'''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 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     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 lowerCAmelCase     (		self    ):
         return self._get_dummy_components()







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=0    ):
         if str(_lowerCamelCase    ).startswith('mps'    ):
             __UpperCamelCase	:	Union[str, Any]		    =  torch.manual_seed(_lowerCamelCase    )
         else:
             __UpperCamelCase	:	Optional[Any]		    =  torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )

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

         return inputs







     def lowerCAmelCase     (		self    ):
         self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != 'cuda'     ,			reason='float16 requires CUDA'    )
     def lowerCAmelCase     (		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 lowerCAmelCase     (		self    ):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )







     def lowerCAmelCase     (		self    ):
         self._test_save_load_local()







     def lowerCAmelCase     (		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 lowerCAmelCase     (		self    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )






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







     """simple docstring"""






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







     def lowerCAmelCase     (		self    ):
         # if

         __UpperCamelCase	:	Tuple		    =  IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'     ,			variant='fp16'     ,			torch_dtype=torch.floataa    )

         __UpperCamelCase	:	List[str]		    =  IFSuperResolutionPipeline.from_pretrained(
             'DeepFloyd/IF-II-L-v1.0'     ,			variant='fp16'     ,			torch_dtype=torch.floataa     ,			text_encoder=_lowerCamelCase     ,			tokenizer=_lowerCamelCase    )

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('cuda'    )

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

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

         __UpperCamelCase	:	int		    =  None
         __UpperCamelCase	:	List[str]		    =  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(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         __UpperCamelCase	:	Union[str, 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(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         __UpperCamelCase	:	List[Any]		    =  IFInpaintingPipeline(**pipe_a.components    )
         __UpperCamelCase	:	Dict		    =  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(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         # pipeline 1

         _start_torch_memory_measurement()

         __UpperCamelCase	:	Dict		    =  torch.Generator(device='cpu'    ).manual_seed(0    )
         __UpperCamelCase	:	List[str]		    =  pipe_a(
             prompt_embeds=_lowerCamelCase     ,			negative_prompt_embeds=_lowerCamelCase     ,			num_inference_steps=2     ,			generator=_lowerCamelCase     ,			output_type='np'     ,			)

         __UpperCamelCase	:	Any		    =  output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         __UpperCamelCase	:	List[Any]		    =  torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_3 * 1_0**9

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

         # pipeline 2

         _start_torch_memory_measurement()

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

         __UpperCamelCase	:	Optional[Any]		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  pipe_a(
             prompt_embeds=_lowerCamelCase     ,			negative_prompt_embeds=_lowerCamelCase     ,			image=_lowerCamelCase     ,			generator=_lowerCamelCase     ,			num_inference_steps=2     ,			output_type='np'     ,			)

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

         assert image.shape == (2_5_6, 2_5_6, 3)

         __UpperCamelCase	:	Optional[int]		    =  torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         __UpperCamelCase	:	List[str]		    =  load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'    )
         assert_mean_pixel_difference(_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         # pipeline 1

         _start_torch_memory_measurement()

         __UpperCamelCase	:	List[Any]		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )

         __UpperCamelCase	:	Optional[int]		    =  torch.Generator(device='cpu'    ).manual_seed(0    )

         __UpperCamelCase	:	Tuple		    =  pipe_a(
             prompt_embeds=_lowerCamelCase     ,			negative_prompt_embeds=_lowerCamelCase     ,			image=_lowerCamelCase     ,			num_inference_steps=2     ,			generator=_lowerCamelCase     ,			output_type='np'     ,			)

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

         assert image.shape == (6_4, 6_4, 3)

         __UpperCamelCase	:	Optional[Any]		    =  torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         __UpperCamelCase	:	Optional[int]		    =  load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'    )
         assert_mean_pixel_difference(_lowerCamelCase     ,			_lowerCamelCase    )

         # pipeline 2

         _start_torch_memory_measurement()

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

         __UpperCamelCase	:	int		    =  floats_tensor((1, 3, 2_5_6, 2_5_6)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )

         __UpperCamelCase	:	Dict		    =  pipe_a(
             prompt_embeds=_lowerCamelCase     ,			negative_prompt_embeds=_lowerCamelCase     ,			image=_lowerCamelCase     ,			original_image=_lowerCamelCase     ,			generator=_lowerCamelCase     ,			num_inference_steps=2     ,			output_type='np'     ,			)

         __UpperCamelCase	:	Tuple		    =  output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         __UpperCamelCase	:	List[Any]		    =  torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         __UpperCamelCase	:	Any		    =  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(_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         # pipeline 1

         _start_torch_memory_measurement()

         __UpperCamelCase	:	Union[str, Any]		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(1    )    ).to(_lowerCamelCase    )

         __UpperCamelCase	:	List[Any]		    =  torch.Generator(device='cpu'    ).manual_seed(0    )
         __UpperCamelCase	:	List[str]		    =  pipe_a(
             prompt_embeds=_lowerCamelCase     ,			negative_prompt_embeds=_lowerCamelCase     ,			image=_lowerCamelCase     ,			mask_image=_lowerCamelCase     ,			num_inference_steps=2     ,			generator=_lowerCamelCase     ,			output_type='np'     ,			)

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

         assert image.shape == (6_4, 6_4, 3)

         __UpperCamelCase	:	Tuple		    =  torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         __UpperCamelCase	:	List[str]		    =  load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'    )
         assert_mean_pixel_difference(_lowerCamelCase     ,			_lowerCamelCase    )

         # pipeline 2

         _start_torch_memory_measurement()

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

         __UpperCamelCase	:	List[Any]		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  floats_tensor((1, 3, 2_5_6, 2_5_6)     ,			rng=random.Random(0    )    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  floats_tensor((1, 3, 2_5_6, 2_5_6)     ,			rng=random.Random(1    )    ).to(_lowerCamelCase    )

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

         __UpperCamelCase	:	Any		    =  output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         __UpperCamelCase	:	Dict		    =  torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         __UpperCamelCase	:	List[str]		    =  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(_lowerCamelCase     ,			_lowerCamelCase    )







def  _UpperCamelCase (     ):




    """simple docstring"""



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




'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url







@pytest.mark.parametrize('repo_id'				,     ['canonical_dataset_name', 'org-name/dataset-name']			)
@pytest.mark.parametrize('path'				,     ['filename.csv', 'filename with blanks.csv']			)
@pytest.mark.parametrize('revision'				,     [None, 'v2']			)
def  _UpperCamelCase (     _a	:	Any				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  hf_hub_url(repo_id=_a				,     path=_a				,     revision=_a			)
    assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_a			)}"""



