






import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    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	(	A__ ,     unittest.TestCase       ):
			_lowercase		:     int        =				BertTokenizer
			_lowercase		:     str        =				BertTokenizerFast
			_lowercase		:     List[Any]        =				True
			_lowercase		:     List[Any]        =				True
			_lowercase		:     Dict        =				filter_non_english






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						super().setUp()

						SCREAMING_SNAKE_CASE							=			[
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE							=			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]))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE							=			'unwanted, running'
						return input_text, output_text






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			self.tokenizer_class(self.vocab_file)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						if not self.test_rust_tokenizer:
									return

						SCREAMING_SNAKE_CASE							=			self.get_tokenizer()
						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE							=			'UNwant\u00E9d,running'

						SCREAMING_SNAKE_CASE							=			tokenizer.tokenize(a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.tokenize(a)
						self.assertListEqual(a		,       a)

						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a		,       add_special_tokens=a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.encode(a		,       add_special_tokens=a)
						self.assertListEqual(a		,       a)

						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.encode(a)
						self.assertListEqual(a		,       a)

						# With lower casing
						SCREAMING_SNAKE_CASE							=			self.get_tokenizer(do_lower_case=a)
						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer(do_lower_case=a)

						SCREAMING_SNAKE_CASE							=			'UNwant\u00E9d,running'

						SCREAMING_SNAKE_CASE							=			tokenizer.tokenize(a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.tokenize(a)
						self.assertListEqual(a		,       a)

						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a		,       add_special_tokens=a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.encode(a		,       add_special_tokens=a)
						self.assertListEqual(a		,       a)

						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE							=			tokenizer.encode(a)
						SCREAMING_SNAKE_CASE							=			rust_tokenizer.encode(a)
						self.assertListEqual(a		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer()

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a		,       strip_accents=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a		,       strip_accents=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a		,       strip_accents=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a		,       strip_accents=a)

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer(do_lower_case=a		,       never_split=['[UNK]'])

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			BasicTokenizer()
						SCREAMING_SNAKE_CASE							=			'a\n\'ll !!to?\'d of, can\'t.'
						SCREAMING_SNAKE_CASE							=			['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
						self.assertListEqual(tokenizer.tokenize(a)		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

						SCREAMING_SNAKE_CASE							=			{}
						for i, token in enumerate(a):
									SCREAMING_SNAKE_CASE							=			i
						SCREAMING_SNAKE_CASE							=			WordpieceTokenizer(vocab=a		,       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  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						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  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						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  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						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  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			self.get_tokenizer()
						SCREAMING_SNAKE_CASE							=			self.get_rust_tokenizer()

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

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






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.tokenizer_class.from_pretrained('bert-base-uncased')

						SCREAMING_SNAKE_CASE							=			tokenizer.encode('sequence builders'		,       add_special_tokens=a)
						SCREAMING_SNAKE_CASE							=			tokenizer.encode('multi-sequence build'		,       add_special_tokens=a)

						SCREAMING_SNAKE_CASE							=			tokenizer.build_inputs_with_special_tokens(a)
						SCREAMING_SNAKE_CASE							=			tokenizer.build_inputs_with_special_tokens(a		,       a)

						assert encoded_sentence == [101] + text + [102]
						assert encoded_pair == [101] + text + [102] + text_a + [102]






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
												SCREAMING_SNAKE_CASE							=			self.rust_tokenizer_class.from_pretrained(a		,       **a)

												SCREAMING_SNAKE_CASE							=			f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
												SCREAMING_SNAKE_CASE							=			tokenizer_r.encode_plus(
												    a		,       return_attention_mask=a		,       return_token_type_ids=a		,       return_offsets_mapping=a		,       add_special_tokens=a		,       )

												SCREAMING_SNAKE_CASE							=			tokenizer_r.do_lower_case if hasattr(a		,       'do_lower_case') else False
												SCREAMING_SNAKE_CASE							=			(
												    [
												        ((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  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			['的', '人', '有']
						SCREAMING_SNAKE_CASE							=			''.join(a)
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
												SCREAMING_SNAKE_CASE							=			True
												SCREAMING_SNAKE_CASE							=			self.tokenizer_class.from_pretrained(a		,       **a)
												SCREAMING_SNAKE_CASE							=			self.rust_tokenizer_class.from_pretrained(a		,       **a)

												SCREAMING_SNAKE_CASE							=			tokenizer_p.encode(a		,       add_special_tokens=a)
												SCREAMING_SNAKE_CASE							=			tokenizer_r.encode(a		,       add_special_tokens=a)

