

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 				SCREAMING_SNAKE_CASE__ (lowercase_		,	lowercase_		,	unittest.TestCase					):
				__lowerCamelCase						:	Optional[int]														=					StableDiffusionDiffEditPipeline
				__lowerCamelCase						:	Optional[Any]														=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
				__lowerCamelCase						:	Union[str, Any]														=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
				__lowerCamelCase						:	str														=					frozenset(
				    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__lowerCamelCase						:	Optional[Any]														=					frozenset([]					)





				def 				snake_case_    (				self):
									torch.manual_seed(0)
									lowercase__   :       List[Any]				=	UNetaDConditionModel(
									    block_out_channels=(32, 64)						,       layers_per_block=2						,       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=a__						,       )
									lowercase__   :       Any				=	DDIMScheduler(
									    beta_start=0.00_085						,       beta_end=0.012						,       beta_schedule='scaled_linear'						,       clip_sample=a__						,       set_alpha_to_one=a__						,       )
									lowercase__   :       str				=	DDIMInverseScheduler(
									    beta_start=0.00_085						,       beta_end=0.012						,       beta_schedule='scaled_linear'						,       clip_sample=a__						,       set_alpha_to_zero=a__						,       )
									torch.manual_seed(0)
									lowercase__   :       List[str]				=	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)
									lowercase__   :       Optional[Any]				=	CLIPTextConfig(
									    bos_token_id=0						,       eos_token_id=2						,       hidden_size=32						,       intermediate_size=37						,       layer_norm_eps=1e-05						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1000						,       hidden_act='gelu'						,       projection_dim=512						,       )
									lowercase__   :       Optional[int]				=	CLIPTextModel(a__)
									lowercase__   :       Optional[int]				=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

									lowercase__   :       List[str]				=	{
									    '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 				snake_case_    (				self						,       a						,       a=0):
									lowercase__   :       str				=	floats_tensor((1, 16, 16)						,       rng=random.Random(a__)).to(a__)
									lowercase__   :       int				=	floats_tensor((1, 2, 4, 16, 16)						,       rng=random.Random(a__)).to(a__)
									if str(a__).startswith('mps'):
														lowercase__   :       Optional[int]				=	torch.manual_seed(a__)
									else:
														lowercase__   :       List[str]				=	torch.Generator(device=a__).manual_seed(a__)
									lowercase__   :       List[Any]				=	{
									    '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 				snake_case_    (				self						,       a						,       a=0):
									lowercase__   :       List[str]				=	floats_tensor((1, 3, 32, 32)						,       rng=random.Random(a__)).to(a__)
									lowercase__   :       List[str]				=	image.cpu().permute(0						,       2						,       3						,       1)[0]
									lowercase__   :       Optional[int]				=	Image.fromarray(np.uinta(a__)).convert('RGB')
									if str(a__).startswith('mps'):
														lowercase__   :       List[str]				=	torch.manual_seed(a__)
									else:
														lowercase__   :       List[str]				=	torch.Generator(device=a__).manual_seed(a__)
									lowercase__   :       Optional[int]				=	{
									    '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 				snake_case_    (				self						,       a						,       a=0):
									lowercase__   :       Optional[Any]				=	floats_tensor((1, 3, 32, 32)						,       rng=random.Random(a__)).to(a__)
									lowercase__   :       List[Any]				=	image.cpu().permute(0						,       2						,       3						,       1)[0]
									lowercase__   :       Any				=	Image.fromarray(np.uinta(a__)).convert('RGB')
									if str(a__).startswith('mps'):
														lowercase__   :       Tuple				=	torch.manual_seed(a__)
									else:
														lowercase__   :       Dict				=	torch.Generator(device=a__).manual_seed(a__)
									lowercase__   :       Optional[int]				=	{
									    '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 				snake_case_    (				self):




									if not hasattr(self.pipeline_class						,       '_optional_components'):
														return

									lowercase__   :       str				=	self.get_dummy_components()
									lowercase__   :       str				=	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})

