






import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

UpperCAmelCase__			 =	[[12, 10], [3, 9]]
UpperCAmelCase__			 =	[[3, 4], [7, 4]]
UpperCAmelCase__			 =	[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
UpperCAmelCase__			 =	[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
UpperCAmelCase__			 =	[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
UpperCAmelCase__			 =	[1]
UpperCAmelCase__			 =	[2]

UpperCAmelCase__			 =	logging.getLogger()
UpperCAmelCase__			 =	logging.DEBUG
UpperCAmelCase__			 =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")							,					[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 						a_      (__A							,					__A			)							->	Dict:



					"""simple docstring"""
					if (np.array(__A			)).shape < (2, 2) or (np.array(__A			)).shape < (2, 2):
										with pytest.raises(__A			):
															logger.info(f'\n\t{test_addition.__name__} returned integer'			)
															matop.add(__A							,					__A			)
					elif (np.array(__A			)).shape == (np.array(__A			)).shape:
										logger.info(f'\n\t{test_addition.__name__} with same matrix dims'			)
										__a				:	Tuple			=				(np.array(__A			) + np.array(__A			)).tolist()
										__a				:	List[str]			=				matop.add(__A							,					__A			)
										assert theo == act
					else:
										with pytest.raises(__A			):
															logger.info(f'\n\t{test_addition.__name__} with different matrix dims'			)
															matop.add(__A							,					__A			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")							,					[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 						a_      (__A							,					__A			)							->	Optional[Any]:



					"""simple docstring"""
					if (np.array(__A			)).shape < (2, 2) or (np.array(__A			)).shape < (2, 2):
										with pytest.raises(__A			):
															logger.info(f'\n\t{test_subtraction.__name__} returned integer'			)
															matop.subtract(__A							,					__A			)
					elif (np.array(__A			)).shape == (np.array(__A			)).shape:
										logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'			)
										__a				:	List[Any]			=				(np.array(__A			) - np.array(__A			)).tolist()
										__a				:	List[str]			=				matop.subtract(__A							,					__A			)
										assert theo == act
					else:
										with pytest.raises(__A			):
															logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'			)
															assert matop.subtract(__A							,					__A			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")							,					[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 						a_      (__A							,					__A			)							->	Any:



					"""simple docstring"""
					if (np.array(__A			)).shape < (2, 2) or (np.array(__A			)).shape < (2, 2):
										logger.info(f'\n\t{test_multiplication.__name__} returned integer'			)
										with pytest.raises(__A			):
															matop.add(__A							,					__A			)
					elif (np.array(__A			)).shape == (np.array(__A			)).shape:
										logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'			)
										__a				:	Dict			=				(np.matmul(__A							,					__A			)).tolist()
										__a				:	Optional[Any]			=				matop.multiply(__A							,					__A			)
										assert theo == act
					else:
										with pytest.raises(__A			):
															logger.info(
															    f'\n\t{test_multiplication.__name__} does not meet dim requirements'			)
															assert matop.subtract(__A							,					__A			)







@pytest.mark.mat_ops
def 						a_      ()							->	str:



					"""simple docstring"""
					__a				:	str			=				(3.5 * np.array(__A			)).tolist()
					__a				:	List[Any]			=				matop.scalar_multiply(__A							,					3.5			)
					assert theo == act







@pytest.mark.mat_ops
def 						a_      ()							->	Any:



					"""simple docstring"""
					__a				:	int			=				(np.identity(5			)).tolist()
					__a				:	Union[str, Any]			=				matop.identity(5			)
					assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"							,					[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]			)
def 						a_      (__A			)							->	Optional[Any]:



					"""simple docstring"""
					if (np.array(__A			)).shape < (2, 2):
										with pytest.raises(__A			):
															logger.info(f'\n\t{test_transpose.__name__} returned integer'			)
															matop.transpose(__A			)
					else:
										__a				:	str			=				(np.transpose(__A			)).tolist()
										__a				:	int			=				matop.transpose(__A							,					return_map=__A			)
										assert theo == act













import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase__			 =	logging.get_logger(__name__)

