

from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin

class   _a					(			snake_case_     ,				snake_case_				):




      """simple docstring"""

      @register_to_config
      def __init__(   self							:				Any						,			UpperCAmelCase							:				int = 768						,			):
            super().__init__()

            A_						  =				nn.Parameter(torch.zeros(1						,			UpperCAmelCase	)	)
            A_						  =				nn.Parameter(torch.ones(1						,			UpperCAmelCase	)	)





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Optional[Union[str, torch.device]] = None						,			UpperCAmelCase							:				Optional[torch.dtype] = None						,			):
            A_						  =				nn.Parameter(self.mean.to(UpperCAmelCase	).to(UpperCAmelCase	)	)
            A_						  =				nn.Parameter(self.std.to(UpperCAmelCase	).to(UpperCAmelCase	)	)
            return self





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				int	):
            A_						  =				(embeds - self.mean) * 1.0 / self.std
            return embeds







      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				int	):
            A_						  =				(embeds * self.std) + self.mean
            return embeds



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 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    IFPipeline
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_BATCH_PARAMS
     __SCREAMING_SNAKE_CASE     					=    PipelineTesterMixin.required_optional_params - {'''latents'''}


     def        __snake_case(  self    ):
       return self._get_dummy_components()


     def        __snake_case(  self							,					A_							,					A_=0    ):
       if str(A_    ).startswith("""mps"""    ):
         _UpperCAmelCase		:							Tuple							=     torch.manual_seed(A_    )
       else:
         _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device=A_    ).manual_seed(A_    )

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

       return inputs


     def        __snake_case(  self    ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != """cuda"""							,					reason="""float16 requires CUDA"""    )
     def        __snake_case(  self    ):
       # 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        __snake_case(  self    ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )


     def        __snake_case(  self    ):
       self._test_save_load_local()


     def        __snake_case(  self    ):
       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        __snake_case(  self    ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


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


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


     def        __snake_case(  self    ):
       # if

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

       _UpperCAmelCase		:							Dict							=     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,_UpperCAmelCase		:							Dict							=     pipe_a.encode_prompt("""anime turtle"""							,					device="""cuda"""    )

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

       _UpperCAmelCase		:							List[Any]							=     None
       _UpperCAmelCase		:							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(A_							,					A_							,					A_							,					A_    )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       _UpperCAmelCase		:							Any							=     IFImgaImgPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, 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(A_							,					A_							,					A_							,					A_    )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       _UpperCAmelCase		:							Optional[int]							=     IFInpaintingPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, 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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

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

       _UpperCAmelCase		:							Dict							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 13 * 10**9

       _UpperCAmelCase		:							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()

       _UpperCAmelCase		:							List[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

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

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							List[Any]							=     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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

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

       _UpperCAmelCase		:							Dict							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

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

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

       _UpperCAmelCase		:							str							=     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		:							List[str]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Union[str, Any]							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[int]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					original_image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Dict							=     output.images[0]

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

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

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

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					mask_image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Optional[Any]							=     output.images[0]

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

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

       _UpperCAmelCase		:							Dict							=     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		:							str							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Tuple							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(1    )    ).to(A_    )

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

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

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

       _UpperCAmelCase		:							Tuple							=     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    a__    (		):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()





