






"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowerCamelCase_       			=   {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}







@is_pipeline_test
class     UpperCamelCase_ (unittest.TestCase  ):
	__magic_name__			   =       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	__magic_name__			   =       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		__magic_name__			   =       {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		__magic_name__			   =       {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Optional[int]		)		->    Optional[Any]:
				UpperCAmelCase_  :				str      =      ZeroShotClassificationPipeline(
				    model=lowerCAmelCase_			,			tokenizer=lowerCAmelCase_			,			candidate_labels=["polics", "health"]		)
				return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				int		)		->    str:
				UpperCAmelCase_  :				List[str]      =      classifier("Who are you voting for in 2020?"			,			candidate_labels="politics"		)
				self.assertEqual(lowerCAmelCase_			,			{"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		)]}		)

				# No kwarg
				UpperCAmelCase_  :				List[str]      =      classifier("Who are you voting for in 2020?"			,			["politics"]		)
				self.assertEqual(lowerCAmelCase_			,			{"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		)]}		)

				UpperCAmelCase_  :				Optional[int]      =      classifier("Who are you voting for in 2020?"			,			candidate_labels=["politics"]		)
				self.assertEqual(lowerCAmelCase_			,			{"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		)]}		)

				UpperCAmelCase_  :				int      =      classifier("Who are you voting for in 2020?"			,			candidate_labels="politics, public health"		)
				self.assertEqual(
				    lowerCAmelCase_			,			{"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)]}		)
				self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]		)		)			,			1.0		)

				UpperCAmelCase_  :				Tuple      =      classifier("Who are you voting for in 2020?"			,			candidate_labels=["politics", "public health"]		)
				self.assertEqual(
				    lowerCAmelCase_			,			{"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)]}		)
				self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]		)		)			,			1.0		)

				UpperCAmelCase_  :				Union[str, Any]      =      classifier(
				    "Who are you voting for in 2020?"			,			candidate_labels="politics"			,			hypothesis_template="This text is about {}"		)
				self.assertEqual(lowerCAmelCase_			,			{"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		)]}		)

				# https://github.com/huggingface/transformers/issues/13846
				UpperCAmelCase_  :				List[str]      =      classifier(["I am happy"]			,			["positive", "negative"]		)
				self.assertEqual(
				    lowerCAmelCase_			,			[
				        {"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)]}
				        for i in range(1		)
				    ]			,			)
				UpperCAmelCase_  :				Optional[Any]      =      classifier(["I am happy", "I am sad"]			,			["positive", "negative"]		)
				self.assertEqual(
				    lowerCAmelCase_			,			[
				        {"sequence": ANY(lowerCAmelCase_		), "labels": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)], "scores": [ANY(lowerCAmelCase_		), ANY(lowerCAmelCase_		)]}
				        for i in range(2		)
				    ]			,			)

				with self.assertRaises(lowerCAmelCase_		):
							classifier(""			,			candidate_labels="politics"		)

				with self.assertRaises(lowerCAmelCase_		):
							classifier(lowerCAmelCase_			,			candidate_labels="politics"		)

				with self.assertRaises(lowerCAmelCase_		):
							classifier("Who are you voting for in 2020?"			,			candidate_labels=""		)

				with self.assertRaises(lowerCAmelCase_		):
							classifier("Who are you voting for in 2020?"			,			candidate_labels=lowerCAmelCase_		)

				with self.assertRaises(lowerCAmelCase_		):
							classifier(
							    "Who are you voting for in 2020?"			,			candidate_labels="politics"			,			hypothesis_template="Not formatting template"			,			)

				with self.assertRaises(lowerCAmelCase_		):
							classifier(
							    "Who are you voting for in 2020?"			,			candidate_labels="politics"			,			hypothesis_template=lowerCAmelCase_			,			)

				self.run_entailment_id(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Pipeline		)		->    List[Any]:
				UpperCAmelCase_  :				Dict      =      zero_shot_classifier.model.config
				UpperCAmelCase_  :				Optional[int]      =      config.labelaid
				UpperCAmelCase_  :				Optional[Any]      =      zero_shot_classifier.entailment_id

				UpperCAmelCase_  :				Tuple      =      {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
				self.assertEqual(zero_shot_classifier.entailment_id			,			-1		)

				UpperCAmelCase_  :				List[Any]      =      {"entailment": 0, "neutral": 1, "contradiction": 2}
				self.assertEqual(zero_shot_classifier.entailment_id			,			0		)

				UpperCAmelCase_  :				List[str]      =      {"ENTAIL": 0, "NON-ENTAIL": 1}
				self.assertEqual(zero_shot_classifier.entailment_id			,			0		)

				UpperCAmelCase_  :				str      =      {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
				self.assertEqual(zero_shot_classifier.entailment_id			,			2		)

				UpperCAmelCase_  :				int      =      original_labelaid
				self.assertEqual(lowerCAmelCase_			,			zero_shot_classifier.entailment_id		)





