




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							(   a__,a__,unittest.TestCase		):
     _a							 =						IFPipeline
     _a							 =						TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     _a							 =						TEXT_TO_IMAGE_BATCH_PARAMS
     _a							 =						PipelineTesterMixin.required_optional_params - {'''latents'''}

     def 					a__	(  self				)						->							Union[str, Any]:
           return self._get_dummy_components()

     def 					a__	(  self		,     _a		,     _a=0				)						->							List[Any]:




           if str(SCREAMING_SNAKE_CASE__				).startswith("""mps"""				):
                 _A							:     Dict			=						torch.manual_seed(SCREAMING_SNAKE_CASE__				)
           else:
                 _A							:     Any			=						torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)

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

           return inputs

     def 					a__	(  self				)						->							Tuple:
           self._test_save_load_optional_components()

     @unittest.skipIf(torch_device != """cuda"""		,     reason="""float16 requires CUDA"""				)
     def 					a__	(  self				)						->							List[str]:
           super().test_save_load_floataa(expected_max_diff=1e-1				)

     def 					a__	(  self				)						->							List[Any]:
           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)

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

     def 					a__	(  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 					a__	(  self				)						->							Optional[int]:
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)







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

     def 					a__	(  self				)						->							Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Optional[int]			=						IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""		,     variant="""fp16"""		,     torch_dtype=torch.floataa				)

           _A							:     Dict			=						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"""				)

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

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

           _A							:     List[str]			=						None
           _A							:     Union[str, Any]			=						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

           _A							:     Union[str, Any]			=						IFImgaImgPipeline(**pipe_a.components				)
           _A							:     Optional[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

           _A							:     Optional[Any]			=						IFInpaintingPipeline(**pipe_a.components				)
           _A							:     int			=						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 					a__	(  self		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           _start_torch_memory_measurement()

           _A							:     Tuple			=						torch.Generator(device="""cpu"""				).manual_seed(0				)
           _A							:     Dict			=						pipe_a(
               prompt_embeds=SCREAMING_SNAKE_CASE__		,     negative_prompt_embeds=SCREAMING_SNAKE_CASE__		,     num_inference_steps=2		,     generator=SCREAMING_SNAKE_CASE__		,     output_type="""np"""		,     )

           _A							:     int			=						output.images[0]

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

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

           _A							:     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()

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

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

           _A							:     Optional[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"""		,     )

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

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

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

           _A							:     int			=						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 					a__	(  self		,     _a		,     _a		,     _a		,     _a				)						->							str:
           _start_torch_memory_measurement()

           _A							:     int			=						floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0				)				).to(SCREAMING_SNAKE_CASE__				)

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

           _A							:     Any			=						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"""		,     )

           _A							:     Dict			=						output.images[0]

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

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

           _A							:     str			=						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()

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

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

           _A							:     Optional[Any]			=						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"""		,     )

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

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

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

           _A							:     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(SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__				)



     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a				)						->							Union[str, Any]:
           _start_torch_memory_measurement()

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

           _A							:     Dict			=						torch.Generator(device="""cpu"""				).manual_seed(0				)
           _A							:     List[str]			=						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"""		,     )

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

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

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

           _A							:     Optional[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()

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

           _A							:     Dict			=						floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0				)				).to(SCREAMING_SNAKE_CASE__				)
           _A							:     int			=						floats_tensor((1, 3, 256, 256)		,     rng=random.Random(0				)				).to(SCREAMING_SNAKE_CASE__				)
           _A							:     int			=						floats_tensor((1, 3, 256, 256)		,     rng=random.Random(1				)				).to(SCREAMING_SNAKE_CASE__				)

           _A							:     int			=						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"""		,     )

           _A							:     Dict			=						output.images[0]

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

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

           _A							:     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(SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__				)







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



"""simple docstring"""




import mpmath  # for roots of unity
import numpy as np



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   list(poly_a or [0]    )[:]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   list(poly_b or [0]    )[:]

											# Remove leading zero coefficients
											while self.polyA[-1] == 0:
																		self.polyA.pop()
											SCREAMING_SNAKE_CASE__						:       int           =   len(self.polyA    )

											while self.polyB[-1] == 0:
																		self.polyB.pop()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self.polyB    )

											# Add 0 to make lengths equal a power of 2
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(
											    2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

											while len(self.polyA    ) < self.c_max_length:
																		self.polyA.append(0    )
											while len(self.polyB    ) < self.c_max_length:
																		self.polyB.append(0    )
											# A complex root used for the fourier transform
											SCREAMING_SNAKE_CASE__						:       List[str]           =   complex(mpmath.root(x=1   ,       n=self.c_max_length   ,       k=1    )    )

											# The product
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.__multiply()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
											# Corner case
											if len(SCREAMING_SNAKE_CASE__    ) <= 1:
																		return dft[0]
											#
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.c_max_length // 2
											while next_ncol > 0:
																		SCREAMING_SNAKE_CASE__						:       Any           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root**next_ncol

																		# First half of next step
																		SCREAMING_SNAKE_CASE__						:       str           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Second half of next step
																		SCREAMING_SNAKE_CASE__						:       int           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   new_dft
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   next_ncol // 2
											return dft[0]



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.__dft("""A"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.__dft("""B"""    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
											del dft_a
											del dft_b

											# Corner Case
											if len(inverce_c[0]    ) <= 1:
																		return inverce_c[0]
											# Inverse DFT
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   2
											while next_ncol <= self.c_max_length:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root ** (next_ncol // 2)
																		SCREAMING_SNAKE_CASE__						:       Any           =   1
																		# First half of next step
																		for j in range(self.c_max_length // next_ncol    ):
																									for i in range(next_ncol // 2    ):
																																# Even positions
																																new_inverse_c[i].append(
																																    (
																																        inverce_c[i][j]
																																        + inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / 2    )
																																# Odd positions
																																new_inverse_c[i + next_ncol // 2].append(
																																    (
																																        inverce_c[i][j]
																																        - inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / (2 * current_root)    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   new_inverse_c
																		next_ncol *= 2
											# Unpack
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [round(x[0].real   ,       8    ) + round(x[0].imag   ,       8    ) * 1j for x in inverce_c]

											# Remove leading 0's
											while inverce_c[-1] == 0:
																		inverce_c.pop()
											return inverce_c



				def __str__(self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   """A = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   """A*B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

											return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
					import doctest

					doctest.testmod()




