




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 		_A					(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Union[str, Any]=[]				)    -> int:
							'''simple docstring'''


							__lowercase      =    size[0] - overlap_pixels * 2
							__lowercase      =    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
							__lowercase      =    np.ones((size_y, size_x)				,       dtype=np.uinta				) * 255
							__lowercase      =    np.pad(UpperCamelCase__				,       mode='''linear_ramp'''				,       pad_width=UpperCamelCase__				,       end_values=0				)

							if "l" in remove_borders:
														__lowercase      =    mask[:, overlap_pixels : mask.shape[1]]
							if "r" in remove_borders:
														__lowercase      =    mask[:, 0 : mask.shape[1] - overlap_pixels]
							if "t" in remove_borders:
														__lowercase      =    mask[overlap_pixels : mask.shape[0], :]
							if "b" in remove_borders:
														__lowercase      =    mask[0 : mask.shape[0] - overlap_pixels, :]
							return mask







def 		_A					(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Any				)    -> str:
							'''simple docstring'''


							return max(UpperCamelCase__				,       min(UpperCamelCase__				,       UpperCamelCase__				)				)







def 		_A					(       UpperCamelCase__							:			[int]				,       UpperCamelCase__							:			[int]				,       UpperCamelCase__							:			[int]				)    -> Optional[int]:
							'''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 		_A					(       UpperCamelCase__							:			[int]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			[int]				)    -> int:
							'''simple docstring'''


							__lowercase      =    list(UpperCamelCase__				)
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							__lowercase      =    clamp_rect(UpperCamelCase__				,       [0, 0]				,       [image_size[0], image_size[1]]				)
							return rect







def 		_A					(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Any				)    -> Union[str, Any]:
							'''simple docstring'''


							__lowercase      =    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(UpperCamelCase__				,       (original_slice, 0)				)
							return result







def 		_A					(       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Optional[int]				)    -> Any:
							'''simple docstring'''


							__lowercase      =    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
							__lowercase      =    tile.crop(UpperCamelCase__				)
							return tile







def 		_A					(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Union[str, Any]				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    n % d
							return n - divisor







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Optional[int]				,							lowerCamelCase__ :     AutoencoderKL				,							lowerCamelCase__ :     CLIPTextModel				,							lowerCamelCase__ :     CLIPTokenizer				,							lowerCamelCase__ :     UNetaDConditionModel				,							lowerCamelCase__ :     DDPMScheduler				,							lowerCamelCase__ :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,							lowerCamelCase__ :     int = 350				,							)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Dict				,							**lowerCamelCase__ :     Optional[int]				)    ->						Optional[Any]:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    (
													    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				),
													)
													__lowercase      =    add_overlap_rect(lowerCamelCase__				,							lowerCamelCase__				,							image.size				)
													__lowercase      =    image.crop(lowerCamelCase__				)
													__lowercase      =    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
													__lowercase      =    translated_slice_x - (original_image_slice / 2)
													__lowercase      =    max(0				,							lowerCamelCase__				)
													__lowercase      =    squeeze_tile(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
													__lowercase      =    to_input.size
													__lowercase      =    to_input.resize((tile_size, tile_size)				,							Image.BICUBIC				)
													__lowercase      =    super(lowerCamelCase__				,							self				).__call__(image=lowerCamelCase__				,							**lowerCamelCase__				).images[0]
													__lowercase      =    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				,							Image.BICUBIC				)
													__lowercase      =    unsqueeze_tile(lowerCamelCase__				,							lowerCamelCase__				)
													__lowercase      =    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)				,							Image.BICUBIC				)
													__lowercase      =    []
													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'''				)
													__lowercase      =    Image.fromarray(
													    make_transparency_mask(
													        (upscaled_tile.size[0], upscaled_tile.size[1])				,							tile_border * 4				,							remove_borders=lowerCamelCase__				)				,							mode='''L'''				,							)
													final_image.paste(
													    lowerCamelCase__				,							(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)				,							lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     Union[str, List[str]]				,							lowerCamelCase__ :     Union[PIL.Image.Image, List[PIL.Image.Image]]				,							lowerCamelCase__ :     int = 75				,							lowerCamelCase__ :     float = 9.0				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[str, List[str]]] = None				,							lowerCamelCase__ :     Optional[int] = 1				,							lowerCamelCase__ :     float = 0.0				,							lowerCamelCase__ :     Optional[torch.Generator] = None				,							lowerCamelCase__ :     Optional[torch.FloatTensor] = None				,							lowerCamelCase__ :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 128				,							lowerCamelCase__ :     int = 32				,							lowerCamelCase__ :     int = 32				,							)    ->						Optional[Any]:
													"""simple docstring"""


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







def 		_A					(       )    -> str:
							'''simple docstring'''


							__lowercase      =    '''stabilityai/stable-diffusion-x4-upscaler'''
							__lowercase      =    StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase__				,       revision='''fp16'''				,       torch_dtype=torch.floataa				)
							__lowercase      =    pipe.to('''cuda'''				)
							__lowercase      =    Image.open('''../../docs/source/imgs/diffusers_library.jpg'''				)

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

							__lowercase      =    pipe(image=UpperCamelCase__				,       prompt='''Black font, white background, vector'''				,       noise_level=40				,       callback=UpperCamelCase__				)
							final_image.save('''diffusers_library.jpg'''				)


if __name__ == "__main__":
			main()












import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    tempfile.mkdtemp()

													__lowercase      =    BlipImageProcessor()
													__lowercase      =    GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''				)

													__lowercase      =    BlipaProcessor(lowerCamelCase__				,							lowerCamelCase__				)

													processor.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							**lowerCamelCase__ :     Any				)    ->						str:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).tokenizer






						def     UpperCAmelCase_      ( self :     Dict				,							**lowerCamelCase__ :     List[str]				)    ->						Dict:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).image_processor






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													shutil.rmtree(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta				)]

													__lowercase      =    [Image.fromarray(np.moveaxis(lowerCamelCase__				,							0				,							-1				)				) for x in image_inputs]

													return image_inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    BlipaProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													__lowercase      =    self.get_tokenizer(bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				)
													__lowercase      =    self.get_image_processor(do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													__lowercase      =    BlipaProcessor.from_pretrained(
													    self.tmpdirname				,							bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				,							do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													self.assertEqual(processor.tokenizer.get_vocab()				,							tokenizer_add_kwargs.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,							lowerCamelCase__				)

													self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    image_processor(lowerCamelCase__				,							return_tensors='''np'''				)
													__lowercase      =    processor(images=lowerCamelCase__				,							return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1e-2				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''

													__lowercase      =    processor(text=lowerCamelCase__				)

													__lowercase      =    tokenizer(lowerCamelCase__				,							return_token_type_ids=lowerCamelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,							encoded_processor[key]				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)

													# test if it raises when no input is passed
													with pytest.raises(lowerCamelCase__				):
																				processor()






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__lowercase      =    processor.batch_decode(lowerCamelCase__				)
													__lowercase      =    tokenizer.batch_decode(lowerCamelCase__				)

													self.assertListEqual(lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)






