

"""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    _UpperCAmelCase(   __snake_case				,					__snake_case				,					unittest.TestCase     ):
		lowercase__      =				IFPipeline
		lowercase__      =				TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
		lowercase__      =				TEXT_TO_IMAGE_BATCH_PARAMS
		lowercase__      =				PipelineTesterMixin.required_optional_params - {'latents'}



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






							'''simple docstring'''
							return self._get_dummy_components()



		def 						UpperCAmelCase							( self    ,		__a    ,		__a=0)	->	Tuple:






							'''simple docstring'''

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

							_UpperCamelCase										=  {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}

							return inputs



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''    ,		reason='''float16 requires CUDA''')
		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							super().test_save_load_floataa(expected_max_diff=1e-1)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)



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






							'''simple docstring'''
							self._test_save_load_local()



		def 						UpperCAmelCase							( self)	->	int:






							'''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)	->	str:






							'''simple docstring'''
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)






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



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






							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



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






							'''simple docstring'''
							_UpperCamelCase										=  IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''    ,		variant='''fp16'''    ,		torch_dtype=torch.floataa)

							_UpperCamelCase										=  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''')

							_UpperCamelCase										=  pipe_a.encode_prompt('''anime turtle'''    ,		device='''cuda''')

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

							_UpperCamelCase										=  None
							_UpperCamelCase										=  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

							_UpperCamelCase										=  IFImgaImgPipeline(**pipe_a.components)
							_UpperCamelCase										=  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

							_UpperCamelCase										=  IFInpaintingPipeline(**pipe_a.components)
							_UpperCamelCase										=  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)	->	List[str]:






							'''simple docstring'''
							_start_torch_memory_measurement()

							_UpperCamelCase										=  torch.Generator(device='''cpu''').manual_seed(0)
							_UpperCamelCase										=  pipe_a(
							    prompt_embeds=a_    ,		negative_prompt_embeds=a_    ,		num_inference_steps=2    ,		generator=a_    ,		output_type='''np'''    ,		)

							_UpperCamelCase										=  output.images[0]

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

							_UpperCamelCase										=  torch.cuda.max_memory_allocated()
							assert mem_bytes < 13 * 10**9

							_UpperCamelCase										=  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()

							_UpperCamelCase										=  torch.Generator(device='''cpu''').manual_seed(0)

							_UpperCamelCase										=  floats_tensor((1, 3, 64, 64)    ,		rng=random.Random(0)).to(a_)

							_UpperCamelCase										=  pipe_a(
							    prompt_embeds=a_    ,		negative_prompt_embeds=a_    ,		image=a_    ,		generator=a_    ,		num_inference_steps=2    ,		output_type='''np'''    ,		)

							_UpperCamelCase										=  output.images[0]

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

							_UpperCamelCase										=  torch.cuda.max_memory_allocated()
							assert mem_bytes < 4 * 10**9

							_UpperCamelCase										=  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)	->	Optional[Any]:






							'''simple docstring'''
							_start_torch_memory_measurement()

							_UpperCamelCase										=  floats_tensor((1, 3, 64, 64)    ,		rng=random.Random(0)).to(a_)

							_UpperCamelCase										=  torch.Generator(device='''cpu''').manual_seed(0)

							_UpperCamelCase										=  pipe_a(
							    prompt_embeds=a_    ,		negative_prompt_embeds=a_    ,		image=a_    ,		num_inference_steps=2    ,		generator=a_    ,		output_type='''np'''    ,		)

							_UpperCamelCase										=  output.images[0]

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

							_UpperCamelCase										=  torch.cuda.max_memory_allocated()
							assert mem_bytes < 10 * 10**9

							_UpperCamelCase										=  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()

							_UpperCamelCase										=  torch.Generator(device='''cpu''').manual_seed(0)

							_UpperCamelCase										=  floats_tensor((1, 3, 2_56, 2_56)    ,		rng=random.Random(0)).to(a_)
							_UpperCamelCase										=  floats_tensor((1, 3, 64, 64)    ,		rng=random.Random(0)).to(a_)

							_UpperCamelCase										=  pipe_a(
							    prompt_embeds=a_    ,		negative_prompt_embeds=a_    ,		image=a_    ,		original_image=a_    ,		generator=a_    ,		num_inference_steps=2    ,		output_type='''np'''    ,		)

							_UpperCamelCase										=  output.images[0]

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

							_UpperCamelCase										=  torch.cuda.max_memory_allocated()
							assert mem_bytes < 4 * 10**9

							_UpperCamelCase										=  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)	->	str:






							'''simple docstring'''
							_start_torch_memory_measurement()

							_UpperCamelCase										=  floats_tensor((1, 3, 64, 64)    ,		rng=random.Random(0)).to(a_)
							_UpperCamelCase										=  floats_tensor((1, 3, 64, 64)    ,		rng=random.Random(1)).to(a_)

							_UpperCamelCase										=  torch.Generator(device='''cpu''').manual_seed(0)
							_UpperCamelCase										=  pipe_a(
							    prompt_embeds=a_    ,		negative_prompt_embeds=a_    ,		image=a_    ,		mask_image=a_    ,		num_inference_steps=2    ,		generator=a_    ,		output_type='''np'''    ,		)

							_UpperCamelCase										=  output.images[0]

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

							_UpperCamelCase										=  torch.cuda.max_memory_allocated()
							assert mem_bytes < 10 * 10**9

							_UpperCamelCase										=  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()

							_UpperCamelCase										=  torch.Generator(device='''cpu''').manual_seed(0)

							_UpperCamelCase										=  floats_tensor((1, 3, 64, 64)    ,		rng=random.Random(0)).to(a_)
							_UpperCamelCase										=  floats_tensor((1, 3, 2_56, 2_56)    ,		rng=random.Random(0)).to(a_)
							_UpperCamelCase										=  floats_tensor((1, 3, 2_56, 2_56)    ,		rng=random.Random(1)).to(a_)

							_UpperCamelCase										=  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'''    ,		)

							_UpperCamelCase										=  output.images[0]

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

							_UpperCamelCase										=  torch.cuda.max_memory_allocated()
							assert mem_bytes < 4 * 10**9

							_UpperCamelCase										=  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 				lowerCamelCase__		(      )							->		Optional[Any]:


					"""simple docstring"""





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








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'levit'



		def __init__( self    ,		__a=2_24    ,		__a=3    ,		__a=3    ,		__a=2    ,		__a=1    ,		__a=16    ,		__a=[1_28, 2_56, 3_84]    ,		__a=[4, 8, 12]    ,		__a=[4, 4, 4]    ,		__a=[16, 16, 16]    ,		__a=0    ,		__a=[2, 2, 2]    ,		__a=[2, 2, 2]    ,		__a=0.02    ,		**__a    ,		)	->	List[str]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  kernel_size
							_UpperCamelCase										=  stride
							_UpperCamelCase										=  padding
							_UpperCamelCase										=  hidden_sizes
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  depths
							_UpperCamelCase										=  key_dim
							_UpperCamelCase										=  drop_path_rate
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  attention_ratio
							_UpperCamelCase										=  mlp_ratio
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  [
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ])




		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-4






