



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 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






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



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Tuple												=					UNetaDModel(
						    sample_size=(32, 64)		,in_channels=1		,out_channels=1		,layers_per_block=2		,block_out_channels=(1_28, 1_28)		,down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')		,up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')		,)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					UNetaDConditionModel(
						    sample_size=(64, 32)		,in_channels=1		,out_channels=1		,layers_per_block=2		,block_out_channels=(1_28, 1_28)		,down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')		,up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')		,cross_attention_dim=10		,)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    int												=					AutoencoderKL(
						    sample_size=(1_28, 64)		,in_channels=1		,out_channels=1		,latent_channels=1		,layers_per_block=2		,block_out_channels=(1_28, 1_28)		,down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')		,up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')		,)
						lowerCAmelCase__					:    Dict												=					UNetaDModel(
						    sample_size=(64, 32)		,in_channels=1		,out_channels=1		,layers_per_block=2		,block_out_channels=(1_28, 1_28)		,down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')		,up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')		,)
						return vqvae, unet



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__					:    Optional[int]												=					Mel(
						    x_res=self.dummy_unet.config.sample_size[1]		,y_res=self.dummy_unet.config.sample_size[0]		,)

						lowerCAmelCase__					:    Any												=					DDPMScheduler()
						lowerCAmelCase__					:    int												=					AudioDiffusionPipeline(vqvae=__lowerCamelCase		,unet=self.dummy_unet		,mel=__lowerCamelCase		,scheduler=__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					torch.Generator(device=__lowerCamelCase			).manual_seed(42			)
						lowerCAmelCase__					:    Tuple												=					pipe(generator=__lowerCamelCase		,steps=4			)
						lowerCAmelCase__					:    Dict												=					output.audios[0]
						lowerCAmelCase__					:    Dict												=					output.images[0]

						lowerCAmelCase__					:    str												=					torch.Generator(device=__lowerCamelCase			).manual_seed(42			)
						lowerCAmelCase__					:    Any												=					pipe(generator=__lowerCamelCase		,steps=4		,return_dict=__lowerCamelCase			)
						lowerCAmelCase__					:    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]
						)
						lowerCAmelCase__					:    Tuple												=					np.frombuffer(image.tobytes()		,dtype='''uint8'''			)[:10]
						lowerCAmelCase__					:    Optional[int]												=					np.frombuffer(image_from_tuple.tobytes()		,dtype='''uint8'''			)[:10]
						lowerCAmelCase__					:    str												=					np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]			)

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

						lowerCAmelCase__					:    int												=					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]		,)

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

						np.random.seed(0			)
						lowerCAmelCase__					:    Union[str, Any]												=					np.random.uniform(-1		,1		,((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)			)
						lowerCAmelCase__					:    List[str]												=					torch.Generator(device=__lowerCamelCase			).manual_seed(42			)
						lowerCAmelCase__					:    Optional[Any]												=					pipe(raw_audio=__lowerCamelCase		,generator=__lowerCamelCase		,start_step=5		,steps=10			)
						lowerCAmelCase__					:    Tuple												=					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]
						)
						lowerCAmelCase__					:    Tuple												=					np.frombuffer(image.tobytes()		,dtype='''uint8'''			)[:10]
						lowerCAmelCase__					:    Union[str, Any]												=					np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]			)

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

						lowerCAmelCase__					:    List[Any]												=					self.dummy_unet_condition
						lowerCAmelCase__					:    Optional[Any]												=					AudioDiffusionPipeline(
						    vqvae=self.dummy_vqvae_and_unet[0]		,unet=__lowerCamelCase		,mel=__lowerCamelCase		,scheduler=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						np.random.seed(0			)
						lowerCAmelCase__					:    str												=					torch.rand((1, 1, 10)			)
						lowerCAmelCase__					:    Optional[int]												=					pipe(generator=__lowerCamelCase		,encoding=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					output.images[0]
						lowerCAmelCase__					:    Optional[int]												=					np.frombuffer(image.tobytes()		,dtype='''uint8'''			)[:10]
						lowerCAmelCase__					:    Union[str, Any]												=					np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]			)

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



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




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device

						lowerCAmelCase__					:    List[str]												=					DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''			)
						lowerCAmelCase__					:    Dict												=					pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					torch.Generator(device=__lowerCamelCase			).manual_seed(42			)
						lowerCAmelCase__					:    int												=					pipe(generator=__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					output.audios[0]
						lowerCAmelCase__					:    Union[str, 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]
						lowerCAmelCase__					:    Tuple												=					np.frombuffer(image.tobytes()		,dtype='''uint8'''			)[:10]
						lowerCAmelCase__					:    Tuple												=					np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]			)

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






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_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

				def get_lens(lowerCamelCase_		:		Tuple):
								lowerCAmelCase__					:    Tuple												=					tqdm(
								    DataLoader(lowerCamelCase_	,batch_size=512	,num_workers=8	,shuffle=lowerCamelCase_	,collate_fn=ds.collate_fn)	,desc=str(ds.len_file)	,)
								lowerCAmelCase__					:    Tuple												=					[]
								for batch in dl:
												lowerCAmelCase__					:    Dict												=					batch['''input_ids'''].ne(lowerCamelCase_).sum(1).tolist()
												lowerCAmelCase__					:    Dict												=					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

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					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)

