



"""simple docstring"""





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


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


             '''simple docstring'''




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



       @property
       def 	lowercase__							(				self     :    List[str]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				1
             UpperCAmelCase_     					=				3
             UpperCAmelCase_     					=				(32, 32)

             UpperCAmelCase_     					=				floats_tensor((batch_size, num_channels) + sizes						,							rng=random.Random(0       )       ).to(_UpperCAmelCase       )
             return image



       @property
       def 	lowercase__							(				self     :    Optional[int]       )					->		int:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				UNetaDConditionModel(
                 block_out_channels=(32, 32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=7						,							out_channels=4						,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")						,							up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")						,							cross_attention_dim=32						,							attention_head_dim=8						,							use_linear_projection=_UpperCAmelCase						,							only_cross_attention=(True, True, False)						,							num_class_embeds=100						,							)
             return model



       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		List[str]:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				AutoencoderKL(
                 block_out_channels=[32, 32, 64]						,							in_channels=3						,							out_channels=3						,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]						,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]						,							latent_channels=4						,							)
             return model



       @property
       def 	lowercase__							(				self     :    int       )					->		Any:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				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						,							)
             return CLIPTextModel(_UpperCAmelCase       )



       def 	lowercase__							(				self     :    str       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.dummy_cond_unet_upscale
             UpperCAmelCase_     					=				DDPMScheduler()
             UpperCAmelCase_     					=				DDIMScheduler(prediction_type="v_prediction"       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

             UpperCAmelCase_     					=				self.dummy_image.cpu().permute(0						,							2						,							3						,							1       )[0]
             UpperCAmelCase_     					=				Image.fromarray(np.uinta(_UpperCAmelCase       )       ).convert("RGB"       ).resize((64, 64)       )

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				StableDiffusionUpscalePipeline(
                 unet=_UpperCAmelCase						,							low_res_scheduler=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							max_noise_level=350						,							)
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				sd_pipe(
                 [prompt]						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type="np"						,							)

             UpperCAmelCase_     					=				output.images

             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				sd_pipe(
                 [prompt]						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type="np"						,							return_dict=_UpperCAmelCase						,							)[0]

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             UpperCAmelCase_     					=				image_from_tuple[0, -3:, -3:, -1]

             UpperCAmelCase_     					=				low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)
             UpperCAmelCase_     					=				np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



       def 	lowercase__							(				self     :    Dict       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.dummy_cond_unet_upscale
             UpperCAmelCase_     					=				DDPMScheduler()
             UpperCAmelCase_     					=				DDIMScheduler(prediction_type="v_prediction"       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

             UpperCAmelCase_     					=				self.dummy_image.cpu().permute(0						,							2						,							3						,							1       )[0]
             UpperCAmelCase_     					=				Image.fromarray(np.uinta(_UpperCAmelCase       )       ).convert("RGB"       ).resize((64, 64)       )

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				StableDiffusionUpscalePipeline(
                 unet=_UpperCAmelCase						,							low_res_scheduler=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							max_noise_level=350						,							)
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				sd_pipe(
                 2 * [prompt]						,							image=2 * [low_res_image]						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images
             assert image.shape[0] == 2

             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				sd_pipe(
                 [prompt]						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							num_images_per_prompt=2						,							guidance_scale=6.0						,							noise_level=20						,							num_inference_steps=2						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images
             assert image.shape[0] == 2







       @unittest.skipIf(torch_device != "cuda"						,							"This test requires a GPU"       )
       def 	lowercase__							(				self     :    List[Any]       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.dummy_cond_unet_upscale
             UpperCAmelCase_     					=				DDPMScheduler()
             UpperCAmelCase_     					=				DDIMScheduler(prediction_type="v_prediction"       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

             UpperCAmelCase_     					=				self.dummy_image.cpu().permute(0						,							2						,							3						,							1       )[0]
             UpperCAmelCase_     					=				Image.fromarray(np.uinta(_UpperCAmelCase       )       ).convert("RGB"       ).resize((64, 64)       )

             # put models in fp16, except vae as it overflows in fp16
             UpperCAmelCase_     					=				unet.half()
             UpperCAmelCase_     					=				text_encoder.half()

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				StableDiffusionUpscalePipeline(
                 unet=_UpperCAmelCase						,							low_res_scheduler=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							max_noise_level=350						,							)
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				sd_pipe(
                 [prompt]						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							num_inference_steps=2						,							output_type="np"						,							).images

             UpperCAmelCase_     					=				low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)



