

import copy

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


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

_UpperCAmelCase		:       Dict      =	{
    """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 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      '''dpt'''




				def __init__( self				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.0				,       snake_case=0.0				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=384				,       snake_case=16				,       snake_case=3				,       snake_case=False				,       snake_case=True				,       snake_case=[2, 5, 8, 11]				,       snake_case="project"				,       snake_case=[4, 2, 1, 0.5]				,       snake_case=[96, 192, 384, 768]				,       snake_case=256				,       snake_case=-1				,       snake_case=False				,       snake_case=True				,       snake_case=0.4				,       snake_case=255				,       snake_case=0.1				,       snake_case=[1, 1024, 24, 24]				,       snake_case=[0, 1]				,       snake_case=None				,       **snake_case				,       ):
											super().__init__(**snake_case	)

											snake_case_          =   hidden_size
											snake_case_          =   is_hybrid

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

																		snake_case_          =   backbone_featmap_shape
																		snake_case_          =   neck_ignore_stages

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

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




				def    a							( self	):
											snake_case_          =   copy.deepcopy(self.__dict__	)

											if output["backbone_config"] is not None:
																		snake_case_          =   self.backbone_config.to_dict()

											snake_case_          =   self.__class__.model_type
											return output







from __future__ import annotations

import os
from typing import Any

import requests

_UpperCAmelCase		:       int						    =	"""https://api.github.com"""

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_UpperCAmelCase		:       Dict						    =	BASE_URL + """/user"""

# https://github.com/settings/tokens
_UpperCAmelCase		:       Optional[Any]						    =	os.environ.get("""USER_TOKEN""", """""")





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {
							    'Authorization': F'''token {auth_token}''',
							    'Accept': 'application/vnd.github.v3+json',
							}
							return requests.get(UpperCamelCase__       ,							headers=UpperCamelCase__				).json()


if __name__ == "__main__":  # pragma: no cover
					if USER_TOKEN:
										for key, value in fetch_github_info(USER_TOKEN).items():
															print(F'''{key}: {value}''')
					else:
										raise ValueError("""'USER_TOKEN' field cannot be empty.""")




