






'''simple docstring'''





import copy
from typing import Dict

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


_UpperCamelCase		:		Tuple								=			logging.get_logger(__name__)


class 				snake_case__     (			UpperCamelCase):
					a_       =			"semantic"




					def __init__(       self       :						List[Any]							,				_A       :						Union[str, Any]=1_00_00							,				_A       :						List[str]=True							,				_A       :						Optional[int]=7_68							,				_A       :						Optional[Any]=False							,				_A       :						Tuple=False							,				_A       :						List[Any]=False							,				_A       :						Union[str, Any]=False							,				_A       :						str=0.7							,				_A       :						str=True							,				_A       :						int=1_00_48							,				_A       :						Optional[int]=12_95_95							,				_A       :						Dict=12_95_99							,				_A       :						Tuple=1_00_00							,				_A       :						Any=2_56							,				_A       :						int=49.9							,				**_A       :						Optional[int]							,				)  ->    List[str]:
								super().__init__(
								    temperature=_A							,				do_sample=_A							,				eos_token_id=_A							,				renormalize_logits=_A							,				max_new_tokens=_A							,				output_scores=_A							,				return_dict_in_generate=_A							,				output_hidden_states=_A							,				output_attentions=_A							,				**_A							,				)

								UpperCAmelCase_     :   List[Any]       =   text_encoding_offset
								UpperCAmelCase_     :   Optional[Any]       =   text_pad_token
								UpperCAmelCase_     :   Optional[Any]       =   eos_token_id
								UpperCAmelCase_     :   int       =   semantic_infer_token
								UpperCAmelCase_     :   Union[str, Any]       =   semantic_vocab_size
								UpperCAmelCase_     :   Any       =   max_input_semantic_length
								UpperCAmelCase_     :   Dict       =   semantic_rate_hz






class 				snake_case__     (			UpperCamelCase):
					a_       =			"coarse_acoustics"




					def __init__(       self       :						Optional[Any]							,				_A       :						Union[str, Any]=True							,				_A       :						Any=False							,				_A       :						Tuple=False							,				_A       :						int=False							,				_A       :						List[Any]=False							,				_A       :						Dict=0.7							,				_A       :						int=True							,				_A       :						Optional[Any]=1_20_48							,				_A       :						Optional[int]=75							,				_A       :						Any=2							,				_A       :						Any=1_20_50							,				_A       :						str=2_56							,				_A       :						int = 6_30							,				_A       :						int = 60							,				**_A       :						str							,				)  ->    List[Any]:
								super().__init__(
								    temperature=_A							,				do_sample=_A							,				renormalize_logits=_A							,				output_scores=_A							,				return_dict_in_generate=_A							,				output_hidden_states=_A							,				output_attentions=_A							,				**_A							,				)

								UpperCAmelCase_     :   Optional[int]       =   coarse_semantic_pad_token
								UpperCAmelCase_     :   Union[str, Any]       =   coarse_rate_hz
								UpperCAmelCase_     :   str       =   n_coarse_codebooks
								UpperCAmelCase_     :   str       =   coarse_infer_token
								UpperCAmelCase_     :   Dict       =   max_coarse_input_length
								UpperCAmelCase_     :   int       =   max_coarse_history
								UpperCAmelCase_     :   Tuple       =   sliding_window_len






class 				snake_case__     (			UpperCamelCase):
					a_       =			"fine_acoustics"




					def __init__(       self       :						Dict							,				_A       :						Optional[int]=0.5							,				_A       :						List[str]=5_12							,				_A       :						List[Any]=10_24							,				_A       :						List[Any]=8							,				**_A       :						str							,				)  ->    Optional[int]:
								super().__init__(temperature=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   max_fine_history_length
								UpperCAmelCase_     :   List[Any]       =   max_fine_input_length
								UpperCAmelCase_     :   List[str]       =   n_fine_codebooks






class 				snake_case__     (			UpperCamelCase):
					a_       =			"bark"
					a_       =			True




					def __init__(       self       :						Tuple							,				_A       :						Dict = None							,				_A       :						Dict = None							,				_A       :						Dict = None							,				_A       :						Tuple=2_40_00							,				_A       :						str=10_24							,				**_A       :						Tuple							,				)  ->    Optional[Any]:
								if semantic_config is None:
											UpperCAmelCase_     :   Optional[Any]       =   {}
											logger.info('''semantic_config is None. initializing the semantic model with default values.'''     )

								if coarse_acoustics_config is None:
											UpperCAmelCase_     :   Union[str, Any]       =   {}
											logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''     )

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

								UpperCAmelCase_     :   List[str]       =   BarkSemanticGenerationConfig(**_A     )
								UpperCAmelCase_     :   Any       =   BarkCoarseGenerationConfig(**_A     )
								UpperCAmelCase_     :   int       =   BarkFineGenerationConfig(**_A     )

								UpperCAmelCase_     :   Tuple       =   sample_rate
								UpperCAmelCase_     :   int       =   codebook_size




					@classmethod
					def 						A     (       cls       :						Optional[int]							,				_A       :						BarkSemanticGenerationConfig							,				_A       :						BarkCoarseGenerationConfig							,				_A       :						BarkFineGenerationConfig							,				**_A       :						str							,				)  ->    Optional[Any]:
								return cls(
								    semantic_config=semantic_config.to_dict()							,				coarse_acoustics_config=coarse_acoustics_config.to_dict()							,				fine_acoustics_config=fine_acoustics_config.to_dict()							,				**_A							,				)





					def 						A     (       self       :						Optional[int]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   copy.deepcopy(self.__dict__     )

								UpperCAmelCase_     :   Tuple       =   self.semantic_config.to_dict()
								UpperCAmelCase_     :   str       =   self.coarse_acoustics_config.to_dict()
								UpperCAmelCase_     :   Any       =   self.fine_acoustics_config.to_dict()

								UpperCAmelCase_     :   Union[str, Any]       =   self.__class__.model_type
								return output










'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								if isinstance(image[0]							,				PIL.Image.Image     ):
											return np.stack([np.array(_A     ) for i in image]							,				axis=0     )
								elif isinstance(_A							,				torch.Tensor     ):
											return image.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								return image




					def 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								UpperCAmelCase_     :   List[Any]       =   image_processor.numpy_to_pil(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6





					def 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6