UpperCAmelCase__			 =	{
    '''facebook/musicgen-small''': '''https://huggingface.co/facebook/musicgen-small/resolve/main/config.json''',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""musicgen_decoder"""
						snake_case__   					=			["""past_key_values"""]



						def __init__(self:				str   ,				__UpperCAmelCase:				Union[str, Any]=2048   ,				__UpperCAmelCase:				Union[str, Any]=2048   ,				__UpperCAmelCase:				List[Any]=24   ,				__UpperCAmelCase:				Dict=4096   ,				__UpperCAmelCase:				int=16   ,				__UpperCAmelCase:				Dict=0.0   ,				__UpperCAmelCase:				str=True   ,				__UpperCAmelCase:				Tuple="gelu"   ,				__UpperCAmelCase:				Optional[Any]=1024   ,				__UpperCAmelCase:				Optional[Any]=0.1   ,				__UpperCAmelCase:				List[Any]=0.0   ,				__UpperCAmelCase:				int=0.0   ,				__UpperCAmelCase:				List[Any]=0.02   ,				__UpperCAmelCase:				Union[str, Any]=False   ,				__UpperCAmelCase:				Any=4   ,				__UpperCAmelCase:				Tuple=2048   ,				__UpperCAmelCase:				Tuple=2048   ,				__UpperCAmelCase:				Dict=None   ,				__UpperCAmelCase:				Dict=False   ,				**__UpperCAmelCase:				Any   ,				)						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Dict			=				vocab_size
											__a				:	List[str]			=				max_position_embeddings
											__a				:	int			=				hidden_size
											__a				:	Union[str, Any]			=				ffn_dim
											__a				:	List[Any]			=				num_hidden_layers
											__a				:	List[Any]			=				num_attention_heads
											__a				:	List[Any]			=				dropout
											__a				:	str			=				attention_dropout
											__a				:	Union[str, Any]			=				activation_dropout
											__a				:	Optional[int]			=				activation_function
											__a				:	List[str]			=				initializer_factor
											__a				:	Union[str, Any]			=				layerdrop
											__a				:	Optional[int]			=				use_cache
											__a				:	List[Any]			=				scale_embedding  # scale factor will be sqrt(d_model) if True
											__a				:	str			=				num_codebooks
											super().__init__(
											    pad_token_id=__UpperCAmelCase   ,				bos_token_id=__UpperCAmelCase   ,				eos_token_id=__UpperCAmelCase   ,				tie_word_embeddings=__UpperCAmelCase   ,				**__UpperCAmelCase   ,				)




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""musicgen"""
						snake_case__   					=			True



						def __init__(self:				Union[str, Any]   ,				**__UpperCAmelCase:				Optional[int]      )						->    List[str]:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase      )
											if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config"      )

											__a				:	List[Any]			=				kwargs.pop("text_encoder"      )
											__a				:	Optional[Any]			=				text_encoder_config.pop("model_type"      )

											__a				:	Union[str, Any]			=				kwargs.pop("audio_encoder"      )
											__a				:	Dict			=				audio_encoder_config.pop("model_type"      )

											__a				:	Optional[Any]			=				kwargs.pop("decoder"      )

											__a				:	Optional[int]			=				AutoConfig.for_model(__UpperCAmelCase   ,				**__UpperCAmelCase      )
											__a				:	int			=				AutoConfig.for_model(__UpperCAmelCase   ,				**__UpperCAmelCase      )
											__a				:	List[Any]			=				MusicgenDecoderConfig(**__UpperCAmelCase      )
											__a				:	Optional[int]			=				True



						@classmethod
						def 					UpperCAmelCase__				(cls:				Union[str, Any]   ,				__UpperCAmelCase:				PretrainedConfig   ,				__UpperCAmelCase:				PretrainedConfig   ,				__UpperCAmelCase:				MusicgenDecoderConfig   ,				**__UpperCAmelCase:				Any   ,				)						->    Tuple:




											'''simple docstring'''



											return cls(
											    text_encoder=text_encoder_config.to_dict()   ,				audio_encoder=audio_encoder_config.to_dict()   ,				decoder=decoder_config.to_dict()   ,				**__UpperCAmelCase   ,				)



						def 					UpperCAmelCase__				(self:				List[str]      )						->    int:




											'''simple docstring'''



											__a				:	Dict			=				copy.deepcopy(self.__dict__      )
											__a				:	Optional[Any]			=				self.text_encoder.to_dict()
											__a				:	Optional[Any]			=				self.audio_encoder.to_dict()
											__a				:	Tuple			=				self.decoder.to_dict()
											__a				:	List[Any]			=				self.__class__.model_type
											return output