												SCREAMING_SNAKE_CASE							=			tokenizer_r.convert_ids_to_tokens(a)
												SCREAMING_SNAKE_CASE							=			tokenizer_p.convert_ids_to_tokens(a)

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

												SCREAMING_SNAKE_CASE							=			False
												SCREAMING_SNAKE_CASE							=			self.rust_tokenizer_class.from_pretrained(a		,       **a)
												SCREAMING_SNAKE_CASE							=			self.tokenizer_class.from_pretrained(a		,       **a)

												SCREAMING_SNAKE_CASE							=			tokenizer_r.encode(a		,       add_special_tokens=a)
												SCREAMING_SNAKE_CASE							=			tokenizer_p.encode(a		,       add_special_tokens=a)

												SCREAMING_SNAKE_CASE							=			tokenizer_r.convert_ids_to_tokens(a)
												SCREAMING_SNAKE_CASE							=			tokenizer_p.convert_ids_to_tokens(a)

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








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]				)
class 		__magic_name__						(snake_case_   ,snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       StableDiffusionLatentUpscalePipeline
     __lowercase				:							List[str]	   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         'height',
         'width',
         'cross_attention_kwargs',
         'negative_prompt_embeds',
         'prompt_embeds',
     }
     __lowercase				:							List[Any]	   =       PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
     __lowercase				:							Any	   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __lowercase				:							int	   =       frozenset(
         []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __lowercase				:							List[Any]	   =       frozenset([]				)

     __lowercase				:							Any	   =       True

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= 1
       snake_case__						= 4
       snake_case__						= (16, 16)

       snake_case__						= floats_tensor((batch_size, num_channels) + sizes     ,		rng=random.Random(0   )   ).to(_a   )
       return image

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       torch.manual_seed(0   )
       snake_case__						= UNetaDConditionModel(
           act_fn='''gelu'''     ,		attention_head_dim=8     ,		norm_num_groups=_a     ,		block_out_channels=[32, 32, 64, 64]     ,		time_cond_proj_dim=1_60     ,		conv_in_kernel=1     ,		conv_out_kernel=1     ,		cross_attention_dim=32     ,		down_block_types=(
               '''KDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
           )     ,		in_channels=8     ,		mid_block_type=_a     ,		only_cross_attention=_a     ,		out_channels=5     ,		resnet_time_scale_shift='''scale_shift'''     ,		time_embedding_type='''fourier'''     ,		timestep_post_act='''gelu'''     ,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')     ,		)
       snake_case__						= AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=[
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
           ]     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
       snake_case__						= EulerDiscreteScheduler(prediction_type='''sample'''   )
       snake_case__						= CLIPTextConfig(
           bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		hidden_act='''quick_gelu'''     ,		projection_dim=5_12     ,		)
       snake_case__						= CLIPTextModel(_a   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       snake_case__						= {
           '''unet''': model.eval(),
           '''vae''': vae.eval(),
           '''scheduler''': scheduler,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }

       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:Optional[Any]     ,		_a:List[str]=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': self.dummy_image.cpu(),
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''cpu'''

       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )
       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )

       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= pipe(**_a   ).images
       snake_case__						= image[0, -3:, -3:, -1]

       self.assertEqual(image.shape     ,		(1, 2_56, 2_56, 3)   )
       snake_case__						= np.array(
           [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]   )
       snake_case__						= np.abs(image_slice.flatten() - expected_slice   ).max()
       self.assertLessEqual(_a     ,		1e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       super().test_inference_batch_single_identical(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       super().test_save_load_local(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_save_load_optional_components(expected_max_difference=3e-3   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= [
           '''DDIMScheduler''',
           '''DDPMScheduler''',
           '''PNDMScheduler''',
           '''HeunDiscreteScheduler''',
           '''EulerAncestralDiscreteScheduler''',
           '''KDPM2DiscreteScheduler''',
           '''KDPM2AncestralDiscreteScheduler''',
           '''DPMSolverSDEScheduler''',
       ]
       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=_a   )

       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= 2

       snake_case__						= []
       for scheduler_enum in KarrasDiffusionSchedulers:
         if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

         snake_case__						= getattr(_a     ,		scheduler_enum.name   )
         snake_case__						= scheduler_cls.from_config(pipe.scheduler.config   )
         snake_case__						= pipe(**_a   )[0]
         outputs.append(_a   )

       assert check_same_shape(_a   )




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





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     ,		torch_dtype=torch.floataa   )
       pipe.to('''cuda'''   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

       snake_case__						= pipe(_a     ,		generator=_a     ,		output_type='''latent'''   ).images

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''   )
       assert np.abs((expected_image - image).mean()   ) < 5e-2







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''   )

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''   )
       assert np.abs((expected_image - image).max()   ) < 5e-2

