


"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE       :  str        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  Optional[int]        = {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}

class     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						'tvlt'






			def __init__(							self     :					Optional[Any]						,  lowercase_     :					Optional[Any]=224						,  lowercase_     :					str=2048						,  lowercase_     :					Tuple=128						,  lowercase_     :					Optional[Any]=[16, 16]						,  lowercase_     :					Optional[Any]=[16, 16]						,  lowercase_     :					Union[str, Any]=3						,  lowercase_     :					int=1						,  lowercase_     :					Any=8						,  lowercase_     :					List[Any]=768						,  lowercase_     :					List[str]=12						,  lowercase_     :					str=12						,  lowercase_     :					List[Any]=3072						,  lowercase_     :					Any="gelu"						,  lowercase_     :					Optional[Any]=0.0						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					Optional[int]=0.0_2						,  lowercase_     :					Dict=1e-6						,  lowercase_     :					Optional[int]=True						,  lowercase_     :					int=False						,  lowercase_     :					List[str]=16						,  lowercase_     :					Optional[int]=512						,  lowercase_     :					Union[str, Any]=8						,  lowercase_     :					Optional[int]=2048						,  lowercase_     :					Tuple=0.7_5						,  lowercase_     :					Any=0.1_5						,  lowercase_     :					str="frame-level"						,  lowercase_     :					str=True						,  lowercase_     :					Union[str, Any]=True						,  lowercase_     :					Optional[Any]="classification"						,  **lowercase_     :					int						,  ):
								super().__init__(**lowercase_						)

								if audio_mask_type not in ("frame-level", "patch_level"):
													raise ValueError(
													    '''audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '''
													    f'''got {audio_mask_type}'''						)

								UpperCamelCase__		:    int         =image_size
								UpperCamelCase__		:    Union[str, Any]         =spectrogram_length
								UpperCamelCase__		:    Tuple         =frequency_length
								UpperCamelCase__		:    int         =image_patch_size
								UpperCamelCase__		:    Any         =audio_patch_size
								UpperCamelCase__		:    Tuple         =num_image_channels
								UpperCamelCase__		:    str         =num_audio_channels
								UpperCamelCase__		:    Dict         =num_frames

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

								UpperCamelCase__		:    int         =decoder_num_attention_heads
								UpperCamelCase__		:    Dict         =decoder_hidden_size
								UpperCamelCase__		:    Any         =decoder_num_hidden_layers
								UpperCamelCase__		:    Optional[int]         =decoder_intermediate_size
								UpperCamelCase__		:    List[str]         =pixel_mask_ratio
								UpperCamelCase__		:    Any         =audio_mask_ratio
								UpperCamelCase__		:    Union[str, Any]         =audio_mask_type

								UpperCamelCase__		:    Any         =task_matching
								UpperCamelCase__		:    int         =task_mae
								UpperCamelCase__		:    List[str]         =loss_type





"""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}}}''')


