
'''simple docstring'''





import numpy as np

def   A			(							A_				:       Tuple     ,				A_				:       Dict     ,				A_				:       List[Any]							):
						snake_case							:  Any			=     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						snake_case							:  int			=     np.floor(A_							)
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						snake_case							:  Tuple			=     int(n_draws - floored.sum()							)
						if need_to_add > 0:
												snake_case							:  int			=     continuous - floored
												snake_case							:  str			=     np.sort(np.unique(A_							)							)[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((snake_case),       )							:  str			=     np.where(remainder == value							)
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		snake_case							:  Dict			=     min(len(A_							)     ,				A_							)
																		snake_case							:  Optional[Any]			=     rng.choice(A_     ,				size=A_     ,				replace=A_							)
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa							)





def   A			(							A_				:       Union[str, Any]     ,				A_				:       str     ,				A_				:       Optional[Any]     ,				A_				:       List[Any]     ,				A_				:       Union[str, Any]=10							):
						snake_case,       snake_case							:  Tuple			=     np.unique(A_     ,				return_inverse=A_							)
						snake_case							:  str			=     classes.shape[0]
						snake_case							:  Optional[Any]			=     np.bincount(A_							)
						if np.min(A_							) < 2:
												raise ValueError('''Minimum class count error'''							)
						if n_train < n_classes:
												raise ValueError(
												    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)							)
						if n_test < n_classes:
												raise ValueError(
												    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)							)
						snake_case							:  List[Any]			=     np.split(np.argsort(A_     ,				kind='''mergesort'''							)     ,				np.cumsum(A_							)[:-1]							)
						for _ in range(A_							):
												snake_case							:  int			=     approximate_mode(A_     ,				A_     ,				A_							)
												snake_case							:  List[str]			=     class_counts - n_i
												snake_case							:  Dict			=     approximate_mode(A_     ,				A_     ,				A_							)

												snake_case							:  List[Any]			=     []
												snake_case							:  Union[str, Any]			=     []

												for i in range(A_							):
																		snake_case							:  Optional[int]			=     rng.permutation(class_counts[i]							)
																		snake_case							:  List[str]			=     class_indices[i].take(A_     ,				mode='''clip'''							)
																		train.extend(perm_indices_class_i[: n_i[i]]							)
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]							)
												snake_case							:  Any			=     rng.permutation(A_							)
												snake_case							:  str			=     rng.permutation(A_							)

												yield train, test



'''simple docstring'''





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     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     tie_word_embeddings=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										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'''   )

										snake_case							:  Any			=     kwargs.pop('''text_encoder'''   )
										snake_case							:  List[Any]			=     text_encoder_config.pop('''model_type'''   )

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										return cls(
										    text_encoder=text_encoder_config.to_dict(),     audio_encoder=audio_encoder_config.to_dict(),     decoder=decoder_config.to_dict(),     **SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     copy.deepcopy(self.__dict__   )
										snake_case							:  Tuple			=     self.text_encoder.to_dict()
										snake_case							:  str			=     self.audio_encoder.to_dict()
										snake_case							:  Dict			=     self.decoder.to_dict()
										snake_case							:  Optional[int]			=     self.__class__.model_type
										return output

