
'''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     __snake_case		(   __lowerCamelCase    ,   __lowerCamelCase    ,   unittest.TestCase):
					_lowerCAmelCase			     =	IFPipeline
					_lowerCAmelCase			     =	TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
					_lowerCAmelCase			     =	TEXT_TO_IMAGE_BATCH_PARAMS
					_lowerCAmelCase			     =	PipelineTesterMixin.required_optional_params - {'''latents'''}
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self._get_dummy_components()
					def     UpperCAmelCase_							(     self,    A,    A=0      ):

									"""simple docstring"""


									if str(a_      ).startswith('mps'      ):
													lowerCamelCase					:			int									= torch.manual_seed(a_      )
									else:
													lowerCamelCase					:			Any									= torch.Generator(device=a_      ).manual_seed(a_      )

									lowerCamelCase					:			List[Any]									= {
									    "prompt": "A painting of a squirrel eating a burger",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}

									return inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self._test_save_load_optional_components()
					@unittest.skipIf(torch_device != 'cuda',    reason='float16 requires CUDA'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().test_save_load_floataa(expected_max_diff=1e-1      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self._test_save_load_local()
					def     UpperCAmelCase_							(     self      ):

									"""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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )





@slow
@require_torch_gpu
class     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									lowerCamelCase					:			Optional[Any]									= IFSuperResolutionPipeline.from_pretrained(
									    'DeepFloyd/IF-II-L-v1.0',    variant='fp16',    torch_dtype=torch.floataa,    text_encoder=a_,    tokenizer=a_      )

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to('cuda'      )

									lowerCamelCase					:			List[Any]									= pipe_a.encode_prompt('anime turtle',    device='cuda'      )

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

									lowerCamelCase					:			Optional[Any]									= None
									lowerCamelCase					:			Tuple									= 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(a_,    a_,    a_,    a_      )

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									lowerCamelCase					:			Union[str, Any]									= IFImgaImgPipeline(**pipe_a.components      )
									lowerCamelCase					:			str									= 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(a_,    a_,    a_,    a_      )

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									lowerCamelCase					:			int									= IFInpaintingPipeline(**pipe_a.components      )
									lowerCamelCase					:			List[Any]									= 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(a_,    a_,    a_,    a_      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									_start_torch_memory_measurement()

									lowerCamelCase					:			List[Any]									= torch.Generator(device='cpu'      ).manual_seed(0      )
									lowerCamelCase					:			Union[str, Any]									= pipe_a(
									    prompt_embeds=a_,    negative_prompt_embeds=a_,    num_inference_steps=2,    generator=a_,    output_type='np',    )

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

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

									lowerCamelCase					:			Optional[int]									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									lowerCamelCase					:			str									= load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
									assert_mean_pixel_difference(a_,    a_      )

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCamelCase					:			List[Any]									= torch.Generator(device='cpu'      ).manual_seed(0      )

									lowerCamelCase					:			Tuple									= floats_tensor((1, 3, 64, 64),    rng=random.Random(0      )      ).to(a_      )

									lowerCamelCase					:			List[Any]									= pipe_a(
									    prompt_embeds=a_,    negative_prompt_embeds=a_,    image=a_,    generator=a_,    num_inference_steps=2,    output_type='np',    )

									lowerCamelCase					:			Dict									= output.images[0]

									assert image.shape == (256, 256, 3)

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

									lowerCamelCase					:			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(a_,    a_      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									_start_torch_memory_measurement()

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

									lowerCamelCase					:			str									= torch.Generator(device='cpu'      ).manual_seed(0      )

									lowerCamelCase					:			int									= pipe_a(
									    prompt_embeds=a_,    negative_prompt_embeds=a_,    image=a_,    num_inference_steps=2,    generator=a_,    output_type='np',    )

									lowerCamelCase					:			Union[str, Any]									= output.images[0]

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

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

									lowerCamelCase					:			Tuple									= load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
									assert_mean_pixel_difference(a_,    a_      )

