



'''simple docstring'''




import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowerCamelCase							:Union[str, Any]				    =					re.compile(R'''\b(a|an|the)\b''', re.UNICODE)

lowerCamelCase							:List[str]				    =					None



def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""    )
			parser.add_argument("""data_file"""						,      metavar="""data.json"""						,      help="""Input data JSON file."""    )
			parser.add_argument("""pred_file"""						,      metavar="""pred.json"""						,      help="""Model predictions."""    )
			parser.add_argument(
			    """--out-file"""						,      """-o"""						,      metavar="""eval.json"""						,      help="""Write accuracy metrics to file (default is stdout)."""    )
			parser.add_argument(
			    """--na-prob-file"""						,      """-n"""						,      metavar="""na_prob.json"""						,      help="""Model estimates of probability of no answer."""    )
			parser.add_argument(
			    """--na-prob-thresh"""						,      """-t"""						,      type=lowerCamelCase__						,      default=1.0						,      help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""						,      )
			parser.add_argument(
			    """--out-image-dir"""						,      """-p"""						,      metavar="""out_images"""						,      default=lowerCamelCase__						,      help="""Save precision-recall curves to directory."""    )
			parser.add_argument("""--verbose"""						,      """-v"""						,      action="""store_true"""    )
			if len(sys.argv    ) == 1:
						parser.print_help()
						sys.exit(1    )
			return parser.parse_args()



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[str]	=     {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												A_	:						Tuple	=     bool(qa["""answers"""]["""text"""]    )
			return qid_to_has_ans



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			def remove_articles(lowerCamelCase__    ):
						return ARTICLES_REGEX.sub(""" """						,      lowerCamelCase__    )

			def white_space_fix(lowerCamelCase__    ):
						return " ".join(text.split()    )

			def remove_punc(lowerCamelCase__    ):
						A_	:						Union[str, Any]	=     set(string.punctuation    )
						return "".join(ch for ch in text if ch not in exclude    )

			def lower(lowerCamelCase__    ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__    )    )    )    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if not s:
						return []
			return normalize_answer(lowerCamelCase__    ).split()



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return int(normalize_answer(lowerCamelCase__    ) == normalize_answer(lowerCamelCase__    )    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     get_tokens(lowerCamelCase__    )
			A_	:						List[Any]	=     get_tokens(lowerCamelCase__    )
			A_	:						Optional[Any]	=     collections.Counter(lowerCamelCase__    ) & collections.Counter(lowerCamelCase__    )
			A_	:						str	=     sum(common.values()    )
			if len(lowerCamelCase__    ) == 0 or len(lowerCamelCase__    ) == 0:
						# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
						return int(gold_toks == pred_toks    )
			if num_same == 0:
						return 0
			A_	:						List[Any]	=     1.0 * num_same / len(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     1.0 * num_same / len(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     (2 * precision * recall) / (precision + recall)
			return fa



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     {}
			A_	:						Union[str, Any]	=     {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												A_	:						str	=     qa["""id"""]
												A_	:						List[Any]	=     [t for t in qa["""answers"""]["""text"""] if normalize_answer(lowerCamelCase__    )]
												if not gold_answers:
															# For unanswerable questions, only correct answer is empty string
															A_	:						List[Any]	=     [""""""]
												if qid not in preds:
															print(f'Missing prediction for {qid}'    )
															continue
												A_	:						Optional[int]	=     preds[qid]
												# Take max over all gold answers
												A_	:						Dict	=     max(compute_exact(lowerCamelCase__						,      lowerCamelCase__    ) for a in gold_answers    )
												A_	:						List[str]	=     max(compute_fa(lowerCamelCase__						,      lowerCamelCase__    ) for a in gold_answers    )
			return exact_scores, fa_scores



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     {}
			for qid, s in scores.items():
						A_	:						Union[str, Any]	=     na_probs[qid] > na_prob_thresh
						if pred_na:
									A_	:						Any	=     float(not qid_to_has_ans[qid]    )
						else:
									A_	:						Union[str, Any]	=     s
			return new_scores



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None    ):
			'''simple docstring'''





			if not qid_list:
						A_	:						Optional[int]	=     len(lowerCamelCase__    )
						return collections.OrderedDict(
						    [
						        ("""exact""", 100.0 * sum(exact_scores.values()    ) / total),
						        ("""f1""", 100.0 * sum(fa_scores.values()    ) / total),
						        ("""total""", total),
						    ]    )
			else:
						A_	:						Optional[int]	=     len(lowerCamelCase__    )
						return collections.OrderedDict(
						    [
						        ("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list    ) / total),
						        ("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list    ) / total),
						        ("""total""", total),
						    ]    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for k in new_eval:
						A_	:						int	=     new_eval[k]



