"""simple docstring"""
import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		MobileBertTokenizer
      a__        =		MobileBertTokenizerFast
      a__        =		True
      a__        =		True
      a__        =		filter_non_english
      a__        =		"""google/mobilebert-uncased"""



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        super().setUp()

        a__:     Optional[int]										=	[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        a__:     Optional[int]										=	os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file'])
        with open(self.vocab_file    ,			'w'    ,			encoding='utf-8') as vocab_writer:
          vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

        a__:     Dict										=	[
            (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
            for tokenizer_def in self.tokenizers_list
        ]



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     List[Any]										=	'UNwant\u00E9d,running'
        a__:     Dict										=	'unwanted, running'
        return input_text, output_text



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     int										=	self.tokenizer_class(self.vocab_file)

        a__:     Tuple										=	tokenizer.tokenize('UNwant\u00E9d,running')
        self.assertListEqual(lowercase    ,			['un', '##want', '##ed', ',', 'runn', '##ing'])
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase)    ,			[9, 6, 7, 12, 10, 11])



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     List[Any]										=	self.get_tokenizer()
        a__:     Dict										=	self.get_rust_tokenizer()

        a__:     Dict										=	'UNwant\u00E9d,running'

        a__:     List[str]										=	tokenizer.tokenize(lowercase)
        a__:     Optional[int]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     int										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     List[str]										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Dict										=	self.get_rust_tokenizer()
        a__:     str										=	tokenizer.encode(lowercase)
        a__:     Dict										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # With lower casing
        a__:     str										=	self.get_tokenizer(do_lower_case=lowercase)
        a__:     Tuple										=	self.get_rust_tokenizer(do_lower_case=lowercase)

        a__:     List[Any]										=	'UNwant\u00E9d,running'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     int										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     List[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Any										=	tokenizer.encode(lowercase)
        a__:     int										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Any										=	BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz')    ,			['ah', '\u535A', '\u63A8', 'zz'])



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	BasicTokenizer(do_lower_case=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')    ,			['hello', '!', 'how', 'are', 'you', '?'])
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[Any]										=	BasicTokenizer(do_lower_case=lowercase    ,			strip_accents=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hällo', '!', 'how', 'are', 'you', '?'])
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['h\u00E9llo'])



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Dict										=	BasicTokenizer(do_lower_case=lowercase    ,			strip_accents=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hallo', '!', 'how', 'are', 'you', '?'])
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[str]										=	BasicTokenizer(do_lower_case=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hallo', '!', 'how', 'are', 'you', '?'])
        self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     List[Any]										=	BasicTokenizer(do_lower_case=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')    ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?'])



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     str										=	BasicTokenizer(do_lower_case=lowercase    ,			strip_accents=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['HäLLo', '!', 'how', 'Are', 'yoU', '?'])



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     str										=	BasicTokenizer(do_lower_case=lowercase    ,			strip_accents=lowercase)

        self.assertListEqual(
            tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['HaLLo', '!', 'how', 'Are', 'yoU', '?'])



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Dict										=	BasicTokenizer(do_lower_case=lowercase    ,			never_split=['[UNK]'])

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]')    ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Tuple										=	['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

        a__:     List[str]										=	{}
        for i, token in enumerate(lowercase):
          a__:     List[str]										=	i
        a__:     Optional[Any]										=	WordpieceTokenizer(vocab=lowercase    ,			unk_token='[UNK]')

        self.assertListEqual(tokenizer.tokenize('')    ,			[])

        self.assertListEqual(tokenizer.tokenize('unwanted running')    ,			['un', '##want', '##ed', 'runn', '##ing'])

        self.assertListEqual(tokenizer.tokenize('unwantedX running')    ,			['[UNK]', 'runn', '##ing'])



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.assertTrue(_is_whitespace(' '))
        self.assertTrue(_is_whitespace('\t'))
        self.assertTrue(_is_whitespace('\r'))
        self.assertTrue(_is_whitespace('\n'))
        self.assertTrue(_is_whitespace('\u00A0'))

        self.assertFalse(_is_whitespace('A'))
        self.assertFalse(_is_whitespace('-'))



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        self.assertTrue(_is_control('\u0005'))

        self.assertFalse(_is_control('A'))
        self.assertFalse(_is_control(' '))
        self.assertFalse(_is_control('\t'))
        self.assertFalse(_is_control('\r'))



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertTrue(_is_punctuation('-'))
        self.assertTrue(_is_punctuation('$'))
        self.assertTrue(_is_punctuation('`'))
        self.assertTrue(_is_punctuation('.'))

        self.assertFalse(_is_punctuation('A'))
        self.assertFalse(_is_punctuation(' '))



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.get_tokenizer()
        a__:     Any										=	self.get_rust_tokenizer()

        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
        self.assertListEqual([tokenizer.tokenize(lowercase) for t in ['Test', '\xad', 'test']]    ,			[['[UNK]'], [], ['[UNK]']])

        self.assertListEqual(
            [rust_tokenizer.tokenize(lowercase) for t in ['Test', '\xad', 'test']]    ,			[['[UNK]'], [], ['[UNK]']])