									lowercase__   :       Optional[Any]				=	self.get_dummy_inputs(a__)
									lowercase__   :       int				=	pipe(**a__)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(a__)
														lowercase__   :       List[Any]				=	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."""						,       )

									lowercase__   :       int				=	self.get_dummy_inputs(a__)
									lowercase__   :       Any				=	pipe_loaded(**a__)[0]

									lowercase__   :       Union[str, Any]				=	np.abs(output - output_loaded).max()
									self.assertLess(a__						,       1e-4)





				def 				snake_case_    (				self):
									lowercase__   :       Optional[Any]				=	'cpu'

									lowercase__   :       List[Any]				=	self.get_dummy_components()
									lowercase__   :       Optional[int]				=	self.pipeline_class(**a__)
									pipe.to(a__)
									pipe.set_progress_bar_config(disable=a__)

									lowercase__   :       Tuple				=	self.get_dummy_mask_inputs(a__)
									lowercase__   :       Any				=	pipe.generate_mask(**a__)
									lowercase__   :       Optional[Any]				=	mask[0, -3:, -3:]

									self.assertEqual(mask.shape						,       (1, 16, 16))
									lowercase__   :       List[str]				=	np.array([0] * 9)
									lowercase__   :       Optional[Any]				=	np.abs(mask_slice.flatten() - expected_slice).max()
									self.assertLessEqual(a__						,       1e-3)
									self.assertEqual(mask[0, -3, -4]						,       0)





				def 				snake_case_    (				self):
									lowercase__   :       List[str]				=	'cpu'

									lowercase__   :       int				=	self.get_dummy_components()
									lowercase__   :       Union[str, Any]				=	self.pipeline_class(**a__)
									pipe.to(a__)
									pipe.set_progress_bar_config(disable=a__)

									lowercase__   :       Optional[Any]				=	self.get_dummy_inversion_inputs(a__)
									lowercase__   :       str				=	pipe.invert(**a__).images
									lowercase__   :       List[str]				=	image[0, -1, -3:, -3:]

									self.assertEqual(image.shape						,       (2, 32, 32, 3))
									lowercase__   :       List[str]				=	np.array(
									    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]						,       )
									lowercase__   :       Optional[int]				=	np.abs(image_slice.flatten() - expected_slice).max()
									self.assertLessEqual(a__						,       1e-3)





				def 				snake_case_    (				self):
									super().test_inference_batch_single_identical(expected_max_diff=5e-3)





				def 				snake_case_    (				self):
									lowercase__   :       Optional[int]				=	'cpu'

									lowercase__   :       Union[str, Any]				=	self.get_dummy_components()

									lowercase__   :       List[Any]				=	{'beta_start': 0.00_085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
									lowercase__   :       List[str]				=	DPMSolverMultistepScheduler(**a__)
									lowercase__   :       List[Any]				=	DPMSolverMultistepInverseScheduler(**a__)

									lowercase__   :       List[str]				=	self.pipeline_class(**a__)
									pipe.to(a__)
									pipe.set_progress_bar_config(disable=a__)

									lowercase__   :       Any				=	self.get_dummy_inversion_inputs(a__)
									lowercase__   :       Tuple				=	pipe.invert(**a__).images
									lowercase__   :       Any				=	image[0, -1, -3:, -3:]

									self.assertEqual(image.shape						,       (2, 32, 32, 3))
									lowercase__   :       Tuple				=	np.array(
									    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]						,       )
									lowercase__   :       Any				=	np.abs(image_slice.flatten() - expected_slice).max()
									self.assertLessEqual(a__						,       1e-3)



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





				def 				snake_case_    (				self):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





