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 			lowerCamelCase_  (     __UpperCAmelCase		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Dict      			=   BertTokenizer
    a__			:		List[Any]      			=   BertTokenizerFast
    a__			:		Union[str, Any]      			=   True
    a__			:		Union[str, Any]      			=   True
    a__			:		Optional[int]      			=   filter_non_english







    def     UpperCamelCase__     (			self) ->			Tuple:
        super().setUp()

        __UpperCamelCase  :List[Any]    						=	[
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        __UpperCamelCase  :List[str]    						=	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     UpperCamelCase__     (			self				,				__lowercase) ->			int:
        __UpperCamelCase  :Dict    						=	'''UNwant\u00E9d,running'''
        __UpperCamelCase  :Any    						=	'''unwanted, running'''
        return input_text, output_text







    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Tuple    						=	self.tokenizer_class(self.vocab_file)

        __UpperCamelCase  :Optional[int]    						=	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     UpperCamelCase__     (			self) ->			Tuple:

        if not self.test_rust_tokenizer:
            return

        __UpperCamelCase  :Tuple    						=	self.get_tokenizer()
        __UpperCamelCase  :Union[str, Any]    						=	self.get_rust_tokenizer()

        __UpperCamelCase  :Any    						=	'''UNwant\u00E9d,running'''

        __UpperCamelCase  :int    						=	tokenizer.tokenize(__lowercase)
        __UpperCamelCase  :int    						=	rust_tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)

        __UpperCamelCase  :Dict    						=	tokenizer.encode(__lowercase				,				add_special_tokens=__lowercase)
        __UpperCamelCase  :List[Any]    						=	rust_tokenizer.encode(__lowercase				,				add_special_tokens=__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)

        __UpperCamelCase  :List[str]    						=	self.get_rust_tokenizer()
        __UpperCamelCase  :List[str]    						=	tokenizer.encode(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	rust_tokenizer.encode(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)

        # With lower casing
        __UpperCamelCase  :Dict    						=	self.get_tokenizer(do_lower_case=__lowercase)
        __UpperCamelCase  :Any    						=	self.get_rust_tokenizer(do_lower_case=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	'''UNwant\u00E9d,running'''

        __UpperCamelCase  :Dict    						=	tokenizer.tokenize(__lowercase)
        __UpperCamelCase  :Any    						=	rust_tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	tokenizer.encode(__lowercase				,				add_special_tokens=__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	rust_tokenizer.encode(__lowercase				,				add_special_tokens=__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)

        __UpperCamelCase  :Dict    						=	self.get_rust_tokenizer()
        __UpperCamelCase  :str    						=	tokenizer.encode(__lowercase)
        __UpperCamelCase  :int    						=	rust_tokenizer.encode(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Optional[int]    						=	BasicTokenizer()

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







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Optional[int]    						=	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     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Optional[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     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[str]    						=	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     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Any    						=	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     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :str    						=	BasicTokenizer(do_lower_case=__lowercase)

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







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Any    						=	BasicTokenizer(do_lower_case=__lowercase				,				strip_accents=__lowercase)

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







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[str]    						=	BasicTokenizer(do_lower_case=__lowercase				,				strip_accents=__lowercase)

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







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :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     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :Optional[int]    						=	BasicTokenizer()
        __UpperCamelCase  :Tuple    						=	'''a\n\'ll !!to?\'d of, can\'t.'''
        __UpperCamelCase  :Union[str, Any]    						=	['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
        self.assertListEqual(tokenizer.tokenize(__lowercase)				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

        __UpperCamelCase  :Optional[int]    						=	{}
        for i, token in enumerate(__lowercase):
            __UpperCamelCase  :Optional[Any]    						=	i
        __UpperCamelCase  :List[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     UpperCamelCase__     (			self) ->			Dict:
        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     UpperCamelCase__     (			self) ->			Optional[int]:
        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     UpperCamelCase__     (			self) ->			Tuple:
        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     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Tuple    						=	self.get_tokenizer()
        __UpperCamelCase  :Optional[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     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :int    						=	self.tokenizer_class.from_pretrained('''bert-base-uncased''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer.encode('''sequence builders'''				,				add_special_tokens=__lowercase)
        __UpperCamelCase  :Any    						=	tokenizer.encode('''multi-sequence build'''				,				add_special_tokens=__lowercase)

        __UpperCamelCase  :Any    						=	tokenizer.build_inputs_with_special_tokens(__lowercase)
        __UpperCamelCase  :Tuple    						=	tokenizer.build_inputs_with_special_tokens(__lowercase				,				__lowercase)

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







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

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase  :int    						=	self.rust_tokenizer_class.from_pretrained(__lowercase				,				**__lowercase)

