





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 				_UpperCamelCase       (						lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       List[str]       = SpectrogramDiffusionPipeline
       _UpperCamelCase					:       List[str]       = PipelineTesterMixin.required_optional_params - {
           """callback""",
           """latents""",
           """callback_steps""",
           """output_type""",
           """num_images_per_prompt""",
       }
       _UpperCamelCase					:       List[str]       = False
       _UpperCamelCase					:       List[str]       = False
       _UpperCamelCase					:       List[Any]       = TOKENS_TO_AUDIO_GENERATION_PARAMS
       _UpperCamelCase					:       int       = TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

       def lowercase			(		self:			int		)							->			Any:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_ 							=	SpectrogramNotesEncoder(
                  max_length=2048       , vocab_size=1536       , d_model=768       , dropout_rate=0.1       , num_layers=1       , num_heads=1       , d_kv=4       , d_ff=2048       , feed_forward_proj="gated-gelu"       , )

              UpperCamelCase_ 							=	SpectrogramContEncoder(
                  input_dims=128       , targets_context_length=256       , d_model=768       , dropout_rate=0.1       , num_layers=1       , num_heads=1       , d_kv=4       , d_ff=2048       , feed_forward_proj="gated-gelu"       , )

              UpperCamelCase_ 							=	TaFilmDecoder(
                  input_dims=128       , targets_length=256       , max_decoder_noise_time=2_00_00.0       , d_model=768       , num_layers=1       , num_heads=1       , d_kv=4       , d_ff=2048       , 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 lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any=0		)							->			Optional[Any]:




              """simple docstring"""


              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_ 							=	torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_tokens": [
                      [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
                  ],
                  "generator": generator,
                  "num_inference_steps": 4,
                  "output_type": "mel",
              }
              return inputs

       def lowercase			(		self:			List[Any]		)							->			List[Any]:




              """simple docstring"""


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

              UpperCamelCase_ 							=	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	output.audios

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

              assert mel_slice.shape == (3, 3)
              UpperCamelCase_ 							=	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 lowercase			(		self:			Union[str, Any]		)							->			Optional[Any]:




              """simple docstring"""


              return super().test_save_load_local()

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




              """simple docstring"""


              return super().test_dict_tuple_outputs_equivalent()

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




              """simple docstring"""


              return super().test_save_load_optional_components()

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




              """simple docstring"""


              return super().test_attention_slicing_forward_pass()

       def lowercase			(		self:			Dict		)							->			Optional[Any]:




              """simple docstring"""


              pass

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




              """simple docstring"""


              pass



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




              """simple docstring"""


              return super().test_progress_bar()



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

       def lowercase			(		self:			List[str]		)							->			List[Any]:




              """simple docstring"""


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

       def lowercase			(		self:			int		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	torch_device

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

              UpperCamelCase_ 							=	pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              def callback(_SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Dict		):
                     # decode mel to audio
                     UpperCamelCase_ 							=	melgan(input_features=mel_output.astype(np.floataa		)		)[0]
                     assert len(audio[0]		) == 81920 * (step + 1)
                     # simulate that audio is played
                     return audio

              UpperCamelCase_ 							=	MidiProcessor()
              UpperCamelCase_ 							=	processor(_SCREAMING_SNAKE_CASE		)

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

       def lowercase			(		self:			Any		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	torch_device

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

              UpperCamelCase_ 							=	processor(_SCREAMING_SNAKE_CASE		)
              # just run two denoising loops
              UpperCamelCase_ 							=	input_tokens[:2]

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(_SCREAMING_SNAKE_CASE       , num_inference_steps=2       , generator=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	output.audios[0]

              assert abs(np.abs(_SCREAMING_SNAKE_CASE		).sum() - 36_12.8_41		) < 1e-1



       def lowercase			(		self:			Any		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	torch_device

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

              UpperCamelCase_ 							=	MidiProcessor()

              UpperCamelCase_ 							=	processor(_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(_SCREAMING_SNAKE_CASE       , num_inference_steps=100       , generator=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	output.audios[0]
              assert abs(np.abs(_SCREAMING_SNAKE_CASE		).sum() - 93_89.11_11		) < 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     =      logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase__		:
		lowerCAmelCase_							:						str							=  field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						bool							=  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.
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)



def 	lowerCAmelCase__      (  )			->				Optional[Any]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							A    =       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.
														A   ,	A   ,	A    =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
							else:
														A   ,	A   ,	A    =       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'					,   lowerCamelCase__      )

							# 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.

							A    =       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					,   )
							A    =       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=lowerCamelCase__					,   )
							A    =       AutoModelForQuestionAnswering.from_pretrained(
							    model_args.model_name_or_path					,   from_tf=bool('.ckpt' in model_args.model_name_or_path      )					,   config=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )

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

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

							# Initialize our Trainer
							A    =       Trainer(
							    model=lowerCamelCase__					,   args=lowerCamelCase__					,   train_dataset=lowerCamelCase__					,   eval_dataset=lowerCamelCase__					,   data_collator=lowerCamelCase__					,   )

							# 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 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()



