
"""simple docstring"""




from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class 		a__						:
   def __init__(      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							vocab
          lowercase   :    Optional[int]			=							normalizers.BertNormalizer(
              clean_text=_a     ,   handle_chinese_chars=_a     ,   strip_accents=_a     ,   lowercase=_a     ,   )
          try:
                 import rjieba
          except ImportError:
                 raise ImportError(
                     "You need to install rjieba to use RoFormerTokenizer. "
                     "See https://pypi.org/project/rjieba/ for installation."    )
          lowercase   :    List[str]			=							rjieba
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          lowercase   :    Dict			=							[]

          # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
          for token, start, end in self.jieba.tokenize(str(_a    )     ,   hmm=_a    ):
                 if token in self.vocab:
                        splits.append(normalized_string[start:end]    )
                 else:
                        lowercase   :    Tuple			=							self.normalizers.normalize_str(_a    ).split()
                        for token in token_list:
                               if token:
                                      lowercase   :    Tuple			=							start + len(_a    )
                                      splits.append(normalized_string[start:end]    )
                                      lowercase   :    Tuple			=							end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

          return splits

   def       __magic_name__      (      self     ,   _a    ):
          pretok.split(self.jieba_split    )




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    int			=							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       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

          lowercase   :    List[str]			=							image[0, -3:, -3:, -1]
          lowercase   :    Any			=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]    )

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

   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance


@slow
@require_torch_gpu
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


