




"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

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







				def     a_     (							self      :    Union[str, Any]				):



					"""simple docstring"""

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







				def     a_     (							self      :    Optional[Any]				):



					"""simple docstring"""

					A_     : int		      =						StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
					A_     : List[str]		      =						sd_pipe.to(__SCREAMING_SNAKE_CASE				)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

					sd_pipe.set_scheduler('''sample_euler'''				)

					A_     : Dict		      =						'''A painting of a squirrel eating a burger'''
					A_     : Tuple		      =						torch.manual_seed(0				)
					A_     : int		      =						sd_pipe([prompt]					,   generator=__SCREAMING_SNAKE_CASE					,   guidance_scale=9.0					,   num_inference_steps=20					,   output_type='''np'''				)

					A_     : Optional[Any]		      =						output.images

					A_     : Union[str, Any]		      =						image[0, -3:, -3:, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					A_     : int		      =						np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]				)

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







				def     a_     (							self      :    Tuple				):



					"""simple docstring"""

					A_     : List[Any]		      =						StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
					A_     : Tuple		      =						sd_pipe.to(__SCREAMING_SNAKE_CASE				)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

					sd_pipe.set_scheduler('''sample_euler'''				)

					A_     : Tuple		      =						'''A painting of a squirrel eating a burger'''
					A_     : Union[str, Any]		      =						torch.manual_seed(0				)
					A_     : Tuple		      =						sd_pipe([prompt]					,   generator=__SCREAMING_SNAKE_CASE					,   guidance_scale=9.0					,   num_inference_steps=20					,   output_type='''np'''				)

					A_     : Optional[int]		      =						output.images

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

					assert image.shape == (1, 5_12, 5_12, 3)
					A_     : Dict		      =						np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]				)

					assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-1







				def     a_     (							self      :    str				):



					"""simple docstring"""

					A_     : List[Any]		      =						StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
					A_     : Any		      =						sd_pipe.to(__SCREAMING_SNAKE_CASE				)
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

					sd_pipe.set_scheduler('''sample_dpmpp_2m'''				)

					A_     : Tuple		      =						'''A painting of a squirrel eating a burger'''
					A_     : Optional[int]		      =						torch.manual_seed(0				)
					A_     : Dict		      =						sd_pipe(
					    [prompt]					,   generator=__SCREAMING_SNAKE_CASE					,   guidance_scale=7.5					,   num_inference_steps=15					,   output_type='''np'''					,   use_karras_sigmas=__SCREAMING_SNAKE_CASE					,   )

					A_     : Optional[int]		      =						output.images

					A_     : Dict		      =						image[0, -3:, -3:, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					A_     : Union[str, Any]		      =						np.array(
					    [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]				)

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













"""simple docstring"""





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class      a						(					__snake_case		):






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str = "▁"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<unk>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "</s>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<pad>"       ,						)     ->		Optional[int]:
      lowerCamelCase_         =						{
          'pad': {'id': 0, 'token': pad_token},
          'eos': {'id': 1, 'token': eos_token},
          'unk': {'id': 2, 'token': unk_token},
      }

      lowerCamelCase_         =						[None] * len(self.special_tokens					)
      for token_dict in self.special_tokens.values():
        lowerCamelCase_         =						token_dict['token']

      lowerCamelCase_         =						Tokenizer(Unigram()					)

      lowerCamelCase_         =						normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(' {2,}'					)       ,						' '					),
              normalizers.Lowercase(),
          ]					)
      lowerCamelCase_         =						pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Digits(individual_digits=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Punctuation(),
          ]					)
      lowerCamelCase_         =						decoders.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						TemplateProcessing(
          single=F'''$A {self.special_tokens["eos"]["token"]}'''       ,						special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]       ,						)

      lowerCamelCase_         =						{
          'model': 'SentencePieceUnigram',
          'replacement': replacement,
          'add_prefix_space': add_prefix_space,
      }

      super().__init__(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		Tuple:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						[files]
      self._tokenizer.train(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[Iterator[str], Iterator[Iterator[str]]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		str:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      self._tokenizer.train_from_iterator(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Dict:
      lowerCamelCase_         =						json.loads(self._tokenizer.to_str()					)

      lowerCamelCase_         =						self.special_tokens['unk']['id']

      lowerCamelCase_         =						Tokenizer.from_str(json.dumps(__SCREAMING_SNAKE_CASE					)					)




