






"""simple docstring"""
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''MCTCTFeatureExtractor'''
	__magic_name__			   =       '''AutoTokenizer'''





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int		)		->    List[str]:
				super().__init__(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      self.feature_extractor
				UpperCAmelCase_  :				str      =      False





	def __call__(   self      :				Optional[int]			,			*lowerCAmelCase_      :				Union[str, Any]			,			**lowerCAmelCase_      :				Any		)		->    int:
				# For backward compatibility
				if self._in_target_context_manager:
							return self.current_processor(*lowerCAmelCase_			,			**lowerCAmelCase_		)

				if "raw_speech" in kwargs:
							warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."		)
							UpperCAmelCase_  :				Tuple      =      kwargs.pop("raw_speech"		)
				else:
							UpperCAmelCase_  :				Optional[int]      =      kwargs.pop("audio"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      kwargs.pop("sampling_rate"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      kwargs.pop("text"			,			lowerCAmelCase_		)
				if len(lowerCAmelCase_		) > 0:
							UpperCAmelCase_  :				List[str]      =      args[0]
							UpperCAmelCase_  :				List[str]      =      args[1:]

				if audio is None and text is None:
							raise ValueError("You need to specify either an `audio` or `text` input to process."		)

				if audio is not None:
							UpperCAmelCase_  :				str      =      self.feature_extractor(lowerCAmelCase_			,			*lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			**lowerCAmelCase_		)
				if text is not None:
							UpperCAmelCase_  :				Optional[int]      =      self.tokenizer(lowerCAmelCase_			,			**lowerCAmelCase_		)

				if text is None:
							return inputs
				elif audio is None:
							return encodings
				else:
							UpperCAmelCase_  :				str      =      encodings["input_ids"]
							return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			*lowerCAmelCase_      :				Optional[int]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    int:
				return self.tokenizer.batch_decode(*lowerCAmelCase_			,			**lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			*lowerCAmelCase_      :				Any			,			**lowerCAmelCase_      :				List[str]		)		->    Union[str, Any]:
				# For backward compatibility
				if self._in_target_context_manager:
							return self.current_processor.pad(*lowerCAmelCase_			,			**lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      kwargs.pop("input_features"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      kwargs.pop("labels"			,			lowerCAmelCase_		)
				if len(lowerCAmelCase_		) > 0:
							UpperCAmelCase_  :				Optional[int]      =      args[0]
							UpperCAmelCase_  :				Optional[int]      =      args[1:]

				if input_features is not None:
							UpperCAmelCase_  :				List[str]      =      self.feature_extractor.pad(lowerCAmelCase_			,			*lowerCAmelCase_			,			**lowerCAmelCase_		)
				if labels is not None:
							UpperCAmelCase_  :				Tuple      =      self.tokenizer.pad(lowerCAmelCase_			,			**lowerCAmelCase_		)

				if labels is None:
							return input_features
				elif input_features is None:
							return labels
				else:
							UpperCAmelCase_  :				List[Any]      =      labels["input_ids"]
							return input_features





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			*lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				int		)		->    Optional[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_			,			**lowerCAmelCase_		)





	@contextmanager
	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				warnings.warn(
				    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
				    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
				    "your audio inputs, or in a separate call."		)
				UpperCAmelCase_  :				int      =      True
				UpperCAmelCase_  :				Union[str, Any]      =      self.tokenizer
				yield
				UpperCAmelCase_  :				Optional[Any]      =      self.feature_extractor
				UpperCAmelCase_  :				int      =      False











"""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        snake_case   (     A__			,A__			,A__=[]		):
			UpperCAmelCase_  :				int      =      size[0] - overlap_pixels * 2
			UpperCAmelCase_  :				Dict      =      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_  :				Union[str, Any]      =      np.ones((size_y, size_x)			,dtype=np.uinta		) * 2_55
			UpperCAmelCase_  :				Optional[int]      =      np.pad(A__			,mode="linear_ramp"			,pad_width=A__			,end_values=0		)