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







       '''simple docstring'''


       def 	lowercase__							(				self     :    Optional[int]       )					->		int:


             '''simple docstring'''




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



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


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/sd2-upscale/low_res_cat.png"       )
             UpperCAmelCase_     					=				load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                 "/upsampled_cat.npy"       )

             UpperCAmelCase_     					=				"stabilityai/stable-diffusion-x4-upscaler"
             UpperCAmelCase_     					=				StableDiffusionUpscalePipeline.from_pretrained(_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				"a cat sitting on a park bench"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image       ).max() < 1e-3



       def 	lowercase__							(				self     :    str       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/sd2-upscale/low_res_cat.png"       )
             UpperCAmelCase_     					=				load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                 "/upsampled_cat_fp16.npy"       )

             UpperCAmelCase_     					=				"stabilityai/stable-diffusion-x4-upscaler"
             UpperCAmelCase_     					=				StableDiffusionUpscalePipeline.from_pretrained(
                 _UpperCAmelCase						,							torch_dtype=torch.floataa						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				"a cat sitting on a park bench"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image       ).max() < 5e-1







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


             '''simple docstring'''




             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/sd2-upscale/low_res_cat.png"       )

             UpperCAmelCase_     					=				"stabilityai/stable-diffusion-x4-upscaler"
             UpperCAmelCase_     					=				StableDiffusionUpscalePipeline.from_pretrained(
                 _UpperCAmelCase						,							torch_dtype=torch.floataa						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing(1       )
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase_     					=				"a cat sitting on a park bench"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							num_inference_steps=5						,							output_type="np"						,							)

             UpperCAmelCase_     					=				torch.cuda.max_memory_allocated()
             # make sure that less than 2.9 GB is allocated
             assert mem_bytes < 2.9 * 10**9








'''simple docstring'''

from __future__ import annotations

from collections import deque
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       str  ,		UpperCamelCase       :       list[str]					):

             '''simple docstring'''







             lowercase__      =					[]
             self.adlist.append(
                 {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}					)

             for keyword in keywords:
                    self.add_keyword(UpperCamelCase					)
             self.set_fail_transitions()



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             for state in self.adlist[current_state]["next_states"]:
                    if char == self.adlist[state]["value"]:
                           return state
             return None



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					0
             for character in keyword:
                    lowercase__      =					self.find_next_state(UpperCamelCase  ,		UpperCamelCase					)
                    if next_state is None:
                           self.adlist.append(
                               {
                                   '''value''': character,
                                   '''next_states''': [],
                                   '''fail_state''': 0,
                                   '''output''': [],
                               }					)
                           self.adlist[current_state]["next_states"].append(len(self.adlist					) - 1					)
                           lowercase__      =					len(self.adlist					) - 1
                    else:
                           lowercase__      =					next_state
             self.adlist[current_state]["output"].append(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					deque()
             for node in self.adlist[0]["next_states"]:
                    q.append(UpperCamelCase					)
                    lowercase__      =					0
             while q:
                    lowercase__      =					q.popleft()
                    for child in self.adlist[r]["next_states"]:
                           q.append(UpperCamelCase					)
                           lowercase__      =					self.adlist[r]['''fail_state''']
                           while (
                               self.find_next_state(UpperCamelCase  ,		self.adlist[child]['''value''']					) is None
                               and state != 0
                           ):
                                  lowercase__      =					self.adlist[state]['''fail_state''']
                           lowercase__      =					self.find_next_state(
                               UpperCamelCase  ,		self.adlist[child]['''value''']					)
                           if self.adlist[child]["fail_state"] is None:
                                  lowercase__      =					0
                           lowercase__      =					(
                               self.adlist[child]['''output''']
                               + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
                           )






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					{}  # returns a dict with keywords and list of its occurrences
             lowercase__      =					0
             for i in range(len(UpperCamelCase					)					):
                    while (
                        self.find_next_state(UpperCamelCase  ,		string[i]					) is None
                        and current_state != 0
                    ):
                           lowercase__      =					self.adlist[current_state]['''fail_state''']
                    lowercase__      =					self.find_next_state(UpperCamelCase  ,		string[i]					)
                    if next_state is None:
                           lowercase__      =					0
                    else:
                           lowercase__      =					next_state
                           for key in self.adlist[current_state]["output"]:
                                  if key not in result:
                                         lowercase__      =					[]
                                  result[key].append(i - len(UpperCamelCase					) + 1					)
             return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()




