_CASE__						:       Any           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       List[str]           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       int           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       List[str]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Tuple           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Tuple           =   image_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_channels
											SCREAMING_SNAKE_CASE__						:       Any           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   backbone_out_indices
											if readout_type not in ["ignore", "add", "project"]:
																		raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   readout_type
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   reassemble_factors
											SCREAMING_SNAKE_CASE__						:       List[str]           =   neck_hidden_sizes
											SCREAMING_SNAKE_CASE__						:       Dict           =   fusion_hidden_size
											SCREAMING_SNAKE_CASE__						:       int           =   head_in_index
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   use_batch_norm_in_fusion_residual
											# auxiliary head attributes (semantic segmentation)
											SCREAMING_SNAKE_CASE__						:       Tuple           =   use_auxiliary_head
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   auxiliary_loss_weight
											SCREAMING_SNAKE_CASE__						:       Tuple           =   semantic_loss_ignore_index
											SCREAMING_SNAKE_CASE__						:       List[str]           =   semantic_classifier_dropout



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   copy.deepcopy(self.__dict__    )

											if output["backbone_config"] is not None:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.backbone_config.to_dict()

											SCREAMING_SNAKE_CASE__						:       str           =   self.__class__.model_type
											return output








'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


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

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




