



"""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 (    lowerCAmelCase__					,  lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Dict	      =	IFPipeline
 _lowerCAmelCase     :    List[str]	      =	TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
 _lowerCAmelCase     :    Tuple	      =	TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCAmelCase     :    Optional[int]	      =	PipelineTesterMixin.required_optional_params - {"""latents"""}

 def 		_snake_case      (  self	: Optional[int]       ):
     return self._get_dummy_components()

 def 		_snake_case      (  self	: str						,  lowercase_	: Any						,  lowercase_	: List[str]=0       ):
     if str(lowercase_       ).startswith('''mps'''       ):
         snake_case_				:  Union[str, Any]       =			torch.manual_seed(lowercase_       )
     else:
         snake_case_				:  Any       =			torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

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

     return inputs

 def 		_snake_case      (  self	: Optional[int]       ):
     self._test_save_load_optional_components()

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

 def 		_snake_case      (  self	: Union[str, Any]       ):
     self._test_save_load_local()

 def 		_snake_case      (  self	: Optional[Any]       ):
     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	: List[str]       ):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )





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

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

 def 		_snake_case      (  self	: int       ):
     # if

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

     snake_case_				:  Tuple       =			IFSuperResolutionPipeline.from_pretrained(
         '''DeepFloyd/IF-II-L-v1.0'''						,  variant='''fp16'''						,  torch_dtype=torch.floataa						,  text_encoder=lowercase_						,  tokenizer=lowercase_       )

     # pre compute text embeddings and remove T5 to save memory

     pipe_a.text_encoder.to('''cuda'''       )

     snake_case_,			snake_case_				:  Any       =			pipe_a.encode_prompt('''anime turtle'''						,  device='''cuda'''       )

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

     snake_case_				:  Dict       =			None
     snake_case_				:  Optional[int]       =			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(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_       )

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # img2img

     snake_case_				:  str       =			IFImgaImgPipeline(**pipe_a.components       )
     snake_case_				:  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(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_       )

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # inpainting

     snake_case_				:  List[str]       =			IFInpaintingPipeline(**pipe_a.components       )
     snake_case_				:  Optional[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(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: str						,  lowercase_	: str						,  lowercase_	: Optional[int]						,  lowercase_	: int						,  lowercase_	: int       ):
     # pipeline 1

     _start_torch_memory_measurement()

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

     snake_case_				:  str       =			output.images[0]

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

     snake_case_				:  Union[str, Any]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 13 * 10**9

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

     # pipeline 2

     _start_torch_memory_measurement()

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

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

     snake_case_				:  Optional[int]       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  generator=lowercase_						,  num_inference_steps=2						,  output_type='''np'''						,  )

     snake_case_				:  Tuple       =			output.images[0]

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

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

     snake_case_				:  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(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: Any						,  lowercase_	: List[str]						,  lowercase_	: Dict						,  lowercase_	: List[Any]       ):
     # pipeline 1

     _start_torch_memory_measurement()

     snake_case_				:  Any       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )

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

     snake_case_				:  List[str]       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  num_inference_steps=2						,  generator=lowercase_						,  output_type='''np'''						,  )

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

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

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

     snake_case_				:  Union[str, Any]       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )

     # pipeline 2

     _start_torch_memory_measurement()

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

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

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

     snake_case_				:  int       =			output.images[0]

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

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

     snake_case_				:  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(lowercase_						,  lowercase_       )






 def 		_snake_case      (  self	: Any						,  lowercase_	: Dict						,  lowercase_	: Tuple						,  lowercase_	: Dict						,  lowercase_	: Any       ):
     # pipeline 1

     _start_torch_memory_measurement()

     snake_case_				:  Optional[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  List[str]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(1       )       ).to(lowercase_       )

     snake_case_				:  Dict       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )
     snake_case_				:  Any       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  mask_image=lowercase_						,  num_inference_steps=2						,  generator=lowercase_						,  output_type='''np'''						,  )

     snake_case_				:  int       =			output.images[0]

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

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

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

     # pipeline 2

     _start_torch_memory_measurement()

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

     snake_case_				:  Optional[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  Any       =			floats_tensor((1, 3, 256, 256)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  Optional[int]       =			floats_tensor((1, 3, 256, 256)						,  rng=random.Random(1       )       ).to(lowercase_       )

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

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

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

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

     snake_case_				:  Any       =			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(lowercase_						,  lowercase_       )





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










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





