

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				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     IFPipeline
			_UpperCAmelCase   :							Tuple										=     TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
			_UpperCAmelCase   :							Any										=     TEXT_TO_IMAGE_BATCH_PARAMS
			_UpperCAmelCase   :							Union[str, Any]										=     PipelineTesterMixin.required_optional_params - {'''latents'''}



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self._get_dummy_components()



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_: int  		=       torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

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

									return inputs



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_save_load_optional_components()



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



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



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







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




			"""simple docstring"""







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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									# if

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

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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")

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       pipe_a.encode_prompt("anime turtle"   ,						device="cuda")

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

									SCREAMING_SNAKE_CASE_: Any  		=       None
									SCREAMING_SNAKE_CASE_: 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(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									SCREAMING_SNAKE_CASE_: Any  		=       IFImgaImgPipeline(**pipe_a.components)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFInpaintingPipeline(**pipe_a.components)
									SCREAMING_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(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Generator(device="cpu").manual_seed(0)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       output.images[0]

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

									SCREAMING_SNAKE_CASE_: Any  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									SCREAMING_SNAKE_CASE_: Any  		=       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()

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

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

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

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

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

									SCREAMING_SNAKE_CASE_: Dict  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int]):
									# pipeline 1

									_start_torch_memory_measurement()

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

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

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

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

									SCREAMING_SNAKE_CASE_: 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()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						original_image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Any  		=       output.images[0]

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

									SCREAMING_SNAKE_CASE_: Dict  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_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(lowerCAmelCase__   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									# pipeline 1

									_start_torch_memory_measurement()

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

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Generator(device="cpu").manual_seed(0)
									SCREAMING_SNAKE_CASE_: Tuple  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						mask_image=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: int  		=       output.images[0]

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

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       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()

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.Generator(device="cpu").manual_seed(0)

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

									SCREAMING_SNAKE_CASE_: Any  		=       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"   ,						)

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

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

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

									SCREAMING_SNAKE_CASE_: List[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(lowerCAmelCase__   ,						lowerCAmelCase__)







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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




