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


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

lowerCamelCase_				:		Tuple       						=       {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}


class 						a__							(   __snake_case		):
			A__				:   List[str]								=			'tvlt'



			def __init__(						self      ,						UpperCAmelCase=2_2_4      ,						UpperCAmelCase=2_0_4_8      ,						UpperCAmelCase=1_2_8      ,						UpperCAmelCase=[1_6, 1_6]      ,						UpperCAmelCase=[1_6, 1_6]      ,						UpperCAmelCase=3      ,						UpperCAmelCase=1      ,						UpperCAmelCase=8      ,						UpperCAmelCase=7_6_8      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase=3_0_7_2      ,						UpperCAmelCase="gelu"      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=1e-6      ,						UpperCAmelCase=True      ,						UpperCAmelCase=False      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=5_1_2      ,						UpperCAmelCase=8      ,						UpperCAmelCase=2_0_4_8      ,						UpperCAmelCase=0.75      ,						UpperCAmelCase=0.15      ,						UpperCAmelCase="frame-level"      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase="classification"      ,						**UpperCAmelCase      ,						) ->    Optional[int]:
								super().__init__(**UpperCAmelCase    )

								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}'''    )

								__a							=   image_size
								__a							=   spectrogram_length
								__a							=   frequency_length
								__a							=   image_patch_size
								__a							=   audio_patch_size
								__a							=   num_image_channels
								__a							=   num_audio_channels
								__a							=   num_frames

								__a							=   hidden_size
								__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							=   qkv_bias
								__a							=   use_mean_pooling

								__a							=   decoder_num_attention_heads
								__a							=   decoder_hidden_size
								__a							=   decoder_num_hidden_layers
								__a							=   decoder_intermediate_size
								__a							=   pixel_mask_ratio
								__a							=   audio_mask_ratio
								__a							=   audio_mask_type

								__a							=   task_matching
								__a							=   task_mae
								__a							=   loss_type





from typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




