



'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       Any		):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							@property
							def 							__lowercase			(							self					:       int		):
									torch.manual_seed(0		)
									_a     :      int        =							UNetaDModel(
									    sample_size=(32, 64)	,in_channels=1	,out_channels=1	,layers_per_block=2	,block_out_channels=(128, 128)	,down_block_types=('AttnDownBlock2D', 'DownBlock2D')	,up_block_types=('UpBlock2D', 'AttnUpBlock2D')	,)
									return model







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									torch.manual_seed(0		)
									_a     :      Any        =							UNetaDConditionModel(
									    sample_size=(64, 32)	,in_channels=1	,out_channels=1	,layers_per_block=2	,block_out_channels=(128, 128)	,down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')	,up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')	,cross_attention_dim=10	,)
									return model







							@property
							def 							__lowercase			(							self					:       Any		):
									torch.manual_seed(0		)
									_a     :      List[str]        =							AutoencoderKL(
									    sample_size=(128, 64)	,in_channels=1	,out_channels=1	,latent_channels=1	,layers_per_block=2	,block_out_channels=(128, 128)	,down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')	,up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')	,)
									_a     :      str        =							UNetaDModel(
									    sample_size=(64, 32)	,in_channels=1	,out_channels=1	,layers_per_block=2	,block_out_channels=(128, 128)	,down_block_types=('AttnDownBlock2D', 'DownBlock2D')	,up_block_types=('UpBlock2D', 'AttnUpBlock2D')	,)
									return vqvae, unet







							@slow
							def 							__lowercase			(							self					:       Any		):
									_a     :      str        =							'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a     :      int        =							Mel(
									    x_res=self.dummy_unet.config.sample_size[1]	,y_res=self.dummy_unet.config.sample_size[0]	,)

									_a     :      int        =							DDPMScheduler()
									_a     :      List[str]        =							AudioDiffusionPipeline(vqvae=_UpperCAmelCase	,unet=self.dummy_unet	,mel=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									_a     :      List[Any]        =							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a     :      Dict        =							torch.Generator(device=_UpperCAmelCase		).manual_seed(42		)
									_a     :      List[Any]        =							pipe(generator=_UpperCAmelCase	,steps=4		)
									_a     :      int        =							output.audios[0]
									_a     :      Tuple        =							output.images[0]

									_a     :      Any        =							torch.Generator(device=_UpperCAmelCase		).manual_seed(42		)
									_a     :      Dict        =							pipe(generator=_UpperCAmelCase	,steps=4	,return_dict=_UpperCAmelCase		)
									_a     :      Optional[int]        =							output[0][0]

									assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
									assert (
									    image.height == self.dummy_unet.config.sample_size[0]
									    and image.width == self.dummy_unet.config.sample_size[1]
									)
									_a     :      Dict        =							np.frombuffer(image.tobytes()	,dtype='uint8'		)[:10]
									_a     :      Dict        =							np.frombuffer(image_from_tuple.tobytes()	,dtype='uint8'		)[:10]
									_a     :      List[Any]        =							np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() == 0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() == 0

									_a     :      Any        =							Mel(
									    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]	,y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]	,)

									_a     :      List[Any]        =							DDIMScheduler()
									_a     :      List[Any]        =							self.dummy_vqvae_and_unet
									_a     :      Optional[int]        =							AudioDiffusionPipeline(
									    vqvae=self.dummy_vqvae_and_unet[0]	,unet=dummy_vqvae_and_unet[1]	,mel=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									_a     :      Optional[Any]        =							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									np.random.seed(0		)
									_a     :      Union[str, Any]        =							np.random.uniform(-1	,1	,((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
									_a     :      List[Any]        =							torch.Generator(device=_UpperCAmelCase		).manual_seed(42		)
									_a     :      Optional[int]        =							pipe(raw_audio=_UpperCAmelCase	,generator=_UpperCAmelCase	,start_step=5	,steps=10		)
									_a     :      Dict        =							output.images[0]

									assert (
									    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
									    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
									)
									_a     :      str        =							np.frombuffer(image.tobytes()	,dtype='uint8'		)[:10]
									_a     :      Any        =							np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

									_a     :      Tuple        =							self.dummy_unet_condition
									_a     :      List[Any]        =							AudioDiffusionPipeline(
									    vqvae=self.dummy_vqvae_and_unet[0]	,unet=_UpperCAmelCase	,mel=_UpperCAmelCase	,scheduler=_UpperCAmelCase		)
									_a     :      int        =							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									np.random.seed(0		)
									_a     :      Tuple        =							torch.rand((1, 1, 10)		)
									_a     :      Optional[Any]        =							pipe(generator=_UpperCAmelCase	,encoding=_UpperCAmelCase		)
									_a     :      int        =							output.images[0]
									_a     :      Any        =							np.frombuffer(image.tobytes()	,dtype='uint8'		)[:10]
									_a     :      Optional[int]        =							np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

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







							def 							__lowercase			(							self					:       Dict		):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 							__lowercase			(							self					:       int		):
									_a     :      Tuple        =							torch_device

									_a     :      Tuple        =							DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'		)
									_a     :      List[Any]        =							pipe.to(_UpperCAmelCase		)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

									_a     :      Tuple        =							torch.Generator(device=_UpperCAmelCase		).manual_seed(42		)
									_a     :      Optional[int]        =							pipe(generator=_UpperCAmelCase		)
									_a     :      List[Any]        =							output.audios[0]
									_a     :      Optional[Any]        =							output.images[0]

									assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
									assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
									_a     :      int        =							np.frombuffer(image.tobytes()	,dtype='uint8'		)[:10]
									_a     :      int        =							np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() == 0









'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




