



'''simple docstring'''







import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	RealmTokenizer
	A__		=	RealmTokenizerFast
	A__		=	True
	A__		=	True
	A__		=	filter_non_english




	def      lowercase__							(  self		:  Optional[Any]					)     ->			Tuple:




								'''simple docstring'''





								super().setUp()

								lowerCAmelCase__  =   [
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """[PAD]""",
								    """[MASK]""",
								    """want""",
								    """##want""",
								    """##ed""",
								    """wa""",
								    """un""",
								    """runn""",
								    """##ing""",
								    """,""",
								    """low""",
								    """lowest""",
								]
								lowerCAmelCase__  =   os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								with open(self.vocab_file   ,		"""w"""   ,		encoding="""utf-8"""					) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)




	def      lowercase__							(  self		:  Dict   ,		__A		:  Tuple					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   """UNwant\u00E9d,running"""
								lowerCAmelCase__  =   """unwanted, running"""
								return input_text, output_text




	def      lowercase__							(  self		:  List[Any]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   self.tokenizer_class(self.vocab_file					)

								lowerCAmelCase__  =   tokenizer.tokenize("""UNwant\u00E9d,running"""					)
								self.assertListEqual(__A   ,		["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]					)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__A					)   ,		[9, 6, 7, 12, 10, 11]					)




	def      lowercase__							(  self		:  str					)     ->			str:




								'''simple docstring'''





								if not self.test_rust_tokenizer:
															return

								lowerCAmelCase__  =   self.get_tokenizer()
								lowerCAmelCase__  =   self.get_rust_tokenizer()

								lowerCAmelCase__  =   """UNwant\u00E9d,running"""

								lowerCAmelCase__  =   tokenizer.tokenize(__A					)
								lowerCAmelCase__  =   rust_tokenizer.tokenize(__A					)
								self.assertListEqual(__A   ,		__A					)

								lowerCAmelCase__  =   tokenizer.encode(__A   ,		add_special_tokens=__A					)
								lowerCAmelCase__  =   rust_tokenizer.encode(__A   ,		add_special_tokens=__A					)
								self.assertListEqual(__A   ,		__A					)

								lowerCAmelCase__  =   self.get_rust_tokenizer()
								lowerCAmelCase__  =   tokenizer.encode(__A					)
								lowerCAmelCase__  =   rust_tokenizer.encode(__A					)
								self.assertListEqual(__A   ,		__A					)

								# With lower casing
								lowerCAmelCase__  =   self.get_tokenizer(do_lower_case=__A					)
								lowerCAmelCase__  =   self.get_rust_tokenizer(do_lower_case=__A					)

								lowerCAmelCase__  =   """UNwant\u00E9d,running"""

								lowerCAmelCase__  =   tokenizer.tokenize(__A					)
								lowerCAmelCase__  =   rust_tokenizer.tokenize(__A					)
								self.assertListEqual(__A   ,		__A					)

								lowerCAmelCase__  =   tokenizer.encode(__A   ,		add_special_tokens=__A					)
								lowerCAmelCase__  =   rust_tokenizer.encode(__A   ,		add_special_tokens=__A					)
								self.assertListEqual(__A   ,		__A					)

								lowerCAmelCase__  =   self.get_rust_tokenizer()
								lowerCAmelCase__  =   tokenizer.encode(__A					)
								lowerCAmelCase__  =   rust_tokenizer.encode(__A					)
								self.assertListEqual(__A   ,		__A					)




	def      lowercase__							(  self		:  Optional[Any]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer()

								self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""					)   ,		["""ah""", """\u535A""", """\u63A8""", """zz"""]					)




	def      lowercase__							(  self		:  Union[str, Any]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """					)   ,		["""hello""", """!""", """how""", """are""", """you""", """?"""]					)
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					)   ,		["""hello"""]					)




	def      lowercase__							(  self		:  str					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A   ,		strip_accents=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					)   ,		["""hällo""", """!""", """how""", """are""", """you""", """?"""]					)
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					)   ,		["""h\u00E9llo"""]					)




	def      lowercase__							(  self		:  int					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A   ,		strip_accents=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					)   ,		["""hallo""", """!""", """how""", """are""", """you""", """?"""]					)
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					)   ,		["""hello"""]					)




	def      lowercase__							(  self		:  List[str]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					)   ,		["""hallo""", """!""", """how""", """are""", """you""", """?"""]					)
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""					)   ,		["""hello"""]					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """					)   ,		["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)




	def      lowercase__							(  self		:  Optional[Any]					)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A   ,		strip_accents=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					)   ,		["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)




	def      lowercase__							(  self		:  str					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A   ,		strip_accents=__A					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """					)   ,		["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   BasicTokenizer(do_lower_case=__A   ,		never_split=["""[UNK]"""]					)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""					)   ,		["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

								lowerCAmelCase__  =   {}
								for i, token in enumerate(__A					):
															lowerCAmelCase__  =   i
								lowerCAmelCase__  =   WordpieceTokenizer(vocab=__A   ,		unk_token="""[UNK]"""					)

								self.assertListEqual(tokenizer.tokenize(""""""					)   ,		[]					)

								self.assertListEqual(tokenizer.tokenize("""unwanted running"""					)   ,		["""un""", """##want""", """##ed""", """runn""", """##ing"""]					)

								self.assertListEqual(tokenizer.tokenize("""unwantedX running"""					)   ,		["""[UNK]""", """runn""", """##ing"""]					)




	def      lowercase__							(  self		:  List[str]					)     ->			Dict:




