




'''simple docstring'''

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 						a				(   _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		BertTokenizer
				snake_case_      =		BertTokenizerFast
				snake_case_      =		True
				snake_case_      =		True
				snake_case_      =		filter_non_english


				def   A_ (		self    :					Union[str, Any]					):
									super().setUp()

									snake_case_									=				[
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									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   A_ (		self    :					Optional[Any]       ,				lowercase_    :					List[Any]					):
									snake_case_									=				'''UNwant\u00E9d,running'''
									snake_case_									=				'''unwanted, running'''
									return input_text, output_text


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				self.tokenizer_class(self.vocab_file					)

									snake_case_									=				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   A_ (		self    :					List[Any]					):
									if not self.test_rust_tokenizer:
														return

									snake_case_									=				self.get_tokenizer()
									snake_case_									=				self.get_rust_tokenizer()

									snake_case_									=				'''UNwant\u00E9d,running'''

									snake_case_									=				tokenizer.tokenize(lowercase_					)
									snake_case_									=				rust_tokenizer.tokenize(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				tokenizer.encode(lowercase_       ,				add_special_tokens=lowercase_					)
									snake_case_									=				rust_tokenizer.encode(lowercase_       ,				add_special_tokens=lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				self.get_rust_tokenizer()
									snake_case_									=				tokenizer.encode(lowercase_					)
									snake_case_									=				rust_tokenizer.encode(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									# With lower casing
									snake_case_									=				self.get_tokenizer(do_lower_case=lowercase_					)
									snake_case_									=				self.get_rust_tokenizer(do_lower_case=lowercase_					)

									snake_case_									=				'''UNwant\u00E9d,running'''

									snake_case_									=				tokenizer.tokenize(lowercase_					)
									snake_case_									=				rust_tokenizer.tokenize(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				tokenizer.encode(lowercase_       ,				add_special_tokens=lowercase_					)
									snake_case_									=				rust_tokenizer.encode(lowercase_       ,				add_special_tokens=lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				self.get_rust_tokenizer()
									snake_case_									=				tokenizer.encode(lowercase_					)
									snake_case_									=				rust_tokenizer.encode(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Any					):
									snake_case_									=				BasicTokenizer()

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


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				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   A_ (		self    :					Optional[int]					):
									snake_case_									=				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   A_ (		self    :					Tuple					):
									snake_case_									=				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   A_ (		self    :					Optional[int]					):
									snake_case_									=				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   A_ (		self    :					Optional[int]					):
									snake_case_									=				BasicTokenizer(do_lower_case=lowercase_					)

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


				def   A_ (		self    :					Dict					):
									snake_case_									=				BasicTokenizer(do_lower_case=lowercase_       ,				strip_accents=lowercase_					)

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


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				BasicTokenizer(do_lower_case=lowercase_       ,				strip_accents=lowercase_					)

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


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				BasicTokenizer(do_lower_case=lowercase_       ,				never_split=['''[UNK]''']					)

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


				def   A_ (		self    :					int					):
									snake_case_									=				BasicTokenizer()
									snake_case_									=				'''a\n\'ll !!to?\'d of, can\'t.'''
									snake_case_									=				['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
									self.assertListEqual(tokenizer.tokenize(lowercase_					)       ,				lowercase_					)


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

									snake_case_									=				{}
									for i, token in enumerate(lowercase_					):
														snake_case_									=				i
									snake_case_									=				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   A_ (		self    :					Tuple					):
									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   A_ (		self    :					str					):
									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   A_ (		self    :					Dict					):
									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   A_ (		self    :					Any					):
									snake_case_									=				self.get_tokenizer()
									snake_case_									=				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   A_ (		self    :					str					):
									snake_case_									=				self.tokenizer_class.from_pretrained('''bert-base-uncased'''					)

									snake_case_									=				tokenizer.encode('''sequence builders'''       ,				add_special_tokens=lowercase_					)
									snake_case_									=				tokenizer.encode('''multi-sequence build'''       ,				add_special_tokens=lowercase_					)

									snake_case_									=				tokenizer.build_inputs_with_special_tokens(lowercase_					)
									snake_case_									=				tokenizer.build_inputs_with_special_tokens(lowercase_       ,				lowercase_					)

