



import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast





def 				A      (	_UpperCAmelCase							:  Union[str, Any]				,  _UpperCAmelCase							:  Union[str, Any]=100				,  _UpperCAmelCase							:  Any=" "       )							->					List[str]:







			'''simple docstring'''
			_UpperCAmelCase									=  text.split(_UpperCAmelCase       )
			return [character.join(text[i : i + n]       ).strip() for i in range(0				,  len(_UpperCAmelCase       )				,  _UpperCAmelCase       )]





def 				A      (	_UpperCAmelCase							:  Union[str, Any]       )							->					Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase			,	_UpperCAmelCase									=  [], []
			for title, text in zip(documents['title']				,  documents['text']       ):
						if text is not None:
									for passage in split_text(_UpperCAmelCase       ):
												titles.append(title if title is not None else ''       )
												texts.append(_UpperCAmelCase       )
			return {"title": titles, "text": texts}





def 				A      (	_UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Optional[int]				,  _UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Union[str, Any]       )							->					Tuple:







			'''simple docstring'''
			_UpperCAmelCase									=  load_dataset(
			    'csv'				,  data_files=[csv_path]				,  split='train'				,  delimiter='\t'				,  column_names=['title', 'text']       )
			_UpperCAmelCase									=  kb_dataset.map(
			    _UpperCAmelCase				,  batched=_UpperCAmelCase				,  num_proc=1       )  # if you want you can load already splitted csv.
			_UpperCAmelCase									=  [kb_dataset.shard(_UpperCAmelCase				,  _UpperCAmelCase				,  contiguous=_UpperCAmelCase       ) for i in range(_UpperCAmelCase       )]
			_UpperCAmelCase									=  kb_list[process_num]

			_UpperCAmelCase									=  'data_' + str(_UpperCAmelCase       )
			_UpperCAmelCase									=  os.path.join(_UpperCAmelCase				,  _UpperCAmelCase       )

			_UpperCAmelCase									=  DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'       )
			_UpperCAmelCase									=  ctx_encoder.to(device=_UpperCAmelCase       )

			def embed(
			    _UpperCAmelCase							:  dict				,  _UpperCAmelCase							:  DPRContextEncoder				,  _UpperCAmelCase							:  DPRContextEncoderTokenizerFast				,  _UpperCAmelCase							:  Tuple       ) -> dict:
						_UpperCAmelCase									=  ctx_tokenizer(
						    documents['title']				,  documents['text']				,  truncation=_UpperCAmelCase				,  padding='longest'				,  return_tensors='pt'       )['input_ids']
						_UpperCAmelCase									=  ctx_encoder(input_ids.to(device=_UpperCAmelCase       )				,  return_dict=_UpperCAmelCase       ).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}

			_UpperCAmelCase									=  Features(
			    {'text': Value('string'       ), 'title': Value('string'       ), 'embeddings': Sequence(Value('float32'       )       )}       )  # optional, save as float32 instead of float64 to save space

			_UpperCAmelCase									=  data_shrad.map(
			    partial(_UpperCAmelCase				,  ctx_encoder=_UpperCAmelCase				,  ctx_tokenizer=_UpperCAmelCase				,  device=_UpperCAmelCase       )				,  batched=_UpperCAmelCase				,  batch_size=16				,  features=_UpperCAmelCase				,  )
			dataset.save_to_disk(_UpperCAmelCase       )





def 				A      (	_UpperCAmelCase							:  Optional[Any]				,  _UpperCAmelCase							:  Union[str, Any]       )							->					Dict:







			'''simple docstring'''
			_UpperCAmelCase									=  []

			for shard_address in glob(str(_UpperCAmelCase       ) + '/*/'       ):
						data_shard_list.append(load_from_disk(_UpperCAmelCase       )       )

			_UpperCAmelCase									=  concatenate_datasets(_UpperCAmelCase       )
			faiss.omp_set_num_threads(96       )

			_UpperCAmelCase									=  faiss.IndexHNSWFlat(768				,  128				,  faiss.METRIC_INNER_PRODUCT       )
			concat.add_faiss_index('embeddings'				,  custom_index=_UpperCAmelCase       )
			concat.get_index('embeddings'       ).save(
			    _UpperCAmelCase       )  # since we load the index in to memory,we can directly update the index in the disk





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()


UpperCAmelCase__  =			"./tests/fixtures/elise_format0.mid"







@unittest.skip('''The note-seq package currently throws an error on import'''		)
class      __lowerCAmelCase       (     A							,		unittest.TestCase		):
			UpperCamelCase     =	SpectrogramDiffusionPipeline
			UpperCamelCase     =	PipelineTesterMixin.required_optional_params - {
			    '''callback''',
			    '''latents''',
			    '''callback_steps''',
			    '''output_type''',
			    '''num_images_per_prompt''',
			}
			UpperCamelCase     =	False
			UpperCamelCase     =	False
			UpperCamelCase     =	TOKENS_TO_AUDIO_GENERATION_PARAMS
			UpperCamelCase     =	TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS



			def        _lowerCamelCase			(		self  :    str)  ->       Any:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase									=  SpectrogramNotesEncoder(
						    max_length=20_48					,						vocab_size=15_36					,						d_model=7_68					,						dropout_rate=0.1					,						num_layers=1					,						num_heads=1					,						d_kv=4					,						d_ff=20_48					,						feed_forward_proj='gated-gelu'					,						)

