

"""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__ :	int						= """./tests/fixtures/elise_format0.mid"""


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







						def        _UpperCamelCase    (self							)			->						int:
											torch.manual_seed(0							)
											__lowercase												=			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'''     ,							)

											__lowercase												=			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'''     ,							)

											__lowercase												=			TaFilmDecoder(
											    input_dims=1_2_8     ,							targets_length=2_5_6     ,							max_decoder_noise_time=2_0_0_0_0.0     ,							d_model=7_6_8     ,							num_layers=1     ,							num_heads=1     ,							d_kv=4     ,							d_ff=2_0_4_8     ,							dropout_rate=0.1     ,							)

											__lowercase												=			DDPMScheduler()

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







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=0							)			->						int:
											if str(UpperCAmelCase_							).startswith('''mps'''							):
																__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											else:
																__lowercase												=			torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''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        _UpperCamelCase    (self							)			->						Optional[Any]:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			SpectrogramDiffusionPipeline(**UpperCAmelCase_							)
											__lowercase												=			pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			pipe(**UpperCAmelCase_							)
											__lowercase												=			output.audios

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

											assert mel_slice.shape == (3, 3)
											__lowercase												=			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        _UpperCamelCase    (self							)			->						Optional[Any]:
											return super().test_save_load_local()







						@skip_mps
						def        _UpperCamelCase    (self							)			->						List[str]:
											return super().test_dict_tuple_outputs_equivalent()







						@skip_mps
						def        _UpperCamelCase    (self							)			->						Optional[int]:
											return super().test_save_load_optional_components()







						@skip_mps
						def        _UpperCamelCase    (self							)			->						str:
											return super().test_attention_slicing_forward_pass()







						def        _UpperCamelCase    (self							)			->						Optional[Any]:
											pass







						def        _UpperCamelCase    (self							)			->						List[Any]:
											pass







						@skip_mps
						def        _UpperCamelCase    (self							)			->						Tuple:
											return super().test_progress_bar()



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







						def        _UpperCamelCase    (self							)			->						List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											# TODO - test that pipeline can decode tokens in a callback
											# so that music can be played live
											__lowercase												=			torch_device

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

											__lowercase												=			pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											def callback(UpperCAmelCase_     ,							UpperCAmelCase_							):
																# decode mel to audio
																__lowercase												=			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

											__lowercase												=			MidiProcessor()
											__lowercase												=			processor(UpperCAmelCase_							)

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







						def        _UpperCamelCase    (self							)			->						Tuple:
											__lowercase												=			torch_device

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

											__lowercase												=			processor(UpperCAmelCase_							)
											# just run two denoising loops
											__lowercase												=			input_tokens[:2]

											__lowercase												=			torch.manual_seed(0							)
											__lowercase												=			pipe(UpperCAmelCase_     ,							num_inference_steps=2     ,							generator=UpperCAmelCase_							)

											__lowercase												=			output.audios[0]

											assert abs(np.abs(UpperCAmelCase_							).sum() - 3_6_1_2.8_4_1							) < 1e-1







						def        _UpperCamelCase    (self							)			->						List[str]:
											__lowercase												=			torch_device

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

											__lowercase												=			MidiProcessor()

											__lowercase												=			processor(UpperCAmelCase_							)

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

											__lowercase												=			torch.manual_seed(0							)
											__lowercase												=			pipe(UpperCAmelCase_     ,							num_inference_steps=1_0_0     ,							generator=UpperCAmelCase_							)

											__lowercase												=			output.audios[0]
											assert abs(np.abs(UpperCAmelCase_							).sum() - 9_3_8_9.1_1_1_1							) < 5e-2





"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


a__ :	List[Any]						= logging.getLogger(__name__)


@dataclass
class 			_snake_case     :
						__snake_case 			=     field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
						__snake_case 			=     field(default=UpperCamelCase						,			metadata={'help': 'Set this flag to use fast tokenization.'}       )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,			)





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




					"""simple docstring"""



					__lowercase												=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

					if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir			)
					    and os.listdir(training_args.output_dir			)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ''' --overwrite_output_dir to overcome.'''			)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,	datefmt='''%m/%d/%Y %H:%M:%S''' ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.local_rank != -1			) ,	training_args.fpaa ,	)
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank			):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s''' ,	a__			)

					# Prepare Question-Answering task
					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__lowercase												=			AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
					__lowercase												=			AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	use_fast=a__ ,	)
					__lowercase												=			AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,	config=a__ ,	cache_dir=model_args.cache_dir ,	)

					# Get datasets
					__lowercase												=			hasattr(model.config ,	'''lang2id'''			)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir			)
					    if training_args.do_train
					    else None
					)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	mode='''dev''' ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir ,	)
					    if training_args.do_eval
					    else None
					)

					# Data collator
					__lowercase												=			DataCollatorWithPadding(a__ ,	pad_to_multiple_of=8			) if training_args.fpaa else None

					# Initialize our Trainer
					__lowercase												=			Trainer(
					    model=a__ ,	args=a__ ,	train_dataset=a__ ,	eval_dataset=a__ ,	data_collator=a__ ,	)

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir			)





def 				__UpperCamelCase   (    a__: Any			)				->			Any:




					"""simple docstring"""



					main()


if __name__ == "__main__":
							main()


