import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						List[Any]    	=       {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

_SCREAMING_SNAKE_CASE:						List[str]    	=       {
    '''openai-gpt''': 512,
}






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      text.strip()
			if not text:
						return []
			_snake_case              =      text.split()
			return tokens

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True  ,					):






					"""simple docstring"""
					if never_split is None:
								_snake_case              =      []
					_snake_case              =      do_lower_case
					_snake_case              =      set(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      tokenize_chinese_chars
					_snake_case              =      strip_accents
					_snake_case              =      do_split_on_punc



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None   ):






					"""simple docstring"""
					_snake_case              =      self.never_split.union(set(__SCREAMING_SNAKE_CASE   )   ) if never_split else self.never_split
					_snake_case              =      self._clean_text(__SCREAMING_SNAKE_CASE   )

					# This was added on November 1st, 2018 for the multilingual and Chinese
					# models. This is also applied to the English models now, but it doesn't
					# matter since the English models were not trained on any Chinese data
					# and generally don't have any Chinese data in them (there are Chinese
					# characters in the vocabulary because Wikipedia does have some Chinese
					# words in the English Wikipedia.).
					if self.tokenize_chinese_chars:
								_snake_case              =      self._tokenize_chinese_chars(__SCREAMING_SNAKE_CASE   )
					# prevents treating the same character with different unicode codepoints as different characters
					_snake_case              =      unicodedata.normalize("NFC"  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      whitespace_tokenize(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					for token in orig_tokens:
								if token not in never_split:
											if self.do_lower_case:
														_snake_case              =      token.lower()
														if self.strip_accents is not False:
																	_snake_case              =      self._run_strip_accents(__SCREAMING_SNAKE_CASE   )
											elif self.strip_accents:
														_snake_case              =      self._run_strip_accents(__SCREAMING_SNAKE_CASE   )
								split_tokens.extend(self._run_split_on_punc(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )   )

					_snake_case              =      whitespace_tokenize(" ".join(__SCREAMING_SNAKE_CASE   )   )
					return output_tokens



		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      unicodedata.normalize("NFD"  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					for char in text:
								_snake_case              =      unicodedata.category(__SCREAMING_SNAKE_CASE   )
								if cat == "Mn":
											continue
								output.append(__SCREAMING_SNAKE_CASE   )
					return "".join(__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				int=None   ):






					"""simple docstring"""
					if not self.do_split_on_punc or (never_split is not None and text in never_split):
								return [text]
					_snake_case              =      list(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      0
					_snake_case              =      True
					_snake_case              =      []
					while i < len(__SCREAMING_SNAKE_CASE   ):
								_snake_case              =      chars[i]
								if _is_punctuation(__SCREAMING_SNAKE_CASE   ):
											output.append([char]   )
											_snake_case              =      True
								else:
											if start_new_word:
														output.append([]   )
											_snake_case              =      False
											output[-1].append(__SCREAMING_SNAKE_CASE   )
								i += 1

					return ["".join(__SCREAMING_SNAKE_CASE   ) for x in output]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      []
					for char in text:
								_snake_case              =      ord(__SCREAMING_SNAKE_CASE   )
								if self._is_chinese_char(__SCREAMING_SNAKE_CASE   ):
											output.append(" "   )
											output.append(__SCREAMING_SNAKE_CASE   )
											output.append(" "   )
								else:
											output.append(__SCREAMING_SNAKE_CASE   )
					return "".join(__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					# This defines a "chinese character" as anything in the CJK Unicode block:
					#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
					#
					# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
					# despite its name. The modern Korean Hangul alphabet is a different block,
					# as is Japanese Hiragana and Katakana. Those alphabets are used to write
					# space-separated words, so they are not treated specially and handled
					# like the all of the other languages.
					if (
					    (cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
					    or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
					    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
					    or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
					    or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
					    or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
					    or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
					    or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
					):  #
								return True

					return False







		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      []
					for char in text:
								_snake_case              =      ord(__SCREAMING_SNAKE_CASE   )
								if cp == 0 or cp == 0Xf_f_f_d or _is_control(__SCREAMING_SNAKE_CASE   ):
											continue
								if _is_whitespace(__SCREAMING_SNAKE_CASE   ):
											output.append(" "   )
								else:
											output.append(__SCREAMING_SNAKE_CASE   )
					return "".join(__SCREAMING_SNAKE_CASE   )






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      set()
			_snake_case              =      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						_snake_case              =      char
			return pairs