						_UpperCAmelCase									=  SpectrogramContEncoder(
						    input_dims=1_28					,						targets_context_length=2_56					,						d_model=7_68					,						dropout_rate=0.1					,						num_layers=1					,						num_heads=1					,						d_kv=4					,						d_ff=20_48					,						feed_forward_proj='gated-gelu'					,						)

						_UpperCAmelCase									=  TaFilmDecoder(
						    input_dims=1_28					,						targets_length=2_56					,						max_decoder_noise_time=2_0_0_0_0.0					,						d_model=7_68					,						num_layers=1					,						num_heads=1					,						d_kv=4					,						d_ff=20_48					,						dropout_rate=0.1					,						)

						_UpperCAmelCase									=  DDPMScheduler()

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



			def        _lowerCamelCase			(		self  :    Optional[Any]					,						A  :    str					,						A  :    Union[str, Any]=0)  ->       Optional[Any]:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase									=  torch.manual_seed(A)
						else:
									_UpperCAmelCase									=  torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase									=  {
						    'input_tokens': [
						        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
						    ],
						    'generator': generator,
						    'num_inference_steps': 4,
						    'output_type': 'mel',
						}
						return inputs



			def        _lowerCamelCase			(		self  :    str)  ->       Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase									=  'cpu'  # ensure determinism for the device-dependent torch.Generator
						_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  SpectrogramDiffusionPipeline(**A)
						_UpperCAmelCase									=  pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase									=  self.get_dummy_inputs(A)
						_UpperCAmelCase									=  pipe(**A)
						_UpperCAmelCase									=  output.audios

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

						assert mel_slice.shape == (3, 3)
						_UpperCAmelCase									=  np.array(
						    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0])
						assert np.abs(mel_slice.flatten() - expected_slice).max() < 1E-2



			@skip_mps
			def        _lowerCamelCase			(		self  :    Optional[Any])  ->       Optional[Any]:



						"""simple docstring"""






						return super().test_save_load_local()



			@skip_mps
			def        _lowerCamelCase			(		self  :    Optional[Any])  ->       List[str]:



						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()



			@skip_mps
			def        _lowerCamelCase			(		self  :    Optional[Any])  ->       Dict:



						"""simple docstring"""






						return super().test_save_load_optional_components()



			@skip_mps
			def        _lowerCamelCase			(		self  :    Union[str, Any])  ->       List[str]:



						"""simple docstring"""






						return super().test_attention_slicing_forward_pass()



			def        _lowerCamelCase			(		self  :    List[Any])  ->       Dict:



						"""simple docstring"""






						pass



			def        _lowerCamelCase			(		self  :    str)  ->       Any:



						"""simple docstring"""






						pass



			@skip_mps
			def        _lowerCamelCase			(		self  :    Optional[int])  ->       int:



						"""simple docstring"""






						return super().test_progress_bar()







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



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



						"""simple docstring"""






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



			def        _lowerCamelCase			(		self  :    int)  ->       Tuple:



						"""simple docstring"""






						_UpperCAmelCase									=  torch_device

						_UpperCAmelCase									=  SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
						_UpperCAmelCase									=  pipe.melgan
						_UpperCAmelCase									=  None

						_UpperCAmelCase									=  pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						def callback(A  :    Dict					,						A  :    Optional[int]):
									# decode mel to audio
									_UpperCAmelCase									=  melgan(input_features=mel_output.astype(np.floataa))[0]
									assert len(audio[0]) == 8_19_20 * (step + 1)
									# simulate that audio is played
									return audio

						_UpperCAmelCase									=  MidiProcessor()
						_UpperCAmelCase									=  processor(A)

						_UpperCAmelCase									=  input_tokens[:3]
						_UpperCAmelCase									=  torch.manual_seed(0)
						pipe(A					,						num_inference_steps=5					,						generator=A					,						callback=A					,						output_type='mel')



			def        _lowerCamelCase			(		self  :    str)  ->       Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase									=  torch_device

						_UpperCAmelCase									=  SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
						_UpperCAmelCase									=  pipe.to(A)
						pipe.set_progress_bar_config(disable=A)
						_UpperCAmelCase									=  MidiProcessor()

						_UpperCAmelCase									=  processor(A)
						# just run two denoising loops
						_UpperCAmelCase									=  input_tokens[:2]

						_UpperCAmelCase									=  torch.manual_seed(0)
						_UpperCAmelCase									=  pipe(A					,						num_inference_steps=2					,						generator=A)

						_UpperCAmelCase									=  output.audios[0]

						assert abs(np.abs(A).sum() - 3_6_1_2.8_4_1) < 1E-1



			def        _lowerCamelCase			(		self  :    List[str])  ->       Tuple:



						"""simple docstring"""






						_UpperCAmelCase									=  torch_device

						_UpperCAmelCase									=  SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
						_UpperCAmelCase									=  pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase									=  MidiProcessor()

						_UpperCAmelCase									=  processor(A)

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

						_UpperCAmelCase									=  torch.manual_seed(0)
						_UpperCAmelCase									=  pipe(A					,						num_inference_steps=1_00					,						generator=A)

						_UpperCAmelCase									=  output.audios[0]
						assert abs(np.abs(A).sum() - 9_3_8_9.1_1_1_1) < 5E-2