				@classmethod
				def 				snake_case_    (				cls):
									lowercase__   :       Any				=	load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png')

									lowercase__   :       int				=	raw_image.convert('RGB').resize((768, 768))

									lowercase__   :       List[str]				=	raw_image





				def 				snake_case_    (				self):
									lowercase__   :       Union[str, Any]				=	torch.manual_seed(0)

									lowercase__   :       List[Any]				=	StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'						,       safety_checker=a__						,       torch_dtype=torch.floataa)
									lowercase__   :       Optional[Any]				=	DDIMScheduler.from_config(pipe.scheduler.config)
									lowercase__   :       Optional[int]				=	DDIMInverseScheduler.from_config(pipe.scheduler.config)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=a__)

									lowercase__   :       Tuple				=	'a bowl of fruit'
									lowercase__   :       Tuple				=	'a bowl of pears'

									lowercase__   :       int				=	pipe.generate_mask(
									    image=self.raw_image						,       source_prompt=a__						,       target_prompt=a__						,       generator=a__						,       )

									lowercase__   :       Union[str, Any]				=	pipe.invert(
									    prompt=a__						,       image=self.raw_image						,       inpaint_strength=0.7						,       generator=a__).latents

									lowercase__   :       Optional[int]				=	pipe(
									    prompt=a__						,       mask_image=a__						,       image_latents=a__						,       generator=a__						,       negative_prompt=a__						,       inpaint_strength=0.7						,       output_type='numpy'						,       ).images[0]

									lowercase__   :       Optional[Any]				=	(
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png').resize((768, 768)))
									    / 255
									)
									assert np.abs((expected_image - image).max()) < 5e-1





				def 				snake_case_    (				self):
									lowercase__   :       int				=	torch.manual_seed(0)

									lowercase__   :       Any				=	StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'						,       safety_checker=a__						,       torch_dtype=torch.floataa)
									lowercase__   :       Any				=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
									lowercase__   :       Dict				=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=a__)

									lowercase__   :       List[Any]				=	'a bowl of fruit'
									lowercase__   :       Dict				=	'a bowl of pears'

									lowercase__   :       int				=	pipe.generate_mask(
									    image=self.raw_image						,       source_prompt=a__						,       target_prompt=a__						,       generator=a__						,       )

									lowercase__   :       List[Any]				=	pipe.invert(
									    prompt=a__						,       image=self.raw_image						,       inpaint_strength=0.7						,       generator=a__						,       num_inference_steps=25						,       ).latents

									lowercase__   :       Dict				=	pipe(
									    prompt=a__						,       mask_image=a__						,       image_latents=a__						,       generator=a__						,       negative_prompt=a__						,       inpaint_strength=0.7						,       num_inference_steps=25						,       output_type='numpy'						,       ).images[0]

									lowercase__   :       Optional[int]				=	(
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png').resize((768, 768)))
									    / 255
									)
									assert np.abs((expected_image - image).max()) < 5e-1




from __future__ import annotations


class 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self						,       a						,       a):
         lowercase__				,			lowercase__   :       Dict				=	text, pattern
         lowercase__				,			lowercase__   :       Any				=	len(a), len(a)





    def 				snake_case_    (				self						,       a):

         for i in range(self.patLen - 1						,       -1						,       -1):
              if char == self.pattern[i]:
                   return i
         return -1





    def 				snake_case_    (				self						,       a):

         for i in range(self.patLen - 1						,       -1						,       -1):
              if self.pattern[i] != self.text[current_pos + i]:
                   return current_pos + i
         return -1





    def 				snake_case_    (				self):
         # searches pattern in text and returns index positions
         lowercase__   :       Any				=	[]
         for i in range(self.textLen - self.patLen + 1):
              lowercase__   :       Optional[Any]				=	self.mismatch_in_text(a)
              if mismatch_index == -1:
                   positions.append(a)
              else:
                   lowercase__   :       Optional[int]				=	self.match_in_pattern(self.text[mismatch_index])
                   lowercase__   :       Optional[Any]				=	(
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions



snake_case_        =			'''ABAABA'''
snake_case_        =			'''AB'''
snake_case_        =			BoyerMooreSearch(text, pattern)
snake_case_        =			bms.bad_character_heuristic()

if len(positions) == 0:
       print('''No match found''')
else:
       print('''Pattern found in following positions: ''')
       print(positions)