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      text.replace("—"							,	"-"  )
			_snake_case              =      text.replace("–"							,	"-"  )
			_snake_case              =      text.replace("―"							,	"-"  )
			_snake_case              =      text.replace("…"							,	"..."  )
			_snake_case              =      text.replace("´"							,	"'"  )
			_snake_case              =      re.sub(R"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"							,	R" \1 "							,	lowerCamelCase_  )
			_snake_case              =      re.sub(R"\s*\n\s*"							,	" \n "							,	lowerCamelCase_  )
			_snake_case              =      re.sub(R"[^\S\n]+"							,	" "							,	lowerCamelCase_  )
			return text.strip()







class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      VOCAB_FILES_NAMES
		A_						 =      PRETRAINED_VOCAB_FILES_MAP
		A_						 =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						 =      ['''input_ids''', '''attention_mask''']



		def __init__(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int="<unk>"  ,					**__SCREAMING_SNAKE_CASE    :				Union[str, Any]   ):






					"""simple docstring"""
					super().__init__(unk_token=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )

					try:
								import ftfy
								from spacy.lang.en import English

								_snake_case              =      English()
								_snake_case              =      _nlp.tokenizer
								_snake_case              =      ftfy.fix_text
					except ImportError:
								logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."   )
								_snake_case              =      BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE   )
								_snake_case              =      None

					with open(__SCREAMING_SNAKE_CASE  ,					encoding="utf-8"   ) as vocab_handle:
								_snake_case              =      json.load(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      {v: k for k, v in self.encoder.items()}
					with open(__SCREAMING_SNAKE_CASE  ,					encoding="utf-8"   ) as merges_handle:
								_snake_case              =      merges_handle.read().split("\n"   )[1:-1]
					_snake_case              =      [tuple(merge.split()   ) for merge in merges]
					_snake_case              =      dict(zip(__SCREAMING_SNAKE_CASE  ,					range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
					_snake_case              =      {}



		@property
		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return True



		@property
		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return len(self.encoder   )



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					return dict(self.encoder  ,					**self.added_tokens_encoder   )



		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      tuple(token[:-1]   ) + (token[-1] + "</w>",)
					if token in self.cache:
								return self.cache[token]
					_snake_case              =      get_pairs(__SCREAMING_SNAKE_CASE   )

					if not pairs:
								return token + "</w>"

					while True:
								_snake_case              =      min(__SCREAMING_SNAKE_CASE  ,					key=lambda __SCREAMING_SNAKE_CASE   : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE  ,					float("inf"   )   )   )
								if bigram not in self.bpe_ranks:
											break
								_snake_case       , _snake_case              =      bigram
								_snake_case              =      []
								_snake_case              =      0
								while i < len(__SCREAMING_SNAKE_CASE   ):
											try:
														_snake_case              =      word.index(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
											except ValueError:
														new_word.extend(word[i:]   )
														break
											else:
														new_word.extend(word[i:j]   )
														_snake_case              =      j

											if word[i] == first and i < len(__SCREAMING_SNAKE_CASE   ) - 1 and word[i + 1] == second:
														new_word.append(first + second   )
														i += 2
											else:
														new_word.append(word[i]   )
														i += 1
								_snake_case              =      tuple(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      new_word
								if len(__SCREAMING_SNAKE_CASE   ) == 1:
											break
								else:
											_snake_case              =      get_pairs(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      " ".join(__SCREAMING_SNAKE_CASE   )
					if word == "\n  </w>":
								_snake_case              =      "\n</w>"
					_snake_case              =      word
					return word



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      []
					if self.fix_text is None:
								# Using BERT's BasicTokenizer
								_snake_case              =      self.nlp.tokenize(__SCREAMING_SNAKE_CASE   )
								for token in text:
											split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE   ).split(" "   )   )   )
					else:
								# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
								_snake_case              =      self.nlp(text_standardize(self.fix_text(__SCREAMING_SNAKE_CASE   )   )   )
								for token in text:
											split_tokens.extend(list(self.bpe(token.text.lower()   ).split(" "   )   )   )
					return split_tokens



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]   ):






					"""simple docstring"""
					return self.encoder.get(__SCREAMING_SNAKE_CASE  ,					self.encoder.get(self.unk_token   )   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					return self.decoder.get(__SCREAMING_SNAKE_CASE  ,					self.unk_token   )



		def 				_a				(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      "".join(__SCREAMING_SNAKE_CASE   ).replace("</w>"  ,					" "   ).strip()
					return out_string







		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None   ):






					"""simple docstring"""
					if not os.path.isdir(__SCREAMING_SNAKE_CASE   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
					_snake_case              =      os.path.join(
					    __SCREAMING_SNAKE_CASE  ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
					_snake_case              =      os.path.join(
					    __SCREAMING_SNAKE_CASE  ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

					with open(__SCREAMING_SNAKE_CASE  ,					"w"  ,					encoding="utf-8"   ) as f:
								f.write(json.dumps(self.encoder  ,					indent=2  ,					sort_keys=__SCREAMING_SNAKE_CASE  ,					ensure_ascii=__SCREAMING_SNAKE_CASE   ) + "\n"   )

