



"""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_(   lowercase_     :							Optional[int]						,   lowercase_     :							List[str]						,   lowercase_     :							List[str]=[]							)	->       Dict:
				_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							) * 2_55
				_lowerCamelCase        =				np.pad(lowercase_						,   mode='''linear_ramp'''						,   pad_width=lowercase_						,   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        lowerCAmelCase_(   lowercase_     :							Dict						,   lowercase_     :							Any						,   lowercase_     :							List[str]							)	->       int:
				return max(lowercase_						,   min(lowercase_						,   lowercase_							)							)



def        lowerCAmelCase_(   lowercase_     :							[int]						,   lowercase_     :							[int]						,   lowercase_     :							[int]							)	->       List[Any]:
				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_(   lowercase_     :							[int]						,   lowercase_     :							int						,   lowercase_     :							[int]							)	->       Optional[Any]:
				_lowerCamelCase        =				list(lowercase_							)
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				_lowerCamelCase        =				clamp_rect(lowercase_						,   [0, 0]						,   [image_size[0], image_size[1]]							)
				return rect



def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							str						,   lowercase_     :							Tuple						,   lowercase_     :							Dict							)	->       Optional[Any]:
				_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(lowercase_						,   (original_slice, 0)							)
				return result



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Dict							)	->       Optional[Any]:
				_lowerCamelCase        =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
				_lowerCamelCase        =				tile.crop(lowercase_							)
				return tile



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[int]							)	->       Union[str, Any]:
				_lowerCamelCase        =				n % d
				return n - divisor


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 3_5_0				,       ):
									super().__init__(
									    vae=lowerCamelCase__				,       text_encoder=lowerCamelCase__				,       tokenizer=lowerCamelCase__				,       unet=lowerCamelCase__				,       low_res_scheduler=lowerCamelCase__				,       scheduler=lowerCamelCase__				,       max_noise_level=lowerCamelCase__				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ):
									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(lowerCamelCase__				,       lowerCamelCase__				,       image.size   )
									_lowerCamelCase        =				image.crop(lowerCamelCase__   )
									_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				,       lowerCamelCase__   )
									_lowerCamelCase        =				squeeze_tile(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
									_lowerCamelCase        =				to_input.size
									_lowerCamelCase        =				to_input.resize((tile_size, tile_size)				,       Image.BICUBIC   )
									_lowerCamelCase        =				super(lowerCamelCase__				,       self   ).__call__(image=lowerCamelCase__				,       **lowerCamelCase__   ).images[0]
									_lowerCamelCase        =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				,       Image.BICUBIC   )
									_lowerCamelCase        =				unsqueeze_tile(lowerCamelCase__				,       lowerCamelCase__   )
									_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=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				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 7_5				,       lowerCamelCase__ = 9.0				,       lowerCamelCase__ = 5_0				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 1				,       lowerCamelCase__ = 0.0				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 1				,       lowerCamelCase__ = 1_2_8				,       lowerCamelCase__ = 3_2				,       lowerCamelCase__ = 3_2				,       ):
									_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(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        lowerCAmelCase_(   )	->       Dict:
				# Run a demo
				_lowerCamelCase        =				'''stabilityai/stable-diffusion-x4-upscaler'''
				_lowerCamelCase        =				StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase_						,   revision='''fp16'''						,   torch_dtype=torch.floataa							)
				_lowerCamelCase        =				pipe.to('''cuda'''							)
				_lowerCamelCase        =				Image.open('''../../docs/source/imgs/diffusers_library.jpg'''							)

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

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


if __name__ == "__main__":
			main()





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
