






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'''




						A     :   List[str]						=					IFPipeline
						A     :   Optional[Any]						=					TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
						A     :   List[str]						=					TEXT_TO_IMAGE_BATCH_PARAMS
						A     :   List[Any]						=					PipelineTesterMixin.required_optional_params - {'latents'}






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return self._get_dummy_components()






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=0				) ->				int:
								if str(_SCREAMING_SNAKE_CASE				).startswith("mps"				):
										snake_case_				:							Dict						 =       torch.manual_seed(_SCREAMING_SNAKE_CASE				)
								else:
										snake_case_				:							Union[str, Any]						 =       torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(_SCREAMING_SNAKE_CASE				)

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

								return inputs






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								self._test_save_load_optional_components()






						@unittest.skipIf(torch_device != "cuda"		,				reason="float16 requires CUDA"				)
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# 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				) ->				Optional[Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								self._test_save_load_local()






						def       _lowerCAmelCase       (	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       _lowerCAmelCase       (	self				) ->				Tuple:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)




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


						'''simple docstring'''




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






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# if

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

								snake_case_				:							Optional[int]						 =       IFSuperResolutionPipeline.from_pretrained(
								    "DeepFloyd/IF-II-L-v1.0"		,				variant="fp16"		,				torch_dtype=torch.floataa		,				text_encoder=_SCREAMING_SNAKE_CASE		,				tokenizer=_SCREAMING_SNAKE_CASE				)

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to("cuda"				)

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

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

								snake_case_				:							Optional[int]						 =       None
								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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								snake_case_				:							Optional[Any]						 =       IFImgaImgPipeline(**pipe_a.components				)
								snake_case_				:							List[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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								snake_case_				:							Tuple						 =       IFInpaintingPipeline(**pipe_a.components				)
								snake_case_				:							str						 =       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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				int:
								# pipeline 1

								_start_torch_memory_measurement()

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

								snake_case_				:							Dict						 =       output.images[0]

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

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

								snake_case_				:							Optional[int]						 =       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"				)
								assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								# pipeline 2

								_start_torch_memory_measurement()

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

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

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

								snake_case_				:							str						 =       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_				:							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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								# pipeline 1

								_start_torch_memory_measurement()

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

								snake_case_				:							Any						 =       torch.Generator(device="cpu"				).manual_seed(0				)

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

								snake_case_				:							Any						 =       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_				:							List[Any]						 =       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"				)
								assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								# pipeline 2

								_start_torch_memory_measurement()

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

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

								snake_case_				:							str						 =       pipe_a(
								    prompt_embeds=_SCREAMING_SNAKE_CASE		,				negative_prompt_embeds=_SCREAMING_SNAKE_CASE		,				image=_SCREAMING_SNAKE_CASE		,				original_image=_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2		,				output_type="np"		,				)

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

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

								snake_case_				:							Tuple						 =       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								snake_case_				:							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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								# pipeline 1

								_start_torch_memory_measurement()

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

								snake_case_				:							Union[str, Any]						 =       torch.Generator(device="cpu"				).manual_seed(0				)
								snake_case_				:							int						 =       pipe_a(
								    prompt_embeds=_SCREAMING_SNAKE_CASE		,				negative_prompt_embeds=_SCREAMING_SNAKE_CASE		,				image=_SCREAMING_SNAKE_CASE		,				mask_image=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2		,				generator=_SCREAMING_SNAKE_CASE		,				output_type="np"		,				)

								snake_case_				:							Any						 =       output.images[0]

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

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

								snake_case_				:							Any						 =       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"				)
								assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								# pipeline 2

								_start_torch_memory_measurement()

								snake_case_				:							int						 =       torch.Generator(device="cpu"				).manual_seed(0				)

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

								snake_case_				:							str						 =       pipe_a(
								    prompt_embeds=_SCREAMING_SNAKE_CASE		,				negative_prompt_embeds=_SCREAMING_SNAKE_CASE		,				image=_SCREAMING_SNAKE_CASE		,				mask_image=_SCREAMING_SNAKE_CASE		,				original_image=_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				num_inference_steps=2		,				output_type="np"		,				)

								snake_case_				:							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_				:							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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		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_speech_available, is_torch_available


lowercase  :							int			  =    {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

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