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			plt.step(lowerCamelCase__						,      lowerCamelCase__						,      color="""b"""						,      alpha=0.2						,      where="""post"""    )
			plt.fill_between(lowerCamelCase__						,      lowerCamelCase__						,      step="""post"""						,      alpha=0.2						,      color="""b"""    )
			plt.xlabel("""Recall"""    )
			plt.ylabel("""Precision"""    )
			plt.xlim([0.0, 1.05]    )
			plt.ylim([0.0, 1.05]    )
			plt.title(lowerCamelCase__    )
			plt.savefig(lowerCamelCase__    )
			plt.clf()



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     sorted(lowerCamelCase__						,      key=lambda lowerCamelCase__    : na_probs[k]    )
			A_	:						Optional[int]	=     0.0
			A_	:						Any	=     1.0
			A_	:						str	=     0.0
			A_	:						Optional[Any]	=     [1.0]
			A_	:						str	=     [0.0]
			A_	:						str	=     0.0
			for i, qid in enumerate(lowerCamelCase__    ):
						if qid_to_has_ans[qid]:
									true_pos += scores[qid]
						A_	:						str	=     true_pos / float(i + 1    )
						A_	:						int	=     true_pos / float(lowerCamelCase__    )
						if i == len(lowerCamelCase__    ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
									# i.e., if we can put a threshold after this point
									avg_prec += cur_p * (cur_r - recalls[-1])
									precisions.append(lowerCamelCase__    )
									recalls.append(lowerCamelCase__    )
			if out_image:
						plot_pr_curve(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
			return {"ap": 100.0 * avg_prec}



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if out_image_dir and not os.path.exists(lowerCamelCase__    ):
						os.makedirs(lowerCamelCase__    )
			A_	:						Dict	=     sum(1 for v in qid_to_has_ans.values() if v    )
			if num_true_pos == 0:
						return
			A_	:						int	=     make_precision_recall_eval(
			    lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      out_image=os.path.join(lowerCamelCase__						,      """pr_exact.png"""    )						,      title="""Precision-Recall curve for Exact Match score"""						,      )
			A_	:						Optional[int]	=     make_precision_recall_eval(
			    lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      out_image=os.path.join(lowerCamelCase__						,      """pr_f1.png"""    )						,      title="""Precision-Recall curve for F1 score"""						,      )
			A_	:						List[Any]	=     {k: float(lowerCamelCase__    ) for k, v in qid_to_has_ans.items()}
			A_	:						Optional[int]	=     make_precision_recall_eval(
			    lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      out_image=os.path.join(lowerCamelCase__						,      """pr_oracle.png"""    )						,      title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""						,      )
			merge_eval(lowerCamelCase__						,      lowerCamelCase__						,      """pr_exact"""    )
			merge_eval(lowerCamelCase__						,      lowerCamelCase__						,      """pr_f1"""    )
			merge_eval(lowerCamelCase__						,      lowerCamelCase__						,      """pr_oracle"""    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if not qid_list:
						return
			A_	:						Union[str, Any]	=     [na_probs[k] for k in qid_list]
			A_	:						Dict	=     np.ones_like(lowerCamelCase__    ) / float(len(lowerCamelCase__    )    )
			plt.hist(lowerCamelCase__						,      weights=lowerCamelCase__						,      bins=20						,      range=(0.0, 1.0)    )
			plt.xlabel("""Model probability of no-answer"""    )
			plt.ylabel("""Proportion of dataset"""    )
			plt.title(f'Histogram of no-answer probability: {name}'    )
			plt.savefig(os.path.join(lowerCamelCase__						,      f'na_prob_hist_{name}.png'    )    )
			plt.clf()



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]    )
			A_	:						List[str]	=     num_no_ans
			A_	:						Optional[int]	=     cur_score
			A_	:						List[Any]	=     0.0
			A_	:						int	=     sorted(lowerCamelCase__						,      key=lambda lowerCamelCase__    : na_probs[k]    )
			for i, qid in enumerate(lowerCamelCase__    ):
						if qid not in scores:
									continue
						if qid_to_has_ans[qid]:
									A_	:						Dict	=     scores[qid]
						else:
									if preds[qid]:
												A_	:						int	=     -1
									else:
												A_	:						str	=     0
						cur_score += diff
						if cur_score > best_score:
									A_	:						Optional[int]	=     cur_score
									A_	:						Union[str, Any]	=     na_probs[qid]
			return 100.0 * best_score / len(lowerCamelCase__    ), best_thresh



