






"""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 						snake_case  (  unittest.TestCase	):





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




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





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


      '''simple docstring'''




      a					:      List[Any]    =       StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'      )
      a					:      Union[str, Any]    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      sd_pipe.set_scheduler('sample_euler'      )

      a					:      Any    =       'A painting of a squirrel eating a burger'
      a					:      List[Any]    =       torch.manual_seed(0      )
      a					:      Tuple    =       sd_pipe([prompt]    ,		generator=A    ,		guidance_scale=9.0    ,		num_inference_steps=2_0    ,		output_type='np'      )

      a					:      Any    =       output.images

      a					:      Optional[int]    =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a					:      str    =       np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]      )

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





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




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

      sd_pipe.set_scheduler('sample_euler'      )

      a					:      Union[str, Any]    =       'A painting of a squirrel eating a burger'
      a					:      List[Any]    =       torch.manual_seed(0      )
      a					:      Any    =       sd_pipe([prompt]    ,		generator=A    ,		guidance_scale=9.0    ,		num_inference_steps=2_0    ,		output_type='np'      )

      a					:      List[str]    =       output.images

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

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a					:      Any    =       np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]      )

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





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      str    =       StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'      )
      a					:      List[str]    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      sd_pipe.set_scheduler('sample_dpmpp_2m'      )

      a					:      int    =       'A painting of a squirrel eating a burger'
      a					:      Union[str, Any]    =       torch.manual_seed(0      )
      a					:      Tuple    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=7.5    ,		num_inference_steps=1_5    ,		output_type='np'    ,		use_karras_sigmas=A    ,		)

      a					:      Tuple    =       output.images

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

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a					:      Union[str, Any]    =       np.array(
          [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]      )

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












"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      a					:      Any    =       ['▁This', '▁is', '▁a', '▁t', 'est']
      a					:      List[Any]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['monolingual_vocab_file']      )
      with open(self.monolingual_vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        for token in vocab_tokens:
          fp.write(F'''{token} {vocab_tokens[token]}\n'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