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

def        snake_case   (     A__			,A__			,A__		):
			return max(A__			,min(A__			,A__		)		)

def        snake_case   (     A__			,A__			,A__		):
			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        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      list(A__		)
			rect[0] -= overlap
			rect[1] -= overlap
			rect[2] += overlap
			rect[3] += overlap
			UpperCAmelCase_  :				Optional[Any]      =      clamp_rect(A__			,[0, 0]			,[image_size[0], image_size[1]]		)
			return rect

def        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				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(A__			,(original_slice, 0)		)
			return result

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Optional[int]      =      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
			UpperCAmelCase_  :				Optional[int]      =      tile.crop(A__		)
			return tile

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      n % d
			return n - divisor







class     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				AutoencoderKL			,			lowerCAmelCase_      :				CLIPTextModel			,			lowerCAmelCase_      :				CLIPTokenizer			,			lowerCAmelCase_      :				UNetaDConditionModel			,			lowerCAmelCase_      :				DDPMScheduler			,			lowerCAmelCase_      :				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,			lowerCAmelCase_      :				int = 350			,			)		->    Optional[Any]:
				super().__init__(
				    vae=lowerCAmelCase_			,			text_encoder=lowerCAmelCase_			,			tokenizer=lowerCAmelCase_			,			unet=lowerCAmelCase_			,			low_res_scheduler=lowerCAmelCase_			,			scheduler=lowerCAmelCase_			,			max_noise_level=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any			,			**lowerCAmelCase_      :				List[str]		)		->    Dict:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Optional[Any]      =      (
				    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_  :				Dict      =      add_overlap_rect(lowerCAmelCase_			,			lowerCAmelCase_			,			image.size		)
				UpperCAmelCase_  :				Tuple      =      image.crop(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
				UpperCAmelCase_  :				int      =      translated_slice_x - (original_image_slice / 2)
				UpperCAmelCase_  :				Any      =      max(0			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      squeeze_tile(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      to_input.size
				UpperCAmelCase_  :				Optional[int]      =      to_input.resize((tile_size, tile_size)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				Optional[Any]      =      super(lowerCAmelCase_			,			self		).__call__(image=lowerCAmelCase_			,			**lowerCAmelCase_		).images[0]
				UpperCAmelCase_  :				Union[str, Any]      =      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				List[Any]      =      unsqueeze_tile(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				List[Any]      =      []
				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_  :				str      =      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      :				int			,			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			,			)		->    int:
				UpperCAmelCase_  :				List[Any]      =      Image.new("RGB"			,			(image.size[0] * 4, image.size[1] * 4)		)
				UpperCAmelCase_  :				Any      =      math.ceil(image.size[0] / tile_size		)
				UpperCAmelCase_  :				str      =      math.ceil(image.size[1] / tile_size		)
				UpperCAmelCase_  :				Any      =      tcx * tcy
				UpperCAmelCase_  :				List[Any]      =      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        snake_case   (     ):
			# Run a demo
			UpperCAmelCase_  :				List[str]      =      "stabilityai/stable-diffusion-x4-upscaler"
			UpperCAmelCase_  :				Optional[Any]      =      StableDiffusionTiledUpscalePipeline.from_pretrained(A__			,revision="fp16"			,torch_dtype=torch.floataa		)
			UpperCAmelCase_  :				Optional[int]      =      pipe.to("cuda"		)
			UpperCAmelCase_  :				Tuple      =      Image.open("../../docs/source/imgs/diffusers_library.jpg"		)

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

			UpperCAmelCase_  :				Union[str, Any]      =      pipe(image=A__			,prompt="Black font, white background, vector"			,noise_level=40			,callback=A__		)
			final_image.save("diffusers_library.jpg"		)


if __name__ == "__main__":
			main()



