




import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 		__lowerCAmelCase	:
							def __init__(						self						:Dict				):







								'''simple docstring'''







								a										=							""""""
								a										=							""""""
								a										=							[]
								a										=							0
								a										=							256
								a										=							0
								a										=							0
								a										=							0
								a										=							0
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							cva.imread(__magic_name__		,      0				)
								a										=							copy.deepcopy(self.img				)
								a				, a				, a										=							plt.hist(self.img.ravel()		,      256		,      [0, 256]		,      label="""x"""				)
								a										=							np.sum(__magic_name__				)
								for i in range(len(__magic_name__				)				):
									a										=							x[i] / self.k
									self.sk += prk
									a										=							(self.L - 1) * self.sk
									if self.rem != 0:
										a										=							int(last % last				)
									a										=							int(last + 1 if self.rem >= 0.5 else last				)
									self.last_list.append(__magic_name__				)
									a										=							int(np.ma.count(self.img				) / self.img[1].size				)
									a										=							self.img[1].size
								for i in range(self.number_of_cols				):
									for j in range(self.number_of_rows				):
										a										=							self.img[j][i]
										if num != self.last_list[num]:
											a										=							self.last_list[num]
								cva.imwrite("""output_data/output.jpg"""		,      self.img				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								plt.hist(self.img.ravel()		,      256		,      [0, 256]				)





							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								cva.imshow("""Output-Image"""		,      self.img				)
								cva.imshow("""Input-Image"""		,      self.original_image				)
								cva.waitKey(5000				)
								cva.destroyAllWindows()


if __name__ == "__main__":
		__UpperCamelCase					:       List[str]    					=      os.path.join(os.path.basename(__file__), "image_data/input.jpg")
		__UpperCamelCase					:       Dict    					=      ConstantStretch()
		stretcher.stretch(file_path)
		stretcher.plot_histogram()
		stretcher.show_image()







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__UpperCamelCase					:       Optional[Any]    					=      False

@skip_mps
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  StableDiffusionAttendAndExcitePipeline
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							UNetaDConditionModel(
								    block_out_channels=(32, 64)		,      layers_per_block=1		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      attention_head_dim=(2, 4)		,      use_linear_projection=__magic_name__		,      )
								a										=							DDIMScheduler(
								    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""		,      clip_sample=__magic_name__		,      set_alpha_to_one=__magic_name__		,      )
								torch.manual_seed(0				)
								a										=							AutoencoderKL(
								    block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,      latent_channels=4		,      sample_size=128		,      )
								torch.manual_seed(0				)
								a										=							CLIPTextConfig(
								    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      hidden_act="""gelu"""		,      projection_dim=512		,      )
								a										=							CLIPTextModel(__magic_name__				)
								a										=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								a										=							{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}

								return components
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:Optional[int]		,      __magic_name__						:Optional[int]=0				):







								'''simple docstring'''







								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							a										=							{
								    """prompt""": """a cat and a frog""",
								    """token_indices""": [2, 5],
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """max_iter_to_alter""": 2,
								    """thresholds""": {0: 0.7},
								}
								return inputs
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							self.get_dummy_inputs(__magic_name__				)
								a										=							pipe(**__magic_name__				).images
								a										=							image[0, -3:, -3:, -1]

								self.assertEqual(image.shape		,      (1, 64, 64, 3)				)
								a										=							np.array(
								    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]				)
								a										=							np.abs(image_slice.flatten() - expected_slice				).max()
								self.assertLessEqual(__magic_name__		,      1E-3				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								self._test_inference_batch_single_identical(batch_size=2		,      expected_max_diff=7E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_save_load_local(expected_max_difference=5E-4				)





							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().test_save_load_optional_components(expected_max_difference=4E-4				)







@require_torch_gpu
@slow
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@classmethod
							def       lowerCamelCase__							(						cls						:int				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Any				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							torch.manual_seed(51				)

								a										=							StableDiffusionAttendAndExcitePipeline.from_pretrained(
								    """CompVis/stable-diffusion-v1-4"""		,      safety_checker=__magic_name__		,      torch_dtype=torch.floataa				)
								pipe.to("""cuda"""				)

								a										=							"""a painting of an elephant with glasses"""
								a										=							[5, 7]

								a										=							pipe(
								    prompt=__magic_name__		,      token_indices=__magic_name__		,      guidance_scale=7.5		,      generator=__magic_name__		,      num_inference_steps=5		,      max_iter_to_alter=5		,      output_type="""numpy"""		,      ).images[0]

								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""				)
								assert np.abs((expected_image - image).max()				) < 5E-1

