
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """的""",
											    """价""",
											    """格""",
											    """是""",
											    """15""",
											    """便""",
											    """alex""",
											    """##andra""",
											    """，""",
											    """。""",
											    """-""",
											    """t""",
											    """shirt""",
											]
											SCREAMING_SNAKE_CASE__						:       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]    )    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {
											    """do_resize""": True,
											    """size""": {"""height""": 2_24, """width""": 2_24},
											    """do_center_crop""": True,
											    """crop_size""": {"""height""": 18, """width""": 18},
											    """do_normalize""": True,
											    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
											    """image_std""": [0.26862954, 0.26130258, 0.27577711],
											    """do_convert_rgb""": True,
											}
											SCREAMING_SNAKE_CASE__						:       Tuple           =   os.path.join(self.tmpdirname   ,       SCREAMING_SNAKE_CASE__    )
											with open(self.image_processor_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		json.dump(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return BertTokenizer.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return BertTokenizerFast.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   [np.random.randint(2_55   ,       size=(3, 30, 4_00)   ,       dtype=np.uinta    )]

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__   ,       0   ,       -1    )    ) for x in image_inputs]

											return image_inputs



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_image_processor()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )
											processor_slow.save_pretrained(self.tmpdirname    )
											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname   ,       use_fast=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )
											processor_fast.save_pretrained(self.tmpdirname    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname    )

											self.assertEqual(processor_slow.tokenizer.get_vocab()   ,       tokenizer_slow.get_vocab()    )
											self.assertEqual(processor_fast.tokenizer.get_vocab()   ,       tokenizer_fast.get_vocab()    )
											self.assertEqual(tokenizer_slow.get_vocab()   ,       tokenizer_fast.get_vocab()    )
											self.assertIsInstance(processor_slow.tokenizer   ,       SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(processor_fast.tokenizer   ,       SCREAMING_SNAKE_CASE__    )

											self.assertEqual(processor_slow.image_processor.to_json_string()   ,       image_processor.to_json_string()    )
											self.assertEqual(processor_fast.image_processor.to_json_string()   ,       image_processor.to_json_string()    )
											self.assertIsInstance(processor_slow.image_processor   ,       SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(processor_fast.image_processor   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()   ,       image_processor=self.get_image_processor()    )
											processor.save_pretrained(self.tmpdirname    )

											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_tokenizer(cls_token="""(CLS)"""   ,       sep_token="""(SEP)"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname   ,       cls_token="""(CLS)"""   ,       sep_token="""(SEP)"""   ,       do_normalize=SCREAMING_SNAKE_CASE__    )

											self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()    )
											self.assertIsInstance(processor.tokenizer   ,       SCREAMING_SNAKE_CASE__    )

											self.assertEqual(processor.image_processor.to_json_string()   ,       image_processor_add_kwargs.to_json_string()    )
											self.assertIsInstance(processor.image_processor   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()   ,       input_processor[key].sum()   ,       delta=1E-2    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """Alexandra，T-shirt的价格是15便士。"""

											SCREAMING_SNAKE_CASE__						:       Tuple           =   processor(text=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       str           =   tokenizer(SCREAMING_SNAKE_CASE__    )

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """Alexandra，T-shirt的价格是15便士。"""
											SCREAMING_SNAKE_CASE__						:       Any           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(list(inputs.keys()    )   ,       ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(SCREAMING_SNAKE_CASE__    ):
																		processor()



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											SCREAMING_SNAKE_CASE__						:       Any           =   processor.batch_decode(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       str           =   """Alexandra，T-shirt的价格是15便士。"""
											SCREAMING_SNAKE_CASE__						:       str           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(list(inputs.keys()    )   ,       processor.model_input_names    )






"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCAmelCase_						(a__ ,					a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   IFPipeline
				__UpperCamelCase      :						Dict					       =   TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
				__UpperCamelCase      :						Any					       =   TEXT_TO_IMAGE_BATCH_PARAMS
				__UpperCamelCase      :						Union[str, Any]					       =   PipelineTesterMixin.required_optional_params - {'''latents'''}