					_snake_case              =      0
					with open(__SCREAMING_SNAKE_CASE  ,					"w"  ,					encoding="utf-8"   ) as writer:
								writer.write("#version: 0.2\n"   )
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,					key=lambda __SCREAMING_SNAKE_CASE   : kv[1]   ):
											if index != token_index:
														logger.warning(
														    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
														    " Please check that the tokenizer is not corrupted!"   )
														_snake_case              =      token_index
											writer.write(" ".join(__SCREAMING_SNAKE_CASE   ) + "\n"   )
											index += 1

					return vocab_file, merge_file


from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				AutoencoderKL  ,					__SCREAMING_SNAKE_CASE    :				CLIPTextModel  ,					__SCREAMING_SNAKE_CASE    :				CLIPTokenizer  ,					__SCREAMING_SNAKE_CASE    :				UNetaDConditionModel  ,					__SCREAMING_SNAKE_CASE    :				DDIMScheduler  ,					__SCREAMING_SNAKE_CASE    :				float = 0.9_3  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					):






					"""simple docstring"""
					_snake_case              =      mixing_coeff
					_snake_case              =      leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=__SCREAMING_SNAKE_CASE  ,					text_encoder=__SCREAMING_SNAKE_CASE  ,					tokenizer=__SCREAMING_SNAKE_CASE  ,					unet=__SCREAMING_SNAKE_CASE  ,					scheduler=__SCREAMING_SNAKE_CASE  ,					)

					_snake_case              =      2 ** (len(self.vae.config.block_out_channels   ) - 1)
					_snake_case              =      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = False   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer(
					    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

					_snake_case              =      self.text_encoder(text_inputs.input_ids.to(self.device   )   ).last_hidden_state

					_snake_case              =      prompt_embeds.to(dtype=self.text_encoder.dtype  ,					device=self.device   )

					if do_classifier_free_guidance:
								_snake_case              =      "" if negative_prompt is None else negative_prompt

								_snake_case              =      self.tokenizer(
								    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

								_snake_case              =      self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

								_snake_case              =      torch.cat([negative_prompt_embeds, prompt_embeds]   )

					return prompt_embeds



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					_snake_case              =      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					_snake_case              =      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					# as self.alphas_cumprod is always in cpu
					_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					_snake_case              =      (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype   )



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					_snake_case              =      a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype   )



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      1 / self.vae.config.scaling_factor * latents
					_snake_case              =      self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample
					_snake_case              =      (image / 2 + 0.5).clamp(0  ,					1   )
					return image



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				float  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      image.to(device=self.device  ,					dtype=text_embeds.dtype   )
					_snake_case              =      self.vae.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.vae.config.scaling_factor * latent

					_snake_case              =      [latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								_snake_case              =      self.noise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

								# j - model_input index, k - base index
								for j in range(2   ):
											_snake_case              =      j ^ 1

											if self.leapfrog_steps:
														if i % 2 == 0:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.noise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

					return coupled_latents







		@torch.no_grad()
		def __call__(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				float = 3.0  ,					__SCREAMING_SNAKE_CASE    :				int = 50  ,					__SCREAMING_SNAKE_CASE    :				float = 0.8  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = "pil"  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      self.image_processor.preprocess(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,					self.device   )

					_snake_case              =      num_inference_steps - int(num_inference_steps * strength   )
					_snake_case              =      self.scheduler.timesteps[t_limit:]
					_snake_case              =      fwd_timesteps.flip(0   )

					_snake_case              =      self.prepare_latents(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								# j - model_input index, k - base index
								for k in range(2   ):
											_snake_case              =      k ^ 1

											if self.leapfrog_steps:
														if i % 2 == 1:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

								_snake_case              =      self.denoise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

					# either one is fine
					_snake_case              =      coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								_snake_case              =      (
								    F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
								    "`pil`, `np`, `pt`, `latent`"
								)
								deprecate("Unsupported output_type"  ,					"1.0.0"  ,					__SCREAMING_SNAKE_CASE  ,					standard_warn=__SCREAMING_SNAKE_CASE   )
								_snake_case              =      "np"

					if output_type == "latent":
								_snake_case              =      final_latent
					else:
								_snake_case              =      self.decode_latents(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.image_processor.postprocess(__SCREAMING_SNAKE_CASE  ,					output_type=__SCREAMING_SNAKE_CASE   )

					return image

