
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor







class 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case=7		,     _snake_case=3		,     _snake_case=18		,     _snake_case=30		,     _snake_case=400		,     _snake_case=True		,     _snake_case=None		,     _snake_case=True		,     _snake_case=None		,     _snake_case=True		,     _snake_case=[0.4814_5466, 0.457_8275, 0.4082_1073]		,     _snake_case=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,     _snake_case=True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    size if size is not None else {'''height''': 224, '''width''': 224}
								__a										=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								__a										=    parent
								__a										=    batch_size
								__a										=    num_channels
								__a										=    image_size
								__a										=    min_resolution
								__a										=    max_resolution
								__a										=    do_resize
								__a										=    size
								__a										=    do_center_crop
								__a										=    crop_size
								__a										=    do_normalize
								__a										=    image_mean
								__a										=    image_std
								__a										=    do_convert_rgb




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


								'''simple docstring'''




								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False		,     _snake_case=False		,     _snake_case=False				)    ->      Union[str, Any]:


								'''simple docstring'''




								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
												__a										=    []
												for i in range(self.batch_size				):
																image_inputs.append(
																    np.random.randint(
																        255		,     size=(self.num_channels, self.max_resolution, self.max_resolution)		,     dtype=np.uinta				)				)
								else:
												__a										=    []
												for i in range(self.batch_size				):
																__a							,     __a										=    np.random.choice(np.arange(self.min_resolution		,     self.max_resolution				)		,     2				)
																image_inputs.append(np.random.randint(255		,     size=(self.num_channels, width, height)		,     dtype=np.uinta				)				)

								if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												__a										=    [Image.fromarray(np.moveaxis(_snake_case		,     0		,     -1				)				) for x in image_inputs]

								if torchify:
												__a										=    [torch.from_numpy(_snake_case				) for x in image_inputs]

								return image_inputs







@require_torch
@require_vision
class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    ChineseCLIPImageProcessor if is_vision_available() else None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    ChineseCLIPImageProcessingTester(self		,     do_center_crop=_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_resize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''size'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_mean'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_std'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




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


								'''simple docstring'''




								__a										=    self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size		,     {'''height''': 224, '''width''': 224}				)
								self.assertEqual(image_processor.crop_size		,     {'''height''': 18, '''width''': 18}				)

								__a										=    self.image_processing_class.from_dict(self.image_processor_dict		,     size=42		,     crop_size=84				)
								self.assertEqual(image_processor.size		,     {'''shortest_edge''': 42}				)
								self.assertEqual(image_processor.crop_size		,     {'''height''': 84, '''width''': 84}				)




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


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     Image.Image				)

								# Test not batched input
								__a										=    image_processing(image_inputs[0]		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )

								# Test batched
								__a										=    image_processing(_snake_case		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case		,     numpify=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     np.ndarray				)

								# Test not batched input
								__a										=    image_processing(image_inputs[0]		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )

								# Test batched
								__a										=    image_processing(_snake_case		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )




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


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case		,     torchify=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     torch.Tensor				)

								# Test not batched input
								__a										=    image_processing(image_inputs[0]		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )

								# Test batched
								__a										=    image_processing(_snake_case		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )







@require_torch
@require_vision
class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    ChineseCLIPImageProcessor if is_vision_available() else None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    ChineseCLIPImageProcessingTester(self		,     num_channels=4		,     do_center_crop=_snake_case				)
								__a										=    3




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_resize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''size'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_mean'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_std'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     Image.Image				)

								# Test not batched input
								__a										=    image_processing(image_inputs[0]		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )

								# Test batched
								__a										=    image_processing(_snake_case		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )		,     )



import math
from typing import Callable, List, Optional, Union

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

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=[]	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				size[0] - overlap_pixels * 2
  lowerCamelCase :					int    =				size[1] - overlap_pixels * 2
  for letter in ["l", "r"]:
    if letter in remove_borders:
      size_x += overlap_pixels
  for letter in ["t", "b"]:
    if letter in remove_borders:
      size_y += overlap_pixels
  lowerCamelCase :					Tuple    =				np.ones((size_y, size_x)      ,						dtype=np.uinta	) * 255
  lowerCamelCase :					List[Any]    =				np.pad(SCREAMING_SNAKE_CASE_      ,						mode="linear_ramp"      ,						pad_width=SCREAMING_SNAKE_CASE_      ,						end_values=0	)

  if "l" in remove_borders:
    lowerCamelCase :					Optional[Any]    =				mask[:, overlap_pixels : mask.shape[1]]
  if "r" in remove_borders:
    lowerCamelCase :					List[Any]    =				mask[:, 0 : mask.shape[1] - overlap_pixels]
  if "t" in remove_borders:
    lowerCamelCase :					List[Any]    =				mask[overlap_pixels : mask.shape[0], :]
  if "b" in remove_borders:
    lowerCamelCase :					Tuple    =				mask[0 : mask.shape[0] - overlap_pixels, :]
  return mask






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return max(SCREAMING_SNAKE_CASE_      ,						min(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return (
      clamp(rect[0]      ,						min[0]      ,						max[0]	),
      clamp(rect[1]      ,						min[1]      ,						max[1]	),
      clamp(rect[2]      ,						min[0]      ,						max[0]	),
      clamp(rect[3]      ,						min[1]      ,						max[1]	),
  )