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_,    A_	:						Optional[Any]	=     find_best_thresh(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
			A_,    A_	:						List[str]	=     find_best_thresh(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
			A_	:						Dict	=     best_exact
			A_	:						Union[str, Any]	=     exact_thresh
			A_	:						Union[str, Any]	=     best_fa
			A_	:						Optional[int]	=     fa_thresh



def 	a			( ):
			'''simple docstring'''





			with open(OPTS.data_file    ) as f:
						A_	:						Dict	=     json.load(lowerCamelCase__    )
						A_	:						Dict	=     dataset_json["""data"""]
			with open(OPTS.pred_file    ) as f:
						A_	:						str	=     json.load(lowerCamelCase__    )
			if OPTS.na_prob_file:
						with open(OPTS.na_prob_file    ) as f:
									A_	:						int	=     json.load(lowerCamelCase__    )
			else:
						A_	:						str	=     {k: 0.0 for k in preds}
			A_	:						int	=     make_qid_to_has_ans(lowerCamelCase__    )  # maps qid to True/False
			A_	:						Tuple	=     [k for k, v in qid_to_has_ans.items() if v]
			A_	:						Tuple	=     [k for k, v in qid_to_has_ans.items() if not v]
			A_,    A_	:						Optional[int]	=     get_raw_scores(lowerCamelCase__						,      lowerCamelCase__    )
			A_	:						List[str]	=     apply_no_ans_threshold(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      OPTS.na_prob_thresh    )
			A_	:						Union[str, Any]	=     apply_no_ans_threshold(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      OPTS.na_prob_thresh    )
			A_	:						Optional[Any]	=     make_eval_dict(lowerCamelCase__						,      lowerCamelCase__    )
			if has_ans_qids:
						A_	:						Optional[int]	=     make_eval_dict(lowerCamelCase__						,      lowerCamelCase__						,      qid_list=lowerCamelCase__    )
						merge_eval(lowerCamelCase__						,      lowerCamelCase__						,      """HasAns"""    )
			if no_ans_qids:
						A_	:						Any	=     make_eval_dict(lowerCamelCase__						,      lowerCamelCase__						,      qid_list=lowerCamelCase__    )
						merge_eval(lowerCamelCase__						,      lowerCamelCase__						,      """NoAns"""    )
			if OPTS.na_prob_file:
						find_all_best_thresh(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
			if OPTS.na_prob_file and OPTS.out_image_dir:
						run_precision_recall_analysis(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      OPTS.out_image_dir    )
						histogram_na_prob(lowerCamelCase__						,      lowerCamelCase__						,      OPTS.out_image_dir						,      """hasAns"""    )
						histogram_na_prob(lowerCamelCase__						,      lowerCamelCase__						,      OPTS.out_image_dir						,      """noAns"""    )
			if OPTS.out_file:
						with open(OPTS.out_file						,      """w"""    ) as f:
									json.dump(lowerCamelCase__						,      lowerCamelCase__    )
			else:
						print(json.dumps(lowerCamelCase__						,      indent=2    )    )


if __name__ == "__main__":
						lowerCamelCase							:int				    =					parse_args()
						if OPTS.out_image_dir:
												import matplotlib

												matplotlib.use('''Agg''')
												import matplotlib.pyplot as plt
						main()




'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowerCamelCase							:Any				    =					logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				super().__init__()
				self.register_modules(
				    vae=lowercase       ,      text_encoder=lowercase       ,      tokenizer=lowercase       ,      unet=lowercase       ,      scheduler=lowercase       ,      safety_checker=lowercase       ,      feature_extractor=lowercase       ,      )


	def     _a	(self       ,      lowercase = "auto"							):
				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							A_	:						int	=     self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowercase							)


	def     _a	(self							):
				self.enable_attention_slicing(lowercase							)


	@torch.no_grad()
	def __call__(self       ,      lowercase       ,      lowercase = 512       ,      lowercase = 512       ,      lowercase = 50       ,      lowercase = 7.5       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = 0.0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = "pil"       ,      lowercase = True       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = None       ,      **lowercase       ,      ):

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Union[str, Any]	=     1
				elif isinstance(lowercase       ,      lowercase							):
							A_	:						Any	=     len(lowercase							)
				else:
							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase							)}'							)

				if height % 8 != 0 or width % 8 != 0:
							raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(lowercase       ,      lowercase							) or callback_steps <= 0)
				):
							raise ValueError(
							    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    F' {type(lowercase							)}.'							)