								'''simple docstring'''





								self.assertTrue(_is_whitespace(""" """					)					)
								self.assertTrue(_is_whitespace("""\t"""					)					)
								self.assertTrue(_is_whitespace("""\r"""					)					)
								self.assertTrue(_is_whitespace("""\n"""					)					)
								self.assertTrue(_is_whitespace("""\u00A0"""					)					)

								self.assertFalse(_is_whitespace("""A"""					)					)
								self.assertFalse(_is_whitespace("""-"""					)					)




	def      lowercase__							(  self		:  Optional[Any]					)     ->			Union[str, Any]:




								'''simple docstring'''





								self.assertTrue(_is_control("""\u0005"""					)					)

								self.assertFalse(_is_control("""A"""					)					)
								self.assertFalse(_is_control(""" """					)					)
								self.assertFalse(_is_control("""\t"""					)					)
								self.assertFalse(_is_control("""\r"""					)					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Union[str, Any]:




								'''simple docstring'''





								self.assertTrue(_is_punctuation("""-"""					)					)
								self.assertTrue(_is_punctuation("""$"""					)					)
								self.assertTrue(_is_punctuation("""`"""					)					)
								self.assertTrue(_is_punctuation("""."""					)					)

								self.assertFalse(_is_punctuation("""A"""					)					)
								self.assertFalse(_is_punctuation(""" """					)					)




	def      lowercase__							(  self		:  Optional[Any]					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(__A					) for t in ["""Test""", """\xad""", """test"""]]   ,		[["""[UNK]"""], [], ["""[UNK]"""]]					)

								if self.test_rust_tokenizer:
															lowerCAmelCase__  =   self.get_rust_tokenizer()
															self.assertListEqual(
															    [rust_tokenizer.tokenize(__A					) for t in ["""Test""", """\xad""", """test"""]]   ,		[["""[UNK]"""], [], ["""[UNK]"""]]					)




	@slow
	def      lowercase__							(  self		:  List[Any]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.tokenizer_class.from_pretrained("""bert-base-uncased"""					)

								lowerCAmelCase__  =   tokenizer.encode("""sequence builders"""   ,		add_special_tokens=__A					)
								lowerCAmelCase__  =   tokenizer.encode("""multi-sequence build"""   ,		add_special_tokens=__A					)

								lowerCAmelCase__  =   tokenizer.build_inputs_with_special_tokens(__A					)
								lowerCAmelCase__  =   tokenizer.build_inputs_with_special_tokens(__A   ,		__A					)

								assert encoded_sentence == [101] + text + [102]
								assert encoded_pair == [101] + text + [102] + text_a + [102]




	def      lowercase__							(  self		:  Any					)     ->			Union[str, Any]:




								'''simple docstring'''





								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																						lowerCAmelCase__  =   self.rust_tokenizer_class.from_pretrained(__A   ,		**__A					)

																						lowerCAmelCase__  =   f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
																						lowerCAmelCase__  =   tokenizer_r.encode_plus(
																						    __A   ,		return_attention_mask=__A   ,		return_token_type_ids=__A   ,		return_offsets_mapping=__A   ,		add_special_tokens=__A   ,		)

																						lowerCAmelCase__  =   tokenizer_r.do_lower_case if hasattr(__A   ,		"""do_lower_case"""					) else False
																						lowerCAmelCase__  =   (
																						    [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), """A"""),
																						        ((1, 2), ""","""),
																						        ((3, 5), """na"""),
																						        ((5, 6), """##ï"""),
																						        ((6, 8), """##ve"""),
																						        ((9, 15), tokenizer_r.mask_token),
																						        ((16, 21), """Allen"""),
																						        ((21, 23), """##NL"""),
																						        ((23, 24), """##P"""),
																						        ((25, 33), """sentence"""),
																						        ((33, 34), """."""),
																						        ((0, 0), tokenizer_r.sep_token),
																						    ]
																						    if not do_lower_case
																						    else [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), """a"""),
																						        ((1, 2), ""","""),
																						        ((3, 8), """naive"""),
																						        ((9, 15), tokenizer_r.mask_token),
																						        ((16, 21), """allen"""),
																						        ((21, 23), """##nl"""),
																						        ((23, 24), """##p"""),
																						        ((25, 33), """sentence"""),
																						        ((33, 34), """."""),
																						        ((0, 0), tokenizer_r.sep_token),
																						    ]
																						)

																						self.assertEqual(
																						    [e[1] for e in expected_results]   ,		tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]					)					)
																						self.assertEqual([e[0] for e in expected_results]   ,		tokens["""offset_mapping"""]					)




	@slow
	def      lowercase__							(  self		:  Dict					)     ->			str:




								'''simple docstring'''





								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																						lowerCAmelCase__  =   self.rust_tokenizer_class.from_pretrained(__A   ,		**__A					)
																						lowerCAmelCase__  =   self.tokenizer_class.from_pretrained(__A   ,		**__A					)
																						lowerCAmelCase__  =   [["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]

																						lowerCAmelCase__  =   tokenizer