
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				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					IFPipeline
	snake_case_    =					TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
	snake_case_    =					TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case_    =					PipelineTesterMixin.required_optional_params - {"latents"}






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return self._get_dummy_components()






	def     UpperCamelCase_  (	self :			int							,A :			Any							,A :			Optional[Any]=0			):
					if str(A			).startswith("mps"			):
									__A             =			torch.manual_seed(A			)
					else:
									__A             =			torch.Generator(device=A			).manual_seed(A			)

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

					return inputs






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					self._test_save_load_optional_components()






	@unittest.skipIf(torch_device != "cuda"							,reason="float16 requires CUDA"			)
	def     UpperCamelCase_  (	self :			List[str]			):
					# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
					super().test_save_load_floataa(expected_max_diff=1E-1			)






	def     UpperCamelCase_  (	self :			Tuple			):
					self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)






	def     UpperCamelCase_  (	self :			int			):
					self._test_save_load_local()






	def     UpperCamelCase_  (	self :			Dict			):
					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 :			List[str]			):
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





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




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			str			):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def     UpperCamelCase_  (	self :			Any			):
					# if

					__A             =			IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"							,variant="fp16"							,torch_dtype=torch.floataa			)

					__A             =			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"			)

					__A      ,							__A             =			pipe_a.encode_prompt("anime turtle"							,device="cuda"			)

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

					__A             =			None
					__A             =			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

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

					__A             =			IFInpaintingPipeline(**pipe_a.components			)
					__A             =			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 :			List[Any]							,A :			Any							,A :			Any							,A :			Dict							,A :			List[Any]			):
					# pipeline 1

					_start_torch_memory_measurement()

					__A             =			torch.Generator(device="cpu"			).manual_seed(0			)
					__A             =			pipe_a(
					    prompt_embeds=A							,negative_prompt_embeds=A							,num_inference_steps=2							,generator=A							,output_type="np"							,)

					__A             =			output.images[0]

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

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

					__A             =			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()

					__A             =			torch.Generator(device="cpu"			).manual_seed(0			)

					__A             =			floats_tensor((1, 3, 64, 64)							,rng=random.Random(0			)			).to(A			)

					__A             =			pipe_a(
					    prompt_embeds=A							,negative_prompt_embeds=A							,image=A							,generator=A							,num_inference_steps=2							,output_type="np"							,)

					__A             =			output.images[0]

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

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

					__A             =			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 :			Optional[Any]							,A :			List[str]							,A :			List[Any]							,A :			Any							,A :			int			):
					# pipeline 1

					_start_torch_memory_measurement()

					__A             =			floats_tensor((1, 3, 64, 64)							,rng=random.Random(0			)			).to(A			)

					__A             =			torch.Generator(device="cpu"			).manual_seed(0			)

					__A             =			pipe_a(
					    prompt_embeds=A							,negative_prompt_embeds=A							,image=A							,num_inference_steps=2							,generator=A							,output_type="np"							,)

					__A             =			output.images[0]

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

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

					__A             =			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()

					__A             =			torch.Generator(device="cpu"			).manual_seed(0			)

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

					__A             =			pipe_a(
					    prompt_embeds=A							,negative_prompt_embeds=A							,image=A							,original_image=A							,generator=A							,num_inference_steps=2							,output_type="np"							,)

					__A             =			output.images[0]

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

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

					__A             =			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 :			Union[str, Any]							,A :			str							,A :			str							,A :			Dict							,A :			Tuple			):
					# pipeline 1

					_start_torch_memory_measurement()

					__A             =			floats_tensor((1, 3, 64, 64)							,rng=random.Random(0			)			).to(A			)
					__A             =			floats_tensor((1, 3, 64, 64)							,rng=random.Random(1			)			).to(A			)

					__A             =			torch.Generator(device="cpu"			).manual_seed(0			)
					__A             =			pipe_a(
					    prompt_embeds=A							,negative_prompt_embeds=A							,image=A							,mask_image=A							,num_inference_steps=2							,generator=A							,output_type="np"							,)

					__A             =			output.images[0]

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

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

					__A             =			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()

					__A             =			torch.Generator(device="cpu"			).manual_seed(0			)

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

					__A             =			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"							,)

					__A             =			output.images[0]

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

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

					__A             =			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      (     )		->     Any:







				"""simple docstring"""



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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:int										=    {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Dict										=    ['EncoderDecoderModel']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int										=    ['TFEncoderDecoderModel']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Optional[int]										=    ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
					from .configuration_encoder_decoder import EncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encoder_decoder import EncoderDecoderModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
