
'''simple docstring'''



import copy

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


UpperCamelCase__ :						str    							=					logging.get_logger(__name__)

UpperCamelCase__ :						str    							=					{
    "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			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'musicgen_decoder'
       SCREAMING_SNAKE_CASE =					['past_key_values']






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






class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'musicgen'
       SCREAMING_SNAKE_CASE =					True






       def __init__(							self    ,							**A__ )      ->     Union[str, Any]:
          super().__init__(**A__ )
          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""" )

          _SCREAMING_SNAKE_CASE       	=			kwargs.pop("""text_encoder""" )
          _SCREAMING_SNAKE_CASE       	=			text_encoder_config.pop("""model_type""" )

          _SCREAMING_SNAKE_CASE       	=			kwargs.pop("""audio_encoder""" )
          _SCREAMING_SNAKE_CASE       	=			audio_encoder_config.pop("""model_type""" )

          _SCREAMING_SNAKE_CASE       	=			kwargs.pop("""decoder""" )

          _SCREAMING_SNAKE_CASE       	=			AutoConfig.for_model(A__    ,							**A__ )
          _SCREAMING_SNAKE_CASE       	=			AutoConfig.for_model(A__    ,							**A__ )
          _SCREAMING_SNAKE_CASE       	=			MusicgenDecoderConfig(**A__ )
          _SCREAMING_SNAKE_CASE       	=			True






       @classmethod
       def     UpperCamelCase			(							cls    ,							A__    ,							A__    ,							A__    ,							**A__    ,							)      ->     Any:
          return cls(
              text_encoder=text_encoder_config.to_dict()    ,							audio_encoder=audio_encoder_config.to_dict()    ,							decoder=decoder_config.to_dict()    ,							**A__    ,							)






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			copy.deepcopy(self.__dict__ )
          _SCREAMING_SNAKE_CASE       	=			self.text_encoder.to_dict()
          _SCREAMING_SNAKE_CASE       	=			self.audio_encoder.to_dict()
          _SCREAMING_SNAKE_CASE       	=			self.decoder.to_dict()
          _SCREAMING_SNAKE_CASE       	=			self.__class__.model_type
          return output








'''simple docstring'''



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(DDIMScheduler,)
       SCREAMING_SNAKE_CASE =					(('eta', 0.0), ('num_inference_steps', 50))






       def     UpperCamelCase			(							self    ,							**A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			{
              """num_train_timesteps""": 10_00,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """clip_sample""": True,
          }

          config.update(**A__ )
          return config






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(**A__ )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			10, 0.0

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter

          scheduler.set_timesteps(A__ )

          for t in scheduler.timesteps:
             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )
             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							A__ ).prev_sample

          return sample






       def     UpperCamelCase			(							self )      ->     str:
          for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(steps_offset=1 )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2] ):
             self.check_over_configs(beta_start=A__    ,							beta_end=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          self.check_over_configs(thresholding=A__ )
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=A__    ,							prediction_type=A__    ,							sample_max_value=A__    ,							)






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for t, num_inference_steps in zip([1, 10, 50]    ,							[10, 50, 5_00] ):
             self.check_over_forward(time_step=A__    ,							num_inference_steps=A__ )






       def     UpperCamelCase			(							self )      ->     str:
          for t, eta in zip([1, 10, 49]    ,							[0.0, 0.5, 1.0] ):
             self.check_over_forward(time_step=A__    ,							eta=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,							4_00 ) - 0.1_4771 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,							9_60 ) - 0.3_2460 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,							4_86 ) - 0.0_0979 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,							9_98 ) - 0.02 ) ) < 1E-5






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop()

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1E-2
          assert abs(result_mean.item() - 0.22_3967 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(prediction_type="""v_prediction""" )

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1E-2
          assert abs(result_mean.item() - 0.0684 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     List[Any]:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1E-2
          assert abs(result_mean.item() - 0.1951 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     Any:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1E-2
          assert abs(result_mean.item() - 0.1941 ) < 1E-3






