


"""simple docstring"""





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_SCREAMING_SNAKE_CASE       :  Tuple        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  List[str]        = {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     __a   (      a__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						"vit-hybrid"






			def __init__(							self     :					List[Any]						,  lowercase_     :					Any=None						,  lowercase_     :					Dict=768						,  lowercase_     :					List[Any]=12						,  lowercase_     :					Union[str, Any]=12						,  lowercase_     :					Optional[Any]=3072						,  lowercase_     :					str="gelu"						,  lowercase_     :					Union[str, Any]=0.0						,  lowercase_     :					int=0.0						,  lowercase_     :					str=0.0_2						,  lowercase_     :					int=1e-12						,  lowercase_     :					Optional[Any]=224						,  lowercase_     :					List[Any]=1						,  lowercase_     :					str=3						,  lowercase_     :					Union[str, Any]=[1, 1024, 24, 24]						,  lowercase_     :					Union[str, Any]=True						,  **lowercase_     :					Any						,  ):
								super().__init__(**SCREAMING_SNAKE_CASE_						)

								if backbone_config is None:
													logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''						)
													UpperCamelCase__		:    Tuple         ={
													    'global_padding': 'same',
													    'layer_type': 'bottleneck',
													    'depths': [3, 4, 9],
													    'out_features': ['stage3'],
													    'embedding_dynamic_padding': True,
													}

								if isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						):
													if "model_type" in backbone_config:
																		UpperCamelCase__		:    List[Any]         =CONFIG_MAPPING[backbone_config['model_type']]
													else:
																		logger.info(
																		    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
																		UpperCamelCase__		:    int         =BitConfig
													UpperCamelCase__		:    str         =backbone_config_class(**SCREAMING_SNAKE_CASE_						)

								UpperCamelCase__		:    List[Any]         =backbone_featmap_shape
								UpperCamelCase__		:    Dict         =backbone_config
								UpperCamelCase__		:    List[str]         =hidden_size
								UpperCamelCase__		:    Any         =num_hidden_layers
								UpperCamelCase__		:    Tuple         =num_attention_heads
								UpperCamelCase__		:    List[str]         =intermediate_size
								UpperCamelCase__		:    Any         =hidden_act
								UpperCamelCase__		:    Optional[int]         =hidden_dropout_prob
								UpperCamelCase__		:    str         =attention_probs_dropout_prob
								UpperCamelCase__		:    Optional[Any]         =initializer_range
								UpperCamelCase__		:    Dict         =layer_norm_eps
								UpperCamelCase__		:    Union[str, Any]         =image_size
								UpperCamelCase__		:    List[Any]         =patch_size
								UpperCamelCase__		:    Optional[Any]         =num_channels
								UpperCamelCase__		:    List[str]         =qkv_bias






			def    _lowerCAmelCase			(							self     :					Any						):
								UpperCamelCase__		:    Tuple         =copy.deepcopy(self.__dict__						)
								UpperCamelCase__		:    Optional[int]         =self.backbone_config.to_dict()
								UpperCamelCase__		:    Any         =self.__class__.model_type
								return output





"""simple docstring"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										if error < maxerror:
															break
					else:
										raise ValueError('''Iteration limit reached, no converging solution found'''				)
					if logsteps:
										# If logstep is true, then log intermediate steps
										return a, error, steps
					return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("""step""")
				plt.ylabel("""error""")
				plt.show()
				print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')


