





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class UpperCAmelCase							(		unittest.TestCase							):



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   tempfile.mkdtemp()

					# fmt: off
					__UpperCAmelCase					=   ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
					# fmt: on
					__UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)
					__UpperCAmelCase					=   ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
					__UpperCAmelCase					=   {'unk_token': '<unk>'}

					__UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']					)
					__UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']					)
					with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'					) as fp:
						fp.write(json.dumps(__A					) + '\n'					)
					with open(self.merges_file     ,			'w'     ,			encoding='utf-8'					) as fp:
						fp.write('\n'.join(__A					)					)

					__UpperCAmelCase					=   {
					    'do_resize': True,
					    'size': 20,
					    'do_center_crop': True,
					    'crop_size': 18,
					    'do_normalize': True,
					    'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					__UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			__A					)
					with open(self.image_processor_file     ,			'w'     ,			encoding='utf-8'					) as fp:
						json.dump(__A     ,			__A					)



				def 					__lowerCamelCase						(  self     ,			**__A					):
					return CLIPTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



				def 					__lowerCamelCase						(  self     ,			**__A					):
					return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,			**__A					)



				def 					__lowerCamelCase						(  self     ,			**__A					):
					return ViTImageProcessor.from_pretrained(self.tmpdirname     ,			**__A					)



				def 					__lowerCamelCase						(  self					):
					shutil.rmtree(self.tmpdirname					)



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   [np.random.randint(255     ,			size=(3, 30, 400)     ,			dtype=np.uinta					)]

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

					return image_inputs



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   self.get_tokenizer()
					__UpperCAmelCase					=   self.get_rust_tokenizer()
					__UpperCAmelCase					=   self.get_image_processor()

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)
					processor_slow.save_pretrained(self.tmpdirname					)
					__UpperCAmelCase					=   CLIPSegProcessor.from_pretrained(self.tmpdirname     ,			use_fast=__A					)

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)
					processor_fast.save_pretrained(self.tmpdirname					)
					__UpperCAmelCase					=   CLIPSegProcessor.from_pretrained(self.tmpdirname					)

					self.assertEqual(processor_slow.tokenizer.get_vocab()     ,			tokenizer_slow.get_vocab()					)
					self.assertEqual(processor_fast.tokenizer.get_vocab()     ,			tokenizer_fast.get_vocab()					)
					self.assertEqual(tokenizer_slow.get_vocab()     ,			tokenizer_fast.get_vocab()					)
					self.assertIsInstance(processor_slow.tokenizer     ,			__A					)
					self.assertIsInstance(processor_fast.tokenizer     ,			__A					)

					self.assertEqual(processor_slow.image_processor.to_json_string()     ,			image_processor.to_json_string()					)
					self.assertEqual(processor_fast.image_processor.to_json_string()     ,			image_processor.to_json_string()					)
					self.assertIsInstance(processor_slow.image_processor     ,			__A					)
					self.assertIsInstance(processor_fast.image_processor     ,			__A					)



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=self.get_tokenizer()     ,			image_processor=self.get_image_processor()					)
					processor.save_pretrained(self.tmpdirname					)

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

					__UpperCAmelCase					=   CLIPSegProcessor.from_pretrained(
					    self.tmpdirname     ,			bos_token='(BOS)'     ,			eos_token='(EOS)'     ,			do_normalize=__A     ,			padding_value=1.0					)

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

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



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   self.get_image_processor()
					__UpperCAmelCase					=   self.get_tokenizer()

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)

					__UpperCAmelCase					=   self.prepare_image_inputs()

					__UpperCAmelCase					=   image_processor(__A     ,			return_tensors='np'					)
					__UpperCAmelCase					=   processor(images=__A     ,			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 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   self.get_image_processor()
					__UpperCAmelCase					=   self.get_tokenizer()

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)

					__UpperCAmelCase					=   'lower newer'

					__UpperCAmelCase					=   processor(text=__A					)

					__UpperCAmelCase					=   tokenizer(__A					)

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



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   self.get_image_processor()
					__UpperCAmelCase					=   self.get_tokenizer()

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)