                __UpperCamelCase  :Optional[Any]    						=	f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                __UpperCamelCase  :Optional[int]    						=	tokenizer_r.encode_plus(
                    __lowercase				,				return_attention_mask=__lowercase				,				return_token_type_ids=__lowercase				,				return_offsets_mapping=__lowercase				,				add_special_tokens=__lowercase				,				)

                __UpperCamelCase  :Optional[int]    						=	tokenizer_r.do_lower_case if hasattr(__lowercase				,				'''do_lower_case''') else False
                __UpperCamelCase  :Optional[Any]    						=	(
                    [
                        ((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     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Tuple    						=	['''的''', '''人''', '''有''']
        __UpperCamelCase  :Any    						=	''''''.join(__lowercase)
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase  :Union[str, Any]    						=	True
                __UpperCamelCase  :Optional[int]    						=	self.tokenizer_class.from_pretrained(__lowercase				,				**__lowercase)
                __UpperCamelCase  :Any    						=	self.rust_tokenizer_class.from_pretrained(__lowercase				,				**__lowercase)

                __UpperCamelCase  :Any    						=	tokenizer_p.encode(__lowercase				,				add_special_tokens=__lowercase)
                __UpperCamelCase  :Optional[Any]    						=	tokenizer_r.encode(__lowercase				,				add_special_tokens=__lowercase)

                __UpperCamelCase  :Any    						=	tokenizer_r.convert_ids_to_tokens(__lowercase)
                __UpperCamelCase  :Tuple    						=	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)

                __UpperCamelCase  :Tuple    						=	False
                __UpperCamelCase  :Tuple    						=	self.rust_tokenizer_class.from_pretrained(__lowercase				,				**__lowercase)
                __UpperCamelCase  :Optional[int]    						=	self.tokenizer_class.from_pretrained(__lowercase				,				**__lowercase)

                __UpperCamelCase  :Optional[Any]    						=	tokenizer_r.encode(__lowercase				,				add_special_tokens=__lowercase)
                __UpperCamelCase  :Any    						=	tokenizer_p.encode(__lowercase				,				add_special_tokens=__lowercase)

                __UpperCamelCase  :int    						=	tokenizer_r.convert_ids_to_tokens(__lowercase)
                __UpperCamelCase  :str    						=	tokenizer_p.convert_ids_to_tokens(__lowercase)

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






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()


class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   StableUnCLIPImgaImgPipeline
    a__			:		Optional[Any]      			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__			:		str      			=   frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__			:		List[Any]      			=   frozenset([]				)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	32
        __UpperCamelCase  :int    						=	embedder_hidden_size

        # image encoding components

        __UpperCamelCase  :Dict    						=	CLIPImageProcessor(crop_size=32				,				size=32)

        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase				,				projection_dim=__lowercase				,				num_hidden_layers=5				,				num_attention_heads=4				,				image_size=32				,				intermediate_size=37				,				patch_size=1				,				))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase  :Any    						=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0				,				eos_token_id=2				,				hidden_size=__lowercase				,				projection_dim=32				,				intermediate_size=37				,				layer_norm_eps=1E-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				))

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	UNetaDConditionModel(
            sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,				up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,				block_out_channels=(32, 64)				,				attention_head_dim=(2, 4)				,				class_embed_type='''projection'''				,				projection_class_embeddings_input_dim=embedder_projection_dim * 2				,				cross_attention_dim=__lowercase				,				layers_per_block=1				,				upcast_attention=__lowercase				,				use_linear_projection=__lowercase				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Dict    						=	DDIMScheduler(
            beta_schedule='''scaled_linear'''				,				beta_start=0.0_00_85				,				beta_end=0.0_12				,				prediction_type='''v_prediction'''				,				set_alpha_to_one=__lowercase				,				steps_offset=1				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	AutoencoderKL()

        __UpperCamelCase  :Dict    						=	{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0				,				__lowercase=True) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :int    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :int    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Tuple    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase  :Tuple    						=	input_image * 0.5 + 0.5
            __UpperCamelCase  :Any    						=	input_image.clamp(0				,				1)
            __UpperCamelCase  :Any    						=	input_image.cpu().permute(0				,				2				,				3				,				1).float().numpy()
            __UpperCamelCase  :int    						=	DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Dict    						=	self.get_dummy_components()
        __UpperCamelCase  :Dict    						=	StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase  :Any    						=	sd_pipe(**__lowercase).images
        __UpperCamelCase  :int    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


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






    '''simple docstring'''






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







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Optional[int]    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :str    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Tuple    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :int    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :int    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase  :Any    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        __UpperCamelCase  :Any    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Tuple    						=	pipe(
            __lowercase				,				'''anime turtle'''				,				num_inference_steps=2				,				output_type='''np'''				,				)

        __UpperCamelCase  :str    						=	torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9