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				list(SCREAMING_SNAKE_CASE_	)
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  lowerCamelCase :					Any    =				clamp_rect(SCREAMING_SNAKE_CASE_      ,						[0, 0]      ,						[image_size[0], image_size[1]]	)
  return rect






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				Image.new("RGB"      ,						(tile.size[0] + original_slice, tile.size[1])	)
  result.paste(
      original_image.resize((tile.size[0], tile.size[1])      ,						Image.BICUBIC	).crop(
          (slice_x, 0, slice_x + original_slice, tile.size[1])	)      ,						(0, 0)      ,						)
  result.paste(SCREAMING_SNAKE_CASE_      ,						(original_slice, 0)	)
  return result






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  lowerCamelCase :					int    =				tile.crop(SCREAMING_SNAKE_CASE_	)
  return tile






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				n % d
  return n - divisor
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A = 350						,				):






     """simple docstring"""





     super().__init__(
         vae=__A						,				text_encoder=__A						,				tokenizer=__A						,				unet=__A						,				low_res_scheduler=__A						,				scheduler=__A						,				max_noise_level=__A						,				)






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """simple docstring"""





     torch.manual_seed(0 )
     lowerCamelCase :					Tuple    =				(
         min(image.size[0] - (tile_size + original_image_slice)						,				x * tile_size ),
         min(image.size[1] - (tile_size + original_image_slice)						,				y * tile_size ),
         min(image.size[0]						,				(x + 1) * tile_size ),
         min(image.size[1]						,				(y + 1) * tile_size ),
     )
     lowerCamelCase :					Union[str, Any]    =				add_overlap_rect(__A						,				__A						,				image.size )
     lowerCamelCase :					List[str]    =				image.crop(__A )
     lowerCamelCase :					Optional[int]    =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     lowerCamelCase :					int    =				translated_slice_x - (original_image_slice / 2)
     lowerCamelCase :					Optional[Any]    =				max(0						,				__A )
     lowerCamelCase :					Tuple    =				squeeze_tile(__A						,				__A						,				__A						,				__A )
     lowerCamelCase :					Dict    =				to_input.size
     lowerCamelCase :					Optional[int]    =				to_input.resize((tile_size, tile_size)						,				Image.BICUBIC )
     lowerCamelCase :					Dict    =				super(__A						,				self ).__call__(image=__A						,				**__A ).images[0]
     lowerCamelCase :					Tuple    =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					Optional[Any]    =				unsqueeze_tile(__A						,				__A )
     lowerCamelCase :					Optional[Any]    =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					int    =				[]
     if x == 0:
       remove_borders.append("l" )
     elif crop_rect[2] == image.size[0]:
       remove_borders.append("r" )
     if y == 0:
       remove_borders.append("t" )
     elif crop_rect[3] == image.size[1]:
       remove_borders.append("b" )
     lowerCamelCase :					int    =				Image.fromarray(
         make_transparency_mask(
             (upscaled_tile.size[0], upscaled_tile.size[1])						,				tile_border * 4						,				remove_borders=__A )						,				mode="L"						,				)
     final_image.paste(
         __A						,				(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)						,				__A )






   @torch.no_grad()
   def __call__(		self						,				__A						,				__A						,				__A = 75						,				__A = 9.0						,				__A = 50						,				__A = None						,				__A = 1						,				__A = 0.0						,				__A = None						,				__A = None						,				__A = None						,				__A = 1						,				__A = 128						,				__A = 32						,				__A = 32						,				):






     """simple docstring"""





     lowerCamelCase :					Dict    =				Image.new("RGB"						,				(image.size[0] * 4, image.size[1] * 4) )
     lowerCamelCase :					Union[str, Any]    =				math.ceil(image.size[0] / tile_size )
     lowerCamelCase :					Dict    =				math.ceil(image.size[1] / tile_size )
     lowerCamelCase :					str    =				tcx * tcy
     lowerCamelCase :					int    =				0
     for y in range(__A ):
       for x in range(__A ):
         self._process_tile(
             __A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				prompt=__A						,				num_inference_steps=__A						,				guidance_scale=__A						,				noise_level=__A						,				negative_prompt=__A						,				num_images_per_prompt=__A						,				eta=__A						,				generator=__A						,				latents=__A						,				)
         current_count += 1
         if callback is not None:
           callback({"progress": current_count / total_tile_count, "image": final_image} )
     return final_image






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"stabilityai/stable-diffusion-x4-upscaler"
  lowerCamelCase :					Union[str, Any]    =				StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,						revision="fp16"      ,						torch_dtype=torch.floataa	)
  lowerCamelCase :					Optional[Any]    =				pipe.to("cuda"	)
  lowerCamelCase :					List[str]    =				Image.open("../../docs/source/imgs/diffusers_library.jpg"	)

  def callback(SCREAMING_SNAKE_CASE_	):
    print(f"""progress: {obj['progress']:.4f}"""	)
    obj["image"].save("diffusers_library_progress.jpg"	)

  lowerCamelCase :					int    =				pipe(image=SCREAMING_SNAKE_CASE_      ,						prompt="Black font, white background, vector"      ,						noise_level=40      ,						callback=SCREAMING_SNAKE_CASE_	)
  final_image.save("diffusers_library.jpg"	)


if __name__ == "__main__":
   main()