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


				def   A_ (		self    :					Optional[Any]					):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"					):
																			snake_case_									=				self.rust_tokenizer_class.from_pretrained(lowercase_       ,				**lowercase_					)

																			snake_case_									=				F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
																			snake_case_									=				tokenizer_r.encode_plus(
																			    lowercase_       ,				return_attention_mask=lowercase_       ,				return_token_type_ids=lowercase_       ,				return_offsets_mapping=lowercase_       ,				add_special_tokens=lowercase_       ,				)

																			snake_case_									=				tokenizer_r.do_lower_case if hasattr(lowercase_       ,				'''do_lower_case'''					) else False
																			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   A_ (		self    :					Dict					):
									snake_case_									=				['''的''', '''人''', '''有''']
									snake_case_									=				''''''.join(lowercase_					)
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"					):
																			snake_case_									=				True
																			snake_case_									=				self.tokenizer_class.from_pretrained(lowercase_       ,				**lowercase_					)
																			snake_case_									=				self.rust_tokenizer_class.from_pretrained(lowercase_       ,				**lowercase_					)

																			snake_case_									=				tokenizer_p.encode(lowercase_       ,				add_special_tokens=lowercase_					)
																			snake_case_									=				tokenizer_r.encode(lowercase_       ,				add_special_tokens=lowercase_					)

																			snake_case_									=				tokenizer_r.convert_ids_to_tokens(lowercase_					)
																			snake_case_									=				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_					)

																			snake_case_									=				False
																			snake_case_									=				self.rust_tokenizer_class.from_pretrained(lowercase_       ,				**lowercase_					)
																			snake_case_									=				self.tokenizer_class.from_pretrained(lowercase_       ,				**lowercase_					)

																			snake_case_									=				tokenizer_r.encode(lowercase_       ,				add_special_tokens=lowercase_					)
																			snake_case_									=				tokenizer_p.encode(lowercase_       ,				add_special_tokens=lowercase_					)

																			snake_case_									=				tokenizer_r.convert_ids_to_tokens(lowercase_					)
																			snake_case_									=				tokenizer_p.convert_ids_to_tokens(lowercase_					)

																			# it is expected that only the first Chinese character is not preceded by "##".
																			snake_case_									=				[
																			    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 transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		CycleDiffusionPipeline
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				snake_case_      =		PipelineTesterMixin.required_optional_params - {"latents"}
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}     )
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS


				def   A_ (		self    :					Tuple					):
									torch.manual_seed(0					)
									snake_case_									=				UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,				layers_per_block=2       ,				sample_size=32       ,				in_channels=4       ,				out_channels=4       ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,				cross_attention_dim=32       ,				)
									snake_case_									=				DDIMScheduler(
									    beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='''scaled_linear'''       ,				num_train_timesteps=1000       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				)
									torch.manual_seed(0					)
									snake_case_									=				AutoencoderKL(
									    block_out_channels=[32, 64]       ,				in_channels=3       ,				out_channels=3       ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,				latent_channels=4       ,				)
									torch.manual_seed(0					)
									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=1000       ,				)
									snake_case_									=				CLIPTextModel(lowercase_					)
									snake_case_									=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

									snake_case_									=				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]=0					):
									snake_case_									=				floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				image / 2 + 0.5
									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									snake_case_									=				{
									    '''prompt''': '''An astronaut riding an elephant''',
									    '''source_prompt''': '''An astronaut riding a horse''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''eta''': 0.1,
									    '''strength''': 0.8,
									    '''guidance_scale''': 3,
									    '''source_guidance_scale''': 1,
									    '''output_type''': '''numpy''',
									}
									return inputs


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_									=				self.get_dummy_components()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

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


				@unittest.skipIf(torch_device != '''cuda'''       ,				'''This test requires a GPU'''					)
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.get_dummy_components()
									for name, module in components.items():
														if hasattr(lowercase_       ,				'''half'''					):
																			snake_case_									=				module.half()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

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


				@skip_mps
				def   A_ (		self    :					Optional[int]					):
									return super().test_save_load_local()


				@unittest.skip('''non-deterministic pipeline'''					)
				def   A_ (		self    :					List[Any]					):
									return super().test_inference_batch_single_identical()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_dict_tuple_outputs_equivalent()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_save_load_optional_components()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_attention_slicing_forward_pass()







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


				def   A_ (		self    :					List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(
									    lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_       ,				torch_dtype=torch.floataa       ,				revision='''fp16'''					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

									# the values aren't exactly equal, but the images look the same visually
									assert np.abs(image - expected_image					).max() < 5e-1


				def   A_ (		self    :					List[str]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

									assert np.abs(image - expected_image					).max() < 2e-2