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

					__UpperCAmelCase					=   processor(text=__A     ,			images=__A					)

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

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



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   self.get_image_processor()
					__UpperCAmelCase					=   self.get_tokenizer()

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)

					__UpperCAmelCase					=   self.prepare_image_inputs()
					__UpperCAmelCase					=   self.prepare_image_inputs()

					__UpperCAmelCase					=   processor(images=__A     ,			visual_prompt=__A					)

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

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




				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   self.get_image_processor()
					__UpperCAmelCase					=   self.get_tokenizer()

					__UpperCAmelCase					=   CLIPSegProcessor(tokenizer=__A     ,			image_processor=__A					)

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

					__UpperCAmelCase					=   processor.batch_decode(__A					)
					__UpperCAmelCase					=   tokenizer.batch_decode(__A					)

					self.assertListEqual(__A     ,			__A					)












'''simple docstring'''




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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=[]    )->     Any:
 __UpperCAmelCase					=   size[0] - overlap_pixels * 2
 __UpperCAmelCase					=   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
 __UpperCAmelCase					=   np.ones((size_y, size_x)       ,    dtype=np.uinta    ) * 2_55
 __UpperCAmelCase					=   np.pad(_lowerCAmelCase       ,    mode='linear_ramp'       ,    pad_width=_lowerCAmelCase       ,    end_values=0    )

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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Any:
 return max(_lowerCAmelCase       ,    min(_lowerCAmelCase       ,    _lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 __UpperCAmelCase					=   clamp_rect(_lowerCAmelCase       ,    [0, 0]       ,    [image_size[0], image_size[1]]    )
 return rect







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Tuple:
 __UpperCAmelCase					=   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(_lowerCAmelCase       ,    (original_slice, 0)    )
 return result







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase					=   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 __UpperCAmelCase					=   tile.crop(_lowerCAmelCase    )
 return tile







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __UpperCAmelCase					=   n % d
 return n - divisor






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A = 350     ,			):
     super().__init__(
         vae=__A     ,			text_encoder=__A     ,			tokenizer=__A     ,			unet=__A     ,			low_res_scheduler=__A     ,			scheduler=__A     ,			max_noise_level=__A     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			**__A					):
     torch.manual_seed(0					)
     __UpperCAmelCase					=   (
         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					),
     )
     __UpperCAmelCase					=   add_overlap_rect(__A     ,			__A     ,			image.size					)
     __UpperCAmelCase					=   image.crop(__A					)
     __UpperCAmelCase					=   ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     __UpperCAmelCase					=   translated_slice_x - (original_image_slice / 2)
     __UpperCAmelCase					=   max(0     ,			__A					)
     __UpperCAmelCase					=   squeeze_tile(__A     ,			__A     ,			__A     ,			__A					)
     __UpperCAmelCase					=   to_input.size
     __UpperCAmelCase					=   to_input.resize((tile_size, tile_size)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   super(__A     ,			self					).__call__(image=__A     ,			**__A					).images[0]
     __UpperCAmelCase					=   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   unsqueeze_tile(__A     ,			__A					)
     __UpperCAmelCase					=   upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   []
     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'					)
     __UpperCAmelCase					=   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     ,			):
     __UpperCAmelCase					=   Image.new('RGB'     ,			(image.size[0] * 4, image.size[1] * 4)					)
     __UpperCAmelCase					=   math.ceil(image.size[0] / tile_size					)
     __UpperCAmelCase					=   math.ceil(image.size[1] / tile_size					)
     __UpperCAmelCase					=   tcx * tcy
     __UpperCAmelCase					=   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 _lowerCAmelCase     (      )->     str:
 # Run a demo
 __UpperCAmelCase					=   'stabilityai/stable-diffusion-x4-upscaler'
 __UpperCAmelCase					=   StableDiffusionTiledUpscalePipeline.from_pretrained(_lowerCAmelCase       ,    revision='fp16'       ,    torch_dtype=torch.floataa    )
 __UpperCAmelCase					=   pipe.to('cuda'    )
 __UpperCAmelCase					=   Image.open('../../docs/source/imgs/diffusers_library.jpg'    )

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

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


if __name__ == "__main__":
    main()





