






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_					:				str    ,   UpperCamelCase_					:				Dict=[]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						size[0] - overlap_pixels * 2
						lowerCAmelCase__       =						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__       =						np.ones((size_y, size_x)    ,   dtype=np.uinta   ) * 255
						lowerCAmelCase__       =						np.pad(UpperCamelCase_    ,   mode="linear_ramp"    ,   pad_width=UpperCamelCase_    ,   end_values=0   )

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


def        _a  (     UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				List[str]   ) ->   Union[str, Any]:
						"""simple docstring"""







						return max(UpperCamelCase_    ,   min(UpperCamelCase_    ,   UpperCamelCase_   )   )


def        _a  (     UpperCamelCase_					:				[int]    ,   UpperCamelCase_					:				[int]    ,   UpperCamelCase_					:				[int]   ) ->   List[Any]:
						"""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]   ) ->   Optional[Any]:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						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_					:				Union[str, Any]    ,   UpperCamelCase_					:				int   ) ->   Dict:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Dict   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						n % d
						return n - divisor


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = 350		,    )->					int:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    **__UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





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


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = 75		,    __UpperCAmelCase = 9.0		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 0.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 128		,    __UpperCAmelCase = 32		,    __UpperCAmelCase = 32		,    )->					Optional[int]:







							'''simple docstring'''





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





def        _a  (     ) ->   Optional[int]:
						"""simple docstring"""







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

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

						lowerCAmelCase__       =						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 unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =None
	a_         =BloomTokenizerFast
	a_         =BloomTokenizerFast
	a_         =True
	a_         =False
	a_         ="""tokenizer_file"""
	a_         ={"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							super().setUp()
							lowerCAmelCase__       =						BloomTokenizerFast.from_pretrained("bigscience/tokenizer"       )
							tokenizer.save_pretrained(self.tmpdirname       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							kwargs.update(self.special_tokens_map       )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()

							lowerCAmelCase__       =						["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase__       =						[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

							lowerCAmelCase__       =						tokenizer.batch_encode_plus(__UpperCAmelCase       )["input_ids"]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						tokenizer.batch_decode(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=6       )->					str:







							'''simple docstring'''





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
																			lowerCAmelCase__       =						self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			# tokenizer_r.pad_token = None # Hotfixing padding = None
																			# Simple input
																			lowerCAmelCase__       =						"This is a simple input"
																			lowerCAmelCase__       =						["This is a simple input 1", "This is a simple input 2"]
																			lowerCAmelCase__       =						("This is a simple input", "This is a pair")
																			lowerCAmelCase__       =						[
																			    ("This is a simple input 1", "This is a simple input 2"),
																			    ("This is a simple pair 1", "This is a simple pair 2"),
																			]

																			# Simple input tests
																			try:
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )

																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																			except ValueError:
																									self.fail("Bloom Tokenizer should be able to deal with padding"       )

																			lowerCAmelCase__       =						None  # Hotfixing padding = None
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()
							lowerCAmelCase__       =						load_dataset("xnli"		,    "all_languages"		,    split="test"		,    streaming=__UpperCAmelCase       )

							lowerCAmelCase__       =						next(iter(__UpperCAmelCase       )       )["premise"]  # pick up one data
							lowerCAmelCase__       =						list(sample_data.values()       )

							lowerCAmelCase__       =						list(map(tokenizer.encode		,    __UpperCAmelCase       )       )
							lowerCAmelCase__       =						[tokenizer.decode(__UpperCAmelCase		,    clean_up_tokenization_spaces=__UpperCAmelCase       ) for x in output_tokens]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


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







							'''simple docstring'''





							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )		,    1       )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )		,    1       )






