
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  lowerCamelCase__							(				__lowercase     ,						__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     StableDiffusionDiffEditPipeline
	_A								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
	_A								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
	_A								=     frozenset(
	    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_A								=     frozenset([])







	def    _lowerCamelCase				(					self		:Dict						)		-> Tuple:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)     ,   layers_per_block=2     ,   sample_size=3_2     ,   in_channels=4     ,   out_channels=4     ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,   cross_attention_dim=3_2     ,   attention_head_dim=(2, 4)     ,   use_linear_projection=a     ,   )
								__UpperCamelCase	:   List[str]			   =					DDIMScheduler(
								    beta_start=0.00085     ,   beta_end=0.012     ,   beta_schedule="scaled_linear"     ,   clip_sample=a     ,   set_alpha_to_one=a     ,   )
								__UpperCamelCase	:   List[str]			   =					DDIMInverseScheduler(
								    beta_start=0.00085     ,   beta_end=0.012     ,   beta_schedule="scaled_linear"     ,   clip_sample=a     ,   set_alpha_to_zero=a     ,   )
								torch.manual_seed(0						)
								__UpperCamelCase	:   Dict			   =					AutoencoderKL(
								    block_out_channels=[3_2, 6_4]     ,   in_channels=3     ,   out_channels=3     ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,   latent_channels=4     ,   sample_size=1_2_8     ,   )
								torch.manual_seed(0						)
								__UpperCamelCase	:   List[Any]			   =					CLIPTextConfig(
								    bos_token_id=0     ,   eos_token_id=2     ,   hidden_size=3_2     ,   intermediate_size=3_7     ,   layer_norm_eps=1E-0_5     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   pad_token_id=1     ,   vocab_size=1_0_0_0     ,   hidden_act="gelu"     ,   projection_dim=5_1_2     ,   )
								__UpperCamelCase	:   List[str]			   =					CLIPTextModel(a						)
								__UpperCamelCase	:   Optional[int]			   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

								__UpperCamelCase	:   Optional[Any]			   =					{
								    "unet": unet,
								    "scheduler": scheduler,
								    "inverse_scheduler": inverse_scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}

								return components







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str     ,   a		:Optional[Any]=0						)		-> str:
								__UpperCamelCase	:   Dict			   =					floats_tensor((1, 1_6, 1_6)     ,   rng=random.Random(a						)						).to(a						)
								__UpperCamelCase	:   List[str]			   =					floats_tensor((1, 2, 4, 1_6, 1_6)     ,   rng=random.Random(a						)						).to(a						)
								if str(a						).startswith("mps"						):
															__UpperCamelCase	:   Optional[int]			   =					torch.manual_seed(a						)
								else:
															__UpperCamelCase	:   int			   =					torch.Generator(device=a						).manual_seed(a						)
								__UpperCamelCase	:   Tuple			   =					{
								    "prompt": "a dog and a newt",
								    "mask_image": mask,
								    "image_latents": latents,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "inpaint_strength": 1.0,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								}

								return inputs







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict     ,   a		:Dict=0						)		-> str:
								__UpperCamelCase	:   Any			   =					floats_tensor((1, 3, 3_2, 3_2)     ,   rng=random.Random(a						)						).to(a						)
								__UpperCamelCase	:   Union[str, Any]			   =					image.cpu().permute(0     ,   2     ,   3     ,   1						)[0]
								__UpperCamelCase	:   Dict			   =					Image.fromarray(np.uinta(a						)						).convert("RGB"						)
								if str(a						).startswith("mps"						):
															__UpperCamelCase	:   Optional[int]			   =					torch.manual_seed(a						)
								else:
															__UpperCamelCase	:   Any			   =					torch.Generator(device=a						).manual_seed(a						)
								__UpperCamelCase	:   str			   =					{
								    "image": image,
								    "source_prompt": "a cat and a frog",
								    "target_prompt": "a dog and a newt",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "num_maps_per_mask": 2,
								    "mask_encode_strength": 1.0,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								}