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



											"""simple docstring"""




											return self._get_dummy_components()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=0    )					->		List[Any]:



											"""simple docstring"""




											if str(SCREAMING_SNAKE_CASE__    ).startswith("""mps"""    ):
																		SCREAMING_SNAKE_CASE__						:       Dict           =   torch.manual_seed(SCREAMING_SNAKE_CASE__    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Any           =   torch.Generator(device=SCREAMING_SNAKE_CASE__    ).manual_seed(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """output_type""": """numpy""",
											}

											return inputs



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != """cuda"""   ,       reason="""float16 requires CUDA"""    )
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											super().test_save_load_floataa(expected_max_diff=1E-1    )



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



											"""simple docstring"""




											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											self._test_save_load_local()



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2   ,       )



				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()   ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,       )
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )






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


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""   ,       variant="""fp16"""   ,       torch_dtype=torch.floataa    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   IFSuperResolutionPipeline.from_pretrained(
											    """DeepFloyd/IF-II-L-v1.0"""   ,       variant="""fp16"""   ,       torch_dtype=torch.floataa   ,       text_encoder=SCREAMING_SNAKE_CASE__   ,       tokenizer=SCREAMING_SNAKE_CASE__    )

											# pre compute text embeddings and remove T5 to save memory

											pipe_a.text_encoder.to("""cuda"""    )

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   pipe_a.encode_prompt("""anime turtle"""   ,       device="""cuda"""    )

											del pipe_a.tokenizer
											del pipe_a.text_encoder
											gc.collect()

											SCREAMING_SNAKE_CASE__						:       List[str]           =   None
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   None

											pipe_a.enable_model_cpu_offload()
											pipe_a.enable_model_cpu_offload()

											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

											self._test_if(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											pipe_a.remove_all_hooks()
											pipe_a.remove_all_hooks()

											# img2img

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   IFImgaImgPipeline(**pipe_a.components    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   IFImgaImgSuperResolutionPipeline(**pipe_a.components    )

											pipe_a.enable_model_cpu_offload()
											pipe_a.enable_model_cpu_offload()

											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

											self._test_if_imgaimg(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											pipe_a.remove_all_hooks()
											pipe_a.remove_all_hooks()

											# inpainting

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   IFInpaintingPipeline(**pipe_a.components    )
											SCREAMING_SNAKE_CASE__						:       int           =   IFInpaintingSuperResolutionPipeline(**pipe_a.components    )

											pipe_a.enable_model_cpu_offload()
											pipe_a.enable_model_cpu_offload()

											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
											pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

											self._test_if_inpainting(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       int           =   output.images[0]

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

											SCREAMING_SNAKE_CASE__						:       Any           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 13 * 10**9

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# pipeline 2

											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       int           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       output_type="""np"""   ,       )

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

											assert image.shape == (2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 4 * 10**9

											SCREAMING_SNAKE_CASE__						:       int           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       int           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Any           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   output.images[0]

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

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 10 * 10**9

											SCREAMING_SNAKE_CASE__						:       str           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# pipeline 2

											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   floats_tensor((1, 3, 2_56, 2_56)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       original_image=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   output.images[0]

											assert image.shape == (2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 4 * 10**9

											SCREAMING_SNAKE_CASE__						:       str           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(1    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   output.images[0]

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

											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 10 * 10**9

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											# pipeline 2

											_start_torch_memory_measurement()

											SCREAMING_SNAKE_CASE__						:       int           =   torch.Generator(device="""cpu"""    ).manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   floats_tensor((1, 3, 2_56, 2_56)   ,       rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   floats_tensor((1, 3, 2_56, 2_56)   ,       rng=random.Random(1    )    ).to(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   pipe_a(
											    prompt_embeds=SCREAMING_SNAKE_CASE__   ,       negative_prompt_embeds=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       original_image=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=2   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   output.images[0]

											assert image.shape == (2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.cuda.max_memory_allocated()
											assert mem_bytes < 4 * 10**9

											SCREAMING_SNAKE_CASE__						:       Any           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""    )
											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )






def lowercase_  (   ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()