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[Any]      =      pipeline(
				    "zero-shot-classification"			,			model="sshleifer/tiny-distilbert-base-cased-distilled-squad"			,			framework="pt"			,			)
				# There was a regression in 4.10 for this
				# Adding a test so we don't make the mistake again.
				# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
				zero_shot_classifier(
				    "Who are you voting for in 2020?" * 100			,			candidate_labels=["politics", "public health", "science"]		)





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    List[Any]:
				UpperCAmelCase_  :				str      =      pipeline(
				    "zero-shot-classification"			,			model="sshleifer/tiny-distilbert-base-cased-distilled-squad"			,			framework="pt"			,			)
				UpperCAmelCase_  :				int      =      zero_shot_classifier(
				    "Who are you voting for in 2020?"			,			candidate_labels=["politics", "public health", "science"]		)

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        "sequence": "Who are you voting for in 2020?",
				        "labels": ["science", "public health", "politics"],
				        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
				    }			,			)





	@require_tf
	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Tuple:
				UpperCAmelCase_  :				Optional[Any]      =      pipeline(
				    "zero-shot-classification"			,			model="sshleifer/tiny-distilbert-base-cased-distilled-squad"			,			framework="tf"			,			)
				UpperCAmelCase_  :				str      =      zero_shot_classifier(
				    "Who are you voting for in 2020?"			,			candidate_labels=["politics", "public health", "science"]		)

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        "sequence": "Who are you voting for in 2020?",
				        "labels": ["science", "public health", "politics"],
				        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
				    }			,			)





	@slow
	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				List[Any]      =      pipeline("zero-shot-classification"			,			model="roberta-large-mnli"			,			framework="pt"		)
				UpperCAmelCase_  :				List[str]      =      zero_shot_classifier(
				    "Who are you voting for in 2020?"			,			candidate_labels=["politics", "public health", "science"]		)

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        "sequence": "Who are you voting for in 2020?",
				        "labels": ["politics", "public health", "science"],
				        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
				    }			,			)
				UpperCAmelCase_  :				List[str]      =      zero_shot_classifier(
				    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
				    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
				    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
				    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
				    " machine translation tasks show these models to be superior in quality while being more parallelizable"
				    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
				    " English-to-German translation task, improving over the existing best results, including ensembles by"
				    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
				    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
				    " fraction of the training costs of the best models from the literature. We show that the Transformer"
				    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
				    " large and limited training data."			,			candidate_labels=["machine learning", "statistics", "translation", "vision"]			,			multi_label=lowerCAmelCase_			,			)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        "sequence": (
				            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
				            " networks in an encoder-decoder configuration. The best performing models also connect the"
				            " encoder and decoder through an attention mechanism. We propose a new simple network"
				            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
				            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
				            " superior in quality while being more parallelizable and requiring significantly less time to"
				            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
				            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
				            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
				            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
				            " costs of the best models from the literature. We show that the Transformer generalizes well to"
				            " other tasks by applying it successfully to English constituency parsing both with large and"
				            " limited training data."
				        ),
				        "labels": ["translation", "machine learning", "vision", "statistics"],
				        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
				    }			,			)





	@slow
	@require_tf
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Dict:
				UpperCAmelCase_  :				Tuple      =      pipeline("zero-shot-classification"			,			model="roberta-large-mnli"			,			framework="tf"		)
				UpperCAmelCase_  :				int      =      zero_shot_classifier(
				    "Who are you voting for in 2020?"			,			candidate_labels=["politics", "public health", "science"]		)

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        "sequence": "Who are you voting for in 2020?",
				        "labels": ["politics", "public health", "science"],
				        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
				    }			,			)
				UpperCAmelCase_  :				List[Any]      =      zero_shot_classifier(
				    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
				    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
				    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
				    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
				    " machine translation tasks show these models to be superior in quality while being more parallelizable"
				    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
				    " English-to-German translation task, improving over the existing best results, including ensembles by"
				    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
				    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
				    " fraction of the training costs of the best models from the literature. We show that the Transformer"
				    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
				    " large and limited training data."			,			candidate_labels=["machine learning", "statistics", "translation", "vision"]			,			multi_label=lowerCAmelCase_			,			)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        "sequence": (
				            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
				            " networks in an encoder-decoder configuration. The best performing models also connect the"
				            " encoder and decoder through an attention mechanism. We propose a new simple network"
				            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
				            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
				            " superior in quality while being more parallelizable and requiring significantly less time to"
				            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
				            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
				            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
				            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
				            " costs of the best models from the literature. We show that the Transformer generalizes well to"
				            " other tasks by applying it successfully to English constituency parsing both with large and"
				            " limited training data."
				        ),
				        "labels": ["translation", "machine learning", "vision", "statistics"],
				        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
				    }			,			)











"""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()