								return inputs







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Optional[int]     ,   a		:Union[str, Any]=0						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					floats_tensor((1, 3, 3_2, 3_2)     ,   rng=random.Random(a						)						).to(a						)
								__UpperCamelCase	:   List[Any]			   =					image.cpu().permute(0     ,   2     ,   3     ,   1						)[0]
								__UpperCamelCase	:   Optional[Any]			   =					Image.fromarray(np.uinta(a						)						).convert("RGB"						)
								if str(a						).startswith("mps"						):
															__UpperCamelCase	:   Optional[int]			   =					torch.manual_seed(a						)
								else:
															__UpperCamelCase	:   List[Any]			   =					torch.Generator(device=a						).manual_seed(a						)
								__UpperCamelCase	:   Dict			   =					{
								    "image": image,
								    "prompt": "a cat and a frog",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "inpaint_strength": 1.0,
								    "guidance_scale": 6.0,
								    "decode_latents": True,
								    "output_type": "numpy",
								}
								return inputs







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Optional[int]:
								if not hasattr(self.pipeline_class     ,   "_optional_components"						):
															return

								__UpperCamelCase	:   Optional[Any]			   =					self.get_dummy_components()
								__UpperCamelCase	:   int			   =					self.pipeline_class(**a						)
								pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)

								# set all optional components to None and update pipeline config accordingly
								for optional_component in pipe._optional_components:
															setattr(a     ,   a     ,   a						)
								pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}						)

								__UpperCamelCase	:   List[Any]			   =					self.get_dummy_inputs(a						)
								__UpperCamelCase	:   Any			   =					pipe(**a						)[0]

								with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(a						)
															__UpperCamelCase	:   Tuple			   =					self.pipeline_class.from_pretrained(a						)
															pipe_loaded.to(a						)
															pipe_loaded.set_progress_bar_config(disable=a						)

								for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(a     ,   a						) is None     ,   f'`{optional_component}` did not stay set to None after loading.'     ,   )

								__UpperCamelCase	:   int			   =					self.get_dummy_inputs(a						)
								__UpperCamelCase	:   Optional[int]			   =					pipe_loaded(**a						)[0]

								__UpperCamelCase	:   Any			   =					np.abs(output - output_loaded						).max()
								self.assertLess(a     ,   1E-4						)







	def    _lowerCamelCase				(					self		:int						)		-> Any:
								__UpperCamelCase	:   Dict			   =					"cpu"

								__UpperCamelCase	:   str			   =					self.get_dummy_components()
								__UpperCamelCase	:   str			   =					self.pipeline_class(**a						)
								pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   int			   =					self.get_dummy_mask_inputs(a						)
								__UpperCamelCase	:   Dict			   =					pipe.generate_mask(**a						)
								__UpperCamelCase	:   Optional[int]			   =					mask[0, -3:, -3:]

								self.assertEqual(mask.shape     ,   (1, 1_6, 1_6)						)
								__UpperCamelCase	:   List[Any]			   =					np.array([0] * 9						)
								__UpperCamelCase	:   Dict			   =					np.abs(mask_slice.flatten() - expected_slice						).max()
								self.assertLessEqual(a     ,   1E-3						)
								self.assertEqual(mask[0, -3, -4]     ,   0						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Any:
								__UpperCamelCase	:   Dict			   =					"cpu"

								__UpperCamelCase	:   Optional[int]			   =					self.get_dummy_components()
								__UpperCamelCase	:   Any			   =					self.pipeline_class(**a						)
								pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Optional[int]			   =					self.get_dummy_inversion_inputs(a						)
								__UpperCamelCase	:   Optional[Any]			   =					pipe.invert(**a						).images
								__UpperCamelCase	:   int			   =					image[0, -1, -3:, -3:]

								self.assertEqual(image.shape     ,   (2, 3_2, 3_2, 3)						)
								__UpperCamelCase	:   List[Any]			   =					np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]     ,   )
								__UpperCamelCase	:   Union[str, Any]			   =					np.abs(image_slice.flatten() - expected_slice						).max()
								self.assertLessEqual(a     ,   1E-3						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Dict:
								super().test_inference_batch_single_identical(expected_max_diff=5E-3						)







	def    _lowerCamelCase				(					self		:Dict						)		-> str:
								__UpperCamelCase	:   Optional[int]			   =					"cpu"

