






"""simple docstring"""






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


UpperCAmelCase_     :				Tuple         =				HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_     :				Tuple         =				parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase_     :				Tuple         =				multiprocessing.cpu_count()
UpperCAmelCase_     :				str         =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase_     :				Tuple         =				time.time()
UpperCAmelCase_     :				List[Any]         =				load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				Dict         =				ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


