


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 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	int          =     IFPipeline
			lowerCAmelCase :	Optional[Any]          =     TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
			lowerCAmelCase :	str          =     TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCAmelCase :	str          =     PipelineTesterMixin.required_optional_params - {"latents"}




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[Any]:



					'''simple docstring'''







					return self._get_dummy_components()




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Optional[Any]=0 )   ->Any:



					'''simple docstring'''







					if str(lowerCamelCase__ ).startswith("mps" ):
							_UpperCAmelCase		:       Tuple								=      torch.manual_seed(lowerCamelCase__ )
					else:
							_UpperCAmelCase		:       int								=      torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )

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

					return inputs




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != "cuda"	,  reason="float16 requires CUDA" )
			def 							lowerCAmelCase__    (		self  :     List[str] )   ->List[str]:



					'''simple docstring'''







					super().test_save_load_floataa(expected_max_diff=1E-1 )




			def 							lowerCAmelCase__    (		self  :     Any )   ->Dict:



					'''simple docstring'''







					self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					self._test_save_load_local()




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->str:



					'''simple docstring'''







					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  :     Dict )   ->Tuple:



					'''simple docstring'''







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )






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




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Optional[Any]:



					'''simple docstring'''







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->str:



					'''simple docstring'''







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

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

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

					_UpperCAmelCase		:       Any								=      None
					_UpperCAmelCase		:       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(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

					pipe_a.remove_all_hooks()
					pipe_a.remove_all_hooks()

					# img2img

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

					pipe_a.remove_all_hooks()
					pipe_a.remove_all_hooks()

					# inpainting

					_UpperCAmelCase		:       Any								=      IFInpaintingPipeline(**pipe_a.components )
					_UpperCAmelCase		:       List[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 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     str )   ->List[str]:



					'''simple docstring'''







					_start_torch_memory_measurement()

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

					_UpperCAmelCase		:       Dict								=      output.images[0]

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

					_UpperCAmelCase		:       Dict								=      torch.cuda.max_memory_allocated()
					assert mem_bytes < 13 * 10**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		:       str								=      torch.Generator(device="cpu" ).manual_seed(0 )

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

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

					_UpperCAmelCase		:       Any								=      output.images[0]

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

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

					_UpperCAmelCase		:       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  :     Dict	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     List[Any] )   ->str:



					'''simple docstring'''







					_start_torch_memory_measurement()

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

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

					_UpperCAmelCase		:       Union[str, Any]								=      pipe_a(
					    prompt_embeds=lowerCamelCase__	,  negative_prompt_embeds=lowerCamelCase__	,  image=lowerCamelCase__	,  num_inference_steps=2	,  generator=lowerCamelCase__	,  output_type="np"	,  )

					_UpperCAmelCase		:       Tuple								=      output.images[0]

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

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

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

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

					_UpperCAmelCase		:       Union[str, Any]								=      pipe_a(
					    prompt_embeds=lowerCamelCase__	,  negative_prompt_embeds=lowerCamelCase__	,  image=lowerCamelCase__	,  original_image=lowerCamelCase__	,  generator=lowerCamelCase__	,  num_inference_steps=2	,  output_type="np"	,  )

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

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

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

					_UpperCAmelCase		:       List[str]								=      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  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_start_torch_memory_measurement()

					_UpperCAmelCase		:       Tuple								=      floats_tensor((1, 3, 64, 64)	,  rng=random.Random(0 ) ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      floats_tensor((1, 3, 64, 64)	,  rng=random.Random(1 ) ).to(lowerCamelCase__ )

					_UpperCAmelCase		:       int								=      torch.Generator(device="cpu" ).manual_seed(0 )
					_UpperCAmelCase		:       Any								=      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[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_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[str]								=      floats_tensor((1, 3, 64, 64)	,  rng=random.Random(0 ) ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      floats_tensor((1, 3, 2_56, 2_56)	,  rng=random.Random(0 ) ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      floats_tensor((1, 3, 2_56, 2_56)	,  rng=random.Random(1 ) ).to(lowerCamelCase__ )

					_UpperCAmelCase		:       Tuple								=      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_56, 2_56, 3)

					_UpperCAmelCase		:       Optional[int]								=      torch.cuda.max_memory_allocated()
					assert mem_bytes < 4 * 10**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  __lowerCAmelCase ():
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()







'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__    	=     {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MraForMaskedLM',
						    'MraForMultipleChoice',
						    'MraForQuestionAnswering',
						    'MraForSequenceClassification',
						    'MraForTokenClassification',
						    'MraLayer',
						    'MraModel',
						    'MraPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure)