								__UpperCamelCase	:   Any			   =					self.get_dummy_components()

								__UpperCamelCase	:   Union[str, Any]			   =					{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
								__UpperCamelCase	:   int			   =					DPMSolverMultistepScheduler(**a						)
								__UpperCamelCase	:   List[Any]			   =					DPMSolverMultistepInverseScheduler(**a						)

								__UpperCamelCase	:   str			   =					self.pipeline_class(**a						)
								pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Any			   =					self.get_dummy_inversion_inputs(a						)
								__UpperCamelCase	:   List[Any]			   =					pipe.invert(**a						).images
								__UpperCamelCase	:   Dict			   =					image[0, -1, -3:, -3:]

								self.assertEqual(image.shape     ,   (2, 3_2, 3_2, 3)						)
								__UpperCamelCase	:   int			   =					np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]     ,   )
								__UpperCamelCase	:   int			   =					np.abs(image_slice.flatten() - expected_slice						).max()
								self.assertLessEqual(a     ,   1E-3						)




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




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:List[Any]						)		-> int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	@classmethod
	def    _lowerCamelCase				(					cls		:Any						)		-> Any:
								__UpperCamelCase	:   Union[str, Any]			   =					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"						)

								__UpperCamelCase	:   List[str]			   =					raw_image.convert("RGB"						).resize((7_6_8, 7_6_8)						)

								__UpperCamelCase	:   Any			   =					raw_image







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   Union[str, Any]			   =					torch.manual_seed(0						)

								__UpperCamelCase	:   Union[str, Any]			   =					StableDiffusionDiffEditPipeline.from_pretrained(
								    "stabilityai/stable-diffusion-2-1"     ,   safety_checker=a     ,   torch_dtype=torch.floataa						)
								__UpperCamelCase	:   Tuple			   =					DDIMScheduler.from_config(pipe.scheduler.config						)
								__UpperCamelCase	:   List[str]			   =					DDIMInverseScheduler.from_config(pipe.scheduler.config						)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Dict			   =					"a bowl of fruit"
								__UpperCamelCase	:   str			   =					"a bowl of pears"

								__UpperCamelCase	:   int			   =					pipe.generate_mask(
								    image=self.raw_image     ,   source_prompt=a     ,   target_prompt=a     ,   generator=a     ,   )

								__UpperCamelCase	:   List[Any]			   =					pipe.invert(
								    prompt=a     ,   image=self.raw_image     ,   inpaint_strength=0.7     ,   generator=a						).latents

								__UpperCamelCase	:   Tuple			   =					pipe(
								    prompt=a     ,   mask_image=a     ,   image_latents=a     ,   generator=a     ,   negative_prompt=a     ,   inpaint_strength=0.7     ,   output_type="numpy"     ,   ).images[0]

								__UpperCamelCase	:   List[Any]			   =					(
								    np.array(
								        load_image(
								            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								            "/diffedit/pears.png"						).resize((7_6_8, 7_6_8)						)						)
								    / 2_5_5
								)
								assert np.abs((expected_image - image).max()						) < 5E-1







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   Any			   =					torch.manual_seed(0						)

								__UpperCamelCase	:   str			   =					StableDiffusionDiffEditPipeline.from_pretrained(
								    "stabilityai/stable-diffusion-2-1"     ,   safety_checker=a     ,   torch_dtype=torch.floataa						)
								__UpperCamelCase	:   Optional[int]			   =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
								__UpperCamelCase	:   List[str]			   =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Tuple			   =					"a bowl of fruit"
								__UpperCamelCase	:   Tuple			   =					"a bowl of pears"

								__UpperCamelCase	:   List[str]			   =					pipe.generate_mask(
								    image=self.raw_image     ,   source_prompt=a     ,   target_prompt=a     ,   generator=a     ,   )

