


'''simple docstring'''
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					List[Any]=1024			):
 __A     ,__A  :		Union[str, Any]       =			[], []

 __A  :		Optional[Any]       =			list(zip(UpperCamelCase__,				UpperCamelCase__			)			)
 __A     ,__A  :		Tuple       =			sorted_examples[0]

 def is_too_big(UpperCamelCase__       :					Optional[int]			):
  return tok(UpperCamelCase__,				return_tensors='pt'			).input_ids.shape[1] > max_tokens

 for src, tgt in tqdm(sorted_examples[1:]			):
  __A  :		Optional[int]       =			new_src + ' ' + src
  __A  :		str       =			new_tgt + ' ' + tgt
  if is_too_big(UpperCamelCase__			) or is_too_big(UpperCamelCase__			):  # cant fit, finalize example
   finished_src.append(UpperCamelCase__			)
   finished_tgt.append(UpperCamelCase__			)
   __A     ,__A  :		Optional[Any]       =			src, tgt
  else:  # can fit, keep adding
   __A     ,__A  :		str       =			cand_src, cand_tgt

    # cleanup
 if new_src:
  assert new_tgt
  finished_src.append(UpperCamelCase__			)
  finished_tgt.append(UpperCamelCase__			)
 return finished_src, finished_tgt


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Path,				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					List[str]			):
 __A  :		Optional[int]       =			Path(UpperCamelCase__			)
 save_path.mkdir(exist_ok=UpperCamelCase__			)
 for split in ["train"]:
  __A     ,__A  :		Tuple       =			data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
  __A  :		Optional[Any]       =			[x.rstrip() for x in Path(UpperCamelCase__			).open().readlines()]
  __A  :		Dict       =			[x.rstrip() for x in Path(UpperCamelCase__			).open().readlines()]
  __A     ,__A  :		Optional[int]       =			pack_examples(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  print(f"""packed {split} split from {len(UpperCamelCase__			)} examples -> {len(UpperCamelCase__			)}."""			)
  Path(save_path / f"""{split}.source"""			).open('w'			).write('\n'.join(UpperCamelCase__			)			)
  Path(save_path / f"""{split}.target"""			).open('w'			).write('\n'.join(UpperCamelCase__			)			)
 for split in ["val", "test"]:
  __A     ,__A  :		Any       =			data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
  shutil.copyfile(UpperCamelCase__,				save_path / f"""{split}.source"""			)
  shutil.copyfile(UpperCamelCase__,				save_path / f"""{split}.target"""			)


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			argparse.ArgumentParser()
 parser.add_argument('--tok_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.'			)
 parser.add_argument('--max_seq_len',				type=UpperCamelCase__,				default=128			)
 parser.add_argument('--data_dir',				type=UpperCamelCase__			)
 parser.add_argument('--save_path',				type=UpperCamelCase__			)
 __A  :		Dict       =			parser.parse_args()
 __A  :		Union[str, Any]       =			AutoTokenizer.from_pretrained(args.tok_name			)
 return pack_data_dir(UpperCamelCase__,				Path(args.data_dir			),				args.max_seq_len,				args.save_path			)


if __name__ == "__main__":
 packer_cli()










'''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 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







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


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[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  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Dict       =			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  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Union[str, Any]       =			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  :		Union[str, Any]       =			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  snake_case__   (      self						):

  """simple docstring"""







  __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  :		Tuple       =			DDPMScheduler()
  __A  :		Optional[int]       =			AudioDiffusionPipeline(vqvae=__lowercase      ,      unet=self.dummy_unet      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Tuple       =			pipe(generator=__lowercase      ,      steps=4						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		Dict       =			output.images[0]

  __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[int]       =			pipe(generator=__lowercase      ,      steps=4      ,      return_dict=__lowercase						)
  __A  :		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  :		Optional[int]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[Any]       =			np.frombuffer(image_from_tuple.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[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  :		Optional[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]      ,      )

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

  np.random.seed(0						)
  __A  :		Dict       =			np.random.uniform(-1      ,      1      ,      ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
  __A  :		Dict       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		List[Any]       =			pipe(raw_audio=__lowercase      ,      generator=__lowercase      ,      start_step=5      ,      steps=10						)
  __A  :		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]
  )
  __A  :		int       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[str]       =			np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

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

  __A  :		Any       =			self.dummy_unet_condition
  __A  :		Optional[int]       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=__lowercase      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Union[str, Any]       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			torch.rand((1, 1, 10)						)
  __A  :		Any       =			pipe(generator=__lowercase      ,      encoding=__lowercase						)
  __A  :		Union[str, Any]       =			output.images[0]
  __A  :		List[Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		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 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			torch_device

  __A  :		Union[str, Any]       =			DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Any       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[Any]       =			pipe(generator=__lowercase						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		List[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  :		Union[str, Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

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