				# get prompt text embeddings
				A_	:						Optional[Any]	=     self.tokenizer(
				    lowercase       ,      padding="""max_length"""       ,      max_length=self.tokenizer.model_max_length       ,      return_tensors="""pt"""       ,      )
				A_	:						Dict	=     text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A_	:						Union[str, Any]	=     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
							A_	:						Dict	=     text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
							A_	:						Any	=     self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				A_,    A_,    A_	:						Tuple	=     text_embeddings.shape
				A_	:						Optional[Any]	=     text_embeddings.repeat(1       ,      lowercase       ,      1							)
				A_	:						Any	=     text_embeddings.view(bs_embed * num_images_per_prompt       ,      lowercase       ,      -1							)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				A_	:						List[str]	=     guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							A_	:						List[str]
							if negative_prompt is None:
										A_	:						Optional[int]	=     [""""""]
							elif type(lowercase							) is not type(lowercase							):
										raise TypeError(
										    F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase							)} !='
										    F' {type(lowercase							)}.'							)
							elif isinstance(lowercase       ,      lowercase							):
										A_	:						Dict	=     [negative_prompt]
							elif batch_size != len(lowercase							):
										raise ValueError(
										    F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase							)}, but `prompt`:'
										    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
										    """ the batch size of `prompt`."""							)
							else:
										A_	:						Dict	=     negative_prompt

							A_	:						int	=     text_input_ids.shape[-1]
							A_	:						List[Any]	=     self.tokenizer(
							    lowercase       ,      padding="""max_length"""       ,      max_length=lowercase       ,      truncation=lowercase       ,      return_tensors="""pt"""       ,      )
							A_	:						str	=     self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A_	:						Optional[Any]	=     uncond_embeddings.shape[1]
							A_	:						str	=     uncond_embeddings.repeat(lowercase       ,      lowercase       ,      1							)
							A_	:						List[str]	=     uncond_embeddings.view(batch_size * num_images_per_prompt       ,      lowercase       ,      -1							)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							A_	:						Dict	=     torch.cat([uncond_embeddings, text_embeddings]							)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				A_	:						int	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				A_	:						Dict	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				A_	:						Dict	=     text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not exist on mps
										A_	:						Tuple	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(self.device							)
										A_	:						int	=     torch.randn(lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(
										    self.device							)
							else:
										A_	:						int	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
										A_	:						str	=     torch.randn(lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
				else:
							if latents_reference.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
							A_	:						str	=     latents_reference.to(self.device							)
							A_	:						Tuple	=     latents.to(self.device							)

				# This is the key part of the pipeline where we
				# try to ensure that the generated images w/ the same seed
				# but different sizes actually result in similar images
				A_	:						Optional[int]	=     (latents_shape[3] - latents_shape_reference[3]) // 2
				A_	:						Optional[int]	=     (latents_shape[2] - latents_shape_reference[2]) // 2
				A_	:						Optional[int]	=     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				A_	:						int	=     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				A_	:						Optional[Any]	=     0 if dx < 0 else dx
				A_	:						Optional[Any]	=     0 if dy < 0 else dy
				A_	:						Optional[int]	=     max(-dx       ,      0							)
				A_	:						List[str]	=     max(-dy       ,      0							)
				# import pdb
				# pdb.set_trace()
				A_	:						str	=     latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(lowercase							)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				A_	:						Any	=     self.scheduler.timesteps.to(self.device							)

				# scale the initial noise by the standard deviation required by the scheduler
				A_	:						Tuple	=     latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				A_	:						Dict	=     """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
				A_	:						Any	=     {}
				if accepts_eta:
							A_	:						Optional[int]	=     eta

				for i, t in enumerate(self.progress_bar(lowercase							)							):
							# expand the latents if we are doing classifier free guidance
							A_	:						Tuple	=     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
							A_	:						Tuple	=     self.scheduler.scale_model_input(lowercase       ,      lowercase							)

							# predict the noise residual
							A_	:						List[str]	=     self.unet(lowercase       ,      lowercase       ,      encoder_hidden_states=lowercase							).sample

							# perform guidance
							if do_classifier_free_guidance:
										A_,    A_	:						str	=     noise_pred.chunk(2							)
										A_	:						Optional[Any]	=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							A_	:						List[str]	=     self.scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
										callback(lowercase       ,      lowercase       ,      lowercase							)

				A_	:						List[str]	=     1 / 0.1_82_15 * latents
				A_	:						List[str]	=     self.vae.decode(lowercase							).sample

				A_	:						Optional[int]	=     (image / 2 + 0.5).clamp(0       ,      1							)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				A_	:						Optional[int]	=     image.cpu().permute(0       ,      2       ,      3       ,      1							).float().numpy()

				if self.safety_checker is not None:
							A_	:						Union[str, Any]	=     self.feature_extractor(self.numpy_to_pil(lowercase							)       ,      return_tensors="""pt"""							).to(
							    self.device							)
							A_,    A_	:						Optional[int]	=     self.safety_checker(
							    images=lowercase       ,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
				else:
							A_	:						Tuple	=     None

				if output_type == "pil":
							A_	:						Tuple	=     self.numpy_to_pil(lowercase							)

				if not return_dict:
							return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=lowercase       ,      nsfw_content_detected=lowercase							)