




'''simple docstring'''
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		eos_token_id=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		max_new_tokens=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						text_encoding_offset
           __lowercase							=						text_pad_token
           __lowercase							=						eos_token_id
           __lowercase							=						semantic_infer_token
           __lowercase							=						semantic_vocab_size
           __lowercase							=						max_input_semantic_length
           __lowercase							=						semantic_rate_hz






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						coarse_semantic_pad_token
           __lowercase							=						coarse_rate_hz
           __lowercase							=						n_coarse_codebooks
           __lowercase							=						coarse_infer_token
           __lowercase							=						max_coarse_input_length
           __lowercase							=						max_coarse_history
           __lowercase							=						sliding_window_len






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     Dict    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """simple docstring"""



           if semantic_config is None:
               __lowercase							=						{}
               logger.info("""semantic_config is None. initializing the semantic model with default values."""     )

           if coarse_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""     )

           if fine_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""     )

           __lowercase							=						BarkSemanticGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineGenerationConfig(**lowerCamelCase__     )

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """simple docstring"""



           return cls(
               semantic_config=semantic_config.to_dict()					,		coarse_acoustics_config=coarse_acoustics_config.to_dict()					,		fine_acoustics_config=fine_acoustics_config.to_dict()					,		**lowerCamelCase__					,		)




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )

           __lowercase							=						self.semantic_config.to_dict()
           __lowercase							=						self.coarse_acoustics_config.to_dict()
           __lowercase							=						self.fine_acoustics_config.to_dict()

           __lowercase							=						self.__class__.model_type
           return output










'''simple docstring'''
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					(HeunDiscreteScheduler,)
       snake_case_ :     Any    =					10

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					int:

           """simple docstring"""



           __lowercase							=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**lowerCamelCase__     )
           return config

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,		[0.00_02, 0.0_02, 0.02]     ):
               self.check_over_configs(beta_start=lowerCamelCase__					,		beta_end=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCamelCase__					,		clip_sample=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config(prediction_type="""v_prediction"""     )
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if str(lowerCamelCase__     ).startswith("""cpu"""     ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           elif str(lowerCamelCase__     ).startswith("""mps"""     ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.00_02     ) < 1E-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__					,		use_karras_sigmas=lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           assert abs(result_sum.item() - 0.0_00_15     ) < 1E-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7     ) < 1E-2