								__UpperCamelCase	:   List[str]			   =					pipe.invert(
								    prompt=a     ,   image=self.raw_image     ,   inpaint_strength=0.7     ,   generator=a     ,   num_inference_steps=2_5     ,   ).latents

								__UpperCamelCase	:   Optional[int]			   =					pipe(
								    prompt=a     ,   mask_image=a     ,   image_latents=a     ,   generator=a     ,   negative_prompt=a     ,   inpaint_strength=0.7     ,   num_inference_steps=2_5     ,   output_type="numpy"     ,   ).images[0]

								__UpperCamelCase	:   Union[str, Any]			   =					(
								    np.array(
								        load_image(
								            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								            "/diffedit/pears.png"						).resize((7_6_8, 7_6_8)						)						)
								    / 2_5_5
								)
								assert np.abs((expected_image - image).max()						) < 5E-1

import argparse
import datetime




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					{
							    "0": "Sunday",
							    "1": "Monday",
							    "2": "Tuesday",
							    "3": "Wednesday",
							    "4": "Thursday",
							    "5": "Friday",
							    "6": "Saturday",
							}

							__UpperCamelCase	:   List[str]			   =					{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

							# Validate
							if not 0 < len(_lowerCamelCase) < 11:
														raise ValueError("Must be 10 characters long")

							# Get month
							__UpperCamelCase	:   int			   =					int(date_input[0] + date_input[1])
							# Validate
							if not 0 < m < 13:
														raise ValueError("Month must be between 1 - 12")

							__UpperCamelCase	:   str			   =					date_input[2]
							# Validate
							if sep_a not in ["-", "/"]:
														raise ValueError("Date separator must be '-' or '/'")

							# Get day
							__UpperCamelCase	:   int			   =					int(date_input[3] + date_input[4])
							# Validate
							if not 0 < d < 32:
														raise ValueError("Date must be between 1 - 31")

							# Get second separator
							__UpperCamelCase	:   str			   =					date_input[5]
							# Validate
							if sep_a not in ["-", "/"]:
														raise ValueError("Date separator must be '-' or '/'")

							# Get year
							__UpperCamelCase	:   int			   =					int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
							# Arbitrary year range
							if not 45 < y < 8_500:
														raise ValueError(
														    "Year out of range. There has to be some sort of limit...right?")

							# Get datetime obj for validation
							__UpperCamelCase	:   Union[str, Any]			   =					datetime.date(int(_lowerCamelCase)						,  int(_lowerCamelCase)						,  int(_lowerCamelCase))

							# Start math
							if m <= 2:
														__UpperCamelCase	:   Any			   =					y - 1
														__UpperCamelCase	:   Optional[Any]			   =					m + 12
							# maths var
							__UpperCamelCase	:   int			   =					int(str(_lowerCamelCase)[:2])
							__UpperCamelCase	:   int			   =					int(str(_lowerCamelCase)[2:])
							__UpperCamelCase	:   int			   =					int(2.6 * m - 5.3_9)
							__UpperCamelCase	:   int			   =					int(c / 4)
							__UpperCamelCase	:   int			   =					int(k / 4)
							__UpperCamelCase	:   int			   =					int(d + k)
							__UpperCamelCase	:   int			   =					int(t + u + v + x)
							__UpperCamelCase	:   int			   =					int(z - (2 * c))
							__UpperCamelCase	:   int			   =					round(w % 7)
							# End math

							# Validate math
							if f != convert_datetime_days[dt_ck.weekday()]:
														raise AssertionError("The date was evaluated incorrectly. Contact developer.")

							# Response
							__UpperCamelCase	:   str			   =					F'Your date {date_input}, is a {days[str(_lowerCamelCase)]}!'
							return response


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase  :					Optional[int]													=       argparse.ArgumentParser(
		    description=(
		        'Find out what day of the week nearly any date is or was. Enter '
		        'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
		    )
		)
		parser.add_argument(
		    'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
		)
		lowercase  :					Tuple													=       parser.parse_args()
		zeller(args.date_input)