


'''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							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]=[]   ):
					__a			:       Any					    =  size[0] - overlap_pixels * 2
					__a			:       List[Any]					    =  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
					__a			:       Tuple					    =  np.ones((size_y, size_x)						,      dtype=np.uinta   ) * 255
					__a			:       Tuple					    =  np.pad(_SCREAMING_SNAKE_CASE						,      mode='linear_ramp'						,      pad_width=_SCREAMING_SNAKE_CASE						,      end_values=0   )

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					return max(_SCREAMING_SNAKE_CASE						,      min(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   [int]						,      _SCREAMING_SNAKE_CASE      :   [int]						,      _SCREAMING_SNAKE_CASE      :   [int]   ):
					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							(_SCREAMING_SNAKE_CASE      :   [int]						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   [int]   ):
					__a			:       int					    =  list(_SCREAMING_SNAKE_CASE   )
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					__a			:       str					    =  clamp_rect(_SCREAMING_SNAKE_CASE						,      [0, 0]						,      [image_size[0], image_size[1]]   )
					return rect






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       Tuple					    =  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
					__a			:       Any					    =  tile.crop(_SCREAMING_SNAKE_CASE   )
					return tile






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       int					    =  n % d
					return n - divisor





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				**__a							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       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							),
						)
						__a			:       List[Any]					    =  add_overlap_rect(__a						,				__a						,				image.size							)
						__a			:       Tuple					    =  image.crop(__a							)
						__a			:       Union[str, Any]					    =  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
						__a			:       Tuple					    =  translated_slice_x - (original_image_slice / 2)
						__a			:       Optional[Any]					    =  max(0						,				__a							)
						__a			:       List[Any]					    =  squeeze_tile(__a						,				__a						,				__a						,				__a							)
						__a			:       int					    =  to_input.size
						__a			:       Union[str, Any]					    =  to_input.resize((tile_size, tile_size)						,				Image.BICUBIC							)
						__a			:       str					    =  super(__a						,				self							).__call__(image=__a						,				**__a							).images[0]
						__a			:       List[Any]					    =  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC							)
						__a			:       Tuple					    =  unsqueeze_tile(__a						,				__a							)
						__a			:       Optional[Any]					    =  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC							)
						__a			:       Dict					    =  []
						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'							)
						__a			:       Dict					    =  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'''





						__a			:       Tuple					    =  Image.new('RGB'						,				(image.size[0] * 4, image.size[1] * 4)							)
						__a			:       Dict					    =  math.ceil(image.size[0] / tile_size							)
						__a			:       Optional[int]					    =  math.ceil(image.size[1] / tile_size							)
						__a			:       int					    =  tcx * tcy
						__a			:       List[str]					    =  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							():
					# Run a demo
					__a			:       Any					    =  'stabilityai/stable-diffusion-x4-upscaler'
					__a			:       int					    =  StableDiffusionTiledUpscalePipeline.from_pretrained(_SCREAMING_SNAKE_CASE						,      revision='fp16'						,      torch_dtype=torch.floataa   )
					__a			:       Union[str, Any]					    =  pipe.to('cuda'   )
					__a			:       Union[str, Any]					    =  Image.open('../../docs/source/imgs/diffusers_library.jpg'   )

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

					__a			:       Dict					    =  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()







'''simple docstring'''



import re
import string

import numpy as np

import datasets


__lowercase	:						Tuple       =	'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__lowercase	:						List[str]       =	'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__lowercase	:						Any       =	'\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION					)
class 							__UpperCamelCase				(		datasets.Metric					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return datasets.MetricInfo(
						    description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
						        {
						            'predictions': datasets.Value('string'						,				id='sequence'							),
						            'references': datasets.Value('string'						,				id='sequence'							),
						        }							)						,				reference_urls=[]						,				)





	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=None						,				__a=False						,				__a=False						,				__a=False						,				):

						'''simple docstring'''





						if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
																__a			:       Tuple					    =  np.array([re.sub(__a						,				''						,				__a							) for x in predictions]							)
																__a			:       List[Any]					    =  np.array([re.sub(__a						,				''						,				__a							) for x in references]							)
						else:
											__a			:       int					    =  np.asarray(__a							)
											__a			:       str					    =  np.asarray(__a							)

						if ignore_case:
											__a			:       Dict					    =  np.char.lower(__a							)
											__a			:       List[str]					    =  np.char.lower(__a							)

						if ignore_punctuation:
											__a			:       Dict					    =  string.punctuation.maketrans(''						,				''						,				string.punctuation							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Dict					    =  np.char.translate(__a						,				table=__a							)

						if ignore_numbers:
											__a			:       Optional[int]					    =  string.digits.maketrans(''						,				''						,				string.digits							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Optional[int]					    =  np.char.translate(__a						,				table=__a							)

						__a			:       Any					    =  predictions == references

						return {"exact_match": np.mean(__a							) * 100}