      @slow
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.tokenizer_class.from_pretrained('google/mobilebert-uncased')

        a__:     List[Any]										=	tokenizer.encode('sequence builders'    ,			add_special_tokens=lowercase)
        a__:     Tuple										=	tokenizer.encode('multi-sequence build'    ,			add_special_tokens=lowercase)

        a__:     Any										=	tokenizer.build_inputs_with_special_tokens(lowercase)
        a__:     int										=	tokenizer.build_inputs_with_special_tokens(lowercase    ,			lowercase)

        assert encoded_sentence == [1_01] + text + [1_02]
        assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
            a__:     Optional[Any]										=	self.rust_tokenizer_class.from_pretrained(lowercase    ,			**lowercase)

            a__:     str										=	f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
            a__:     Optional[Any]										=	tokenizer_r.encode_plus(
                lowercase    ,			return_attention_mask=lowercase    ,			return_token_type_ids=lowercase    ,			return_offsets_mapping=lowercase    ,			add_special_tokens=lowercase    ,			)

            a__:     int										=	tokenizer_r.do_lower_case if hasattr(lowercase    ,			'do_lower_case') else False
            a__:     int										=	(
                [
                    ((0, 0), tokenizer_r.cls_token),
                    ((0, 1), 'A'),
                    ((1, 2), ','),
                    ((3, 5), 'na'),
                    ((5, 6), '##ï'),
                    ((6, 8), '##ve'),
                    ((9, 15), tokenizer_r.mask_token),
                    ((16, 21), 'Allen'),
                    ((21, 23), '##NL'),
                    ((23, 24), '##P'),
                    ((25, 33), 'sentence'),
                    ((33, 34), '.'),
                    ((0, 0), tokenizer_r.sep_token),
                ]
                if not do_lower_case
                else [
                    ((0, 0), tokenizer_r.cls_token),
                    ((0, 1), 'a'),
                    ((1, 2), ','),
                    ((3, 8), 'naive'),
                    ((9, 15), tokenizer_r.mask_token),
                    ((16, 21), 'allen'),
                    ((21, 23), '##nl'),
                    ((23, 24), '##p'),
                    ((25, 33), 'sentence'),
                    ((33, 34), '.'),
                    ((0, 0), tokenizer_r.sep_token),
                ]
            )

            self.assertEqual(
                [e[1] for e in expected_results]    ,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids']))
            self.assertEqual([e[0] for e in expected_results]    ,			tokens['offset_mapping'])



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Any										=	['的', '人', '有']
        a__:     Union[str, Any]										=	''.join(lowercase)
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
            a__:     Optional[int]										=	True
            a__:     Union[str, Any]										=	self.tokenizer_class.from_pretrained(lowercase    ,			**lowercase)
            a__:     List[Any]										=	self.rust_tokenizer_class.from_pretrained(lowercase    ,			**lowercase)

            a__:     int										=	tokenizer_p.encode(lowercase    ,			add_special_tokens=lowercase)
            a__:     Union[str, Any]										=	tokenizer_r.encode(lowercase    ,			add_special_tokens=lowercase)

            a__:     List[Any]										=	tokenizer_r.convert_ids_to_tokens(lowercase)
            a__:     List[Any]										=	tokenizer_p.convert_ids_to_tokens(lowercase)

            # it is expected that each Chinese character is not preceded by "##"
            self.assertListEqual(lowercase    ,			lowercase)
            self.assertListEqual(lowercase    ,			lowercase)

            a__:     Any										=	False
            a__:     Dict										=	self.rust_tokenizer_class.from_pretrained(lowercase    ,			**lowercase)
            a__:     List[Any]										=	self.tokenizer_class.from_pretrained(lowercase    ,			**lowercase)

            a__:     str										=	tokenizer_r.encode(lowercase    ,			add_special_tokens=lowercase)
            a__:     str										=	tokenizer_p.encode(lowercase    ,			add_special_tokens=lowercase)

            a__:     Tuple										=	tokenizer_r.convert_ids_to_tokens(lowercase)
            a__:     List[Any]										=	tokenizer_p.convert_ids_to_tokens(lowercase)

            # it is expected that only the first Chinese character is not preceded by "##".
            a__:     Dict										=	[
                f'##{token}' if idx != 0 else token for idx, token in enumerate(lowercase)
            ]
            self.assertListEqual(lowercase    ,			lowercase)
            self.assertListEqual(lowercase    ,			lowercase)



"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	MCLIPConfig(
            numDims=self.cross_attention_dim    ,			transformerDimensions=self.text_embedder_hidden_size    ,			hidden_size=self.text_embedder_hidden_size    ,			intermediate_size=37    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			vocab_size=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            'in_channels': 9,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'text_image',
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'encoder_hid_dim': self.text_embedder_hidden_size,
            'encoder_hid_dim_type': 'text_image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            'prompt': 'horse',
            'image': init_image,
            'mask_image': mask,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 2,
            'guidance_scale': 4.0,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

        print(f'image.shape {image.shape}')

        assert image.shape == (1, 64, 64, 3)

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        assert (
            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

        a__:     int										=	KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowercase)

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(lowercase    ,			lowercase)


