




'''simple docstring'''


import copy

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


_snake_case : Dict  				=						logging.get_logger(__name__)

_snake_case : int  				=						{
    '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      A       (						_a       ):
							lowercase_							     =							'dpt'







							def __init__(    self			:    List[Any]				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    str=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[Any]=30_72				,     lowerCAmelCase_			:    Any="gelu"				,     lowerCAmelCase_			:    Any=0.0				,     lowerCAmelCase_			:    Union[str, Any]=0.0				,     lowerCAmelCase_			:    List[str]=0.0_2				,     lowerCAmelCase_			:    Any=1e-12				,     lowerCAmelCase_			:    List[Any]=3_84				,     lowerCAmelCase_			:    Optional[Any]=16				,     lowerCAmelCase_			:    Optional[int]=3				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    Dict=[2, 5, 8, 11]				,     lowerCAmelCase_			:    Optional[Any]="project"				,     lowerCAmelCase_			:    int=[4, 2, 1, 0.5]				,     lowerCAmelCase_			:    Optional[Any]=[96, 1_92, 3_84, 7_68]				,     lowerCAmelCase_			:    List[Any]=2_56				,     lowerCAmelCase_			:    Optional[int]=-1				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    Optional[int]=True				,     lowerCAmelCase_			:    Any=0.4				,     lowerCAmelCase_			:    List[str]=2_55				,     lowerCAmelCase_			:    Any=0.1				,     lowerCAmelCase_			:    Tuple=[1, 10_24, 24, 24]				,     lowerCAmelCase_			:    Optional[int]=[0, 1]				,     lowerCAmelCase_			:    int=None				,     **lowerCAmelCase_			:    Dict				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )

														_a      	=							hidden_size
														_a      	=							is_hybrid

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

																					_a      	=							backbone_featmap_shape
																					_a      	=							neck_ignore_stages

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

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







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							copy.deepcopy(self.__dict__   )

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

														_a      	=							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


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

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


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

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


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