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCamelCase					:			Optional[Any]									= torch.Generator(device='cpu'      ).manual_seed(0      )

									lowerCamelCase					:			List[str]									= floats_tensor((1, 3, 256, 256),    rng=random.Random(0      )      ).to(a_      )
									lowerCamelCase					:			List[str]									= floats_tensor((1, 3, 64, 64),    rng=random.Random(0      )      ).to(a_      )

									lowerCamelCase					:			List[Any]									= pipe_a(
									    prompt_embeds=a_,    negative_prompt_embeds=a_,    image=a_,    original_image=a_,    generator=a_,    num_inference_steps=2,    output_type='np',    )

									lowerCamelCase					:			Any									= output.images[0]

									assert image.shape == (256, 256, 3)

									lowerCamelCase					:			Union[str, Any]									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									lowerCamelCase					:			int									= 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(a_,    a_      )




					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									_start_torch_memory_measurement()

									lowerCamelCase					:			Any									= floats_tensor((1, 3, 64, 64),    rng=random.Random(0      )      ).to(a_      )
									lowerCamelCase					:			Union[str, Any]									= floats_tensor((1, 3, 64, 64),    rng=random.Random(1      )      ).to(a_      )

									lowerCamelCase					:			Optional[int]									= torch.Generator(device='cpu'      ).manual_seed(0      )
									lowerCamelCase					:			Any									= pipe_a(
									    prompt_embeds=a_,    negative_prompt_embeds=a_,    image=a_,    mask_image=a_,    num_inference_steps=2,    generator=a_,    output_type='np',    )

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

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

									lowerCamelCase					:			str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									lowerCamelCase					:			Optional[int]									= load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
									assert_mean_pixel_difference(a_,    a_      )

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCamelCase					:			List[str]									= torch.Generator(device='cpu'      ).manual_seed(0      )

									lowerCamelCase					:			Dict									= floats_tensor((1, 3, 64, 64),    rng=random.Random(0      )      ).to(a_      )
									lowerCamelCase					:			Any									= floats_tensor((1, 3, 256, 256),    rng=random.Random(0      )      ).to(a_      )
									lowerCamelCase					:			List[Any]									= floats_tensor((1, 3, 256, 256),    rng=random.Random(1      )      ).to(a_      )

									lowerCamelCase					:			Optional[int]									= pipe_a(
									    prompt_embeds=a_,    negative_prompt_embeds=a_,    image=a_,    mask_image=a_,    original_image=a_,    generator=a_,    num_inference_steps=2,    output_type='np',    )

									lowerCamelCase					:			Tuple									= output.images[0]

									assert image.shape == (256, 256, 3)

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

									lowerCamelCase					:			int									= 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(a_,    a_      )





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





'''simple docstring'''






import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A			      =      {
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]):
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
								    F''' reinstalling {pkg}.''')
				if not ops[op](version.parse(UpperCAmelCase__)       ,							version.parse(UpperCAmelCase__)):
								raise ImportError(
								    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Optional[str] = None):
				lowerCamelCase					:			List[Any]									= F'''\n{hint}''' if hint is not None else ''

				# non-versioned check
				if re.match(R'^[\w_\-\d]+$'       ,							UpperCAmelCase__):
								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= requirement, None, None
				else:
								lowerCamelCase					:			Optional[Any]									= re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'       ,							UpperCAmelCase__)
								if not match:
												raise ValueError(
												    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
												    F''' got {requirement}''')
								lowerCamelCase			, lowerCamelCase					:			Dict									= match[0]
								lowerCamelCase					:			Dict									= want_full.split(',')  # there could be multiple requirements
								lowerCamelCase					:			Union[str, Any]									= {}
								for w in want_range:
												lowerCamelCase					:			int									= re.findall(R'^([\s!=<>]{1,2})(.+)'       ,							UpperCAmelCase__)
												if not match:
																raise ValueError(
																    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
																    F''' but got {requirement}''')
												lowerCamelCase			, lowerCamelCase					:			List[Any]									= match[0]
												lowerCamelCase					:			Optional[int]									= want_ver
												if op not in ops:
																raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')

    # special case
				if pkg == "python":
								lowerCamelCase					:			Optional[int]									= '.'.join([str(UpperCAmelCase__) for x in sys.version_info[:3]])
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
								return

				# check if any version is installed
				try:
								lowerCamelCase					:			Any									= importlib.metadata.version(UpperCAmelCase__)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			List[str]									= 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
				return require_version(UpperCAmelCase__       ,							UpperCAmelCase__)
