




'''simple docstring'''

import copy
from typing import Dict

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


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Optional[Any]    =  """semantic"""






						def __init__(   self						:						str					,   __UpperCamelCase						:						Optional[int]=1_0_0_0_0					,   __UpperCamelCase						:						Any=True					,   __UpperCamelCase						:						Any=7_6_8					,   __UpperCamelCase						:						Union[str, Any]=False					,   __UpperCamelCase						:						Union[str, Any]=False					,   __UpperCamelCase						:						List[str]=False					,   __UpperCamelCase						:						Tuple=False					,   __UpperCamelCase						:						Optional[int]=0.7					,   __UpperCamelCase						:						Union[str, Any]=True					,   __UpperCamelCase						:						str=1_0_0_4_8					,   __UpperCamelCase						:						Dict=1_2_9_5_9_5					,   __UpperCamelCase						:						Dict=1_2_9_5_9_9					,   __UpperCamelCase						:						Tuple=1_0_0_0_0					,   __UpperCamelCase						:						Optional[Any]=2_5_6					,   __UpperCamelCase						:						int=49.9					,   **__UpperCamelCase						:						Optional[int]					,   ):



									'''simple docstring'''






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

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







class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					List[str]    =  """coarse_acoustics"""






						def __init__(   self						:						List[Any]					,   __UpperCamelCase						:						Optional[Any]=True					,   __UpperCamelCase						:						str=False					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						Optional[Any]=False					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						int=0.7					,   __UpperCamelCase						:						List[Any]=True					,   __UpperCamelCase						:						Any=1_2_0_4_8					,   __UpperCamelCase						:						Dict=7_5					,   __UpperCamelCase						:						Any=2					,   __UpperCamelCase						:						List[str]=1_2_0_5_0					,   __UpperCamelCase						:						List[str]=2_5_6					,   __UpperCamelCase						:						int = 6_3_0					,   __UpperCamelCase						:						int = 6_0					,   **__UpperCamelCase						:						List[Any]					,   ):



									'''simple docstring'''






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

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







class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					List[str]    =  """fine_acoustics"""






						def __init__(   self						:						Optional[int]					,   __UpperCamelCase						:						Optional[int]=0.5					,   __UpperCamelCase						:						Union[str, Any]=5_1_2					,   __UpperCamelCase						:						Optional[Any]=1_0_2_4					,   __UpperCamelCase						:						List[Any]=8					,   **__UpperCamelCase						:						Tuple					,   ):



									'''simple docstring'''






									super().__init__(temperature=__UpperCamelCase       )

									snake_case__          =						max_fine_history_length
									snake_case__          =						max_fine_input_length
									snake_case__          =						n_fine_codebooks







class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Tuple    =  """bark"""
						UpperCAmelCase_		:					Any    =  True






						def __init__(   self						:						Optional[int]					,   __UpperCamelCase						:						Dict = None					,   __UpperCamelCase						:						Dict = None					,   __UpperCamelCase						:						Dict = None					,   __UpperCamelCase						:						str=2_4_0_0_0					,   __UpperCamelCase						:						Optional[int]=1_0_2_4					,   **__UpperCamelCase						:						str					,   ):



									'''simple docstring'''






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

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

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

									snake_case__          =						BarkSemanticGenerationConfig(**__UpperCamelCase       )
									snake_case__          =						BarkCoarseGenerationConfig(**__UpperCamelCase       )
									snake_case__          =						BarkFineGenerationConfig(**__UpperCamelCase       )

									snake_case__          =						sample_rate
									snake_case__          =						codebook_size






						@classmethod
						def       __lowerCAmelCase(   cls						:						Dict					,   __UpperCamelCase						:						BarkSemanticGenerationConfig					,   __UpperCamelCase						:						BarkCoarseGenerationConfig					,   __UpperCamelCase						:						BarkFineGenerationConfig					,   **__UpperCamelCase						:						Dict					,   ):



									'''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()					,   **__UpperCamelCase					,   )







						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						copy.deepcopy(self.__dict__       )

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

									snake_case__          =						self.__class__.model_type
									return output





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


a__         =     '''./tests/fixtures/elise_format0.mid'''





@unittest.skip("""The note-seq package currently throws an error on import"""      )
class __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Tuple    =  SpectrogramDiffusionPipeline
						UpperCAmelCase_		:					List[str]    =  PipelineTesterMixin.required_optional_params - {
						    """callback""",
						    """latents""",
						    """callback_steps""",
						    """output_type""",
						    """num_images_per_prompt""",
						}
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					List[str]    =  TOKENS_TO_AUDIO_GENERATION_PARAMS
						UpperCAmelCase_		:					Union[str, Any]    =  TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						SpectrogramNotesEncoder(
									    max_length=2_0_4_8					,   vocab_size=1_5_3_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						SpectrogramContEncoder(
									    input_dims=1_2_8					,   targets_context_length=2_5_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						TaFilmDecoder(
									    input_dims=1_2_8					,   targets_length=2_5_6					,   max_decoder_noise_time=2_00_00.0					,   d_model=7_6_8					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   dropout_rate=0.1					,   )

									snake_case__          =						DDPMScheduler()

									snake_case__          =						{
									    """notes_encoder""": notes_encoder.eval(),
									    """continuous_encoder""": continuous_encoder.eval(),
									    """decoder""": decoder.eval(),
									    """scheduler""": scheduler,
									    """melgan""": None,
									}
									return components






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						str=0       ):



									'''simple docstring'''






									if str(__UpperCamelCase       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									else:
												snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """input_tokens""": [
									        [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
									    ],
									    """generator""": generator,
									    """num_inference_steps""": 4,
									    """output_type""": """mel""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						SpectrogramDiffusionPipeline(**__UpperCamelCase       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						pipe(**__UpperCamelCase       )
									snake_case__          =						output.audios

									snake_case__          =						mel[0, -3:, -3:]

									assert mel_slice.shape == (3, 3)
									snake_case__          =						np.array(
									    [-11.51_29_25, -4.78_82_15, -0.46_17_29_05, -2.05_17_15, -10.53_91_47, -10.97_09_63, -9.09_16_34, 4.0, 4.0]       )
									assert np.abs(mel_slice.flatten() - expected_slice       ).max() < 1E-2






						@skip_mps
						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									return super().test_save_load_local()






						@skip_mps
						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									return super().test_save_load_optional_components()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									return super().test_attention_slicing_forward_pass()






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									pass







						@skip_mps
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.melgan
									snake_case__          =						None

									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									def callback(__UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						List[str]       ):
												# decode mel to audio
												snake_case__          =						melgan(input_features=mel_output.astype(np.floataa       )       )[0]
												assert len(audio[0]       ) == 8_1_9_2_0 * (step + 1)
												# simulate that audio is played
												return audio

									snake_case__          =						MidiProcessor()
									snake_case__          =						processor(__UpperCamelCase       )

									snake_case__          =						input_tokens[:3]
									snake_case__          =						torch.manual_seed(0       )
									pipe(__UpperCamelCase					,   num_inference_steps=5					,   generator=__UpperCamelCase					,   callback=__UpperCamelCase					,   output_type="""mel"""       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )
									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )
									# just run two denoising loops
									snake_case__          =						input_tokens[:2]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=2					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]

									assert abs(np.abs(__UpperCamelCase       ).sum() - 36_12.8_41       ) < 1E-1







						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )

									# just run 4 denoising loops
									snake_case__          =						input_tokens[:4]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=1_0_0					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]
									assert abs(np.abs(__UpperCamelCase       ).sum() - 93_89.11_11       ) < 5E-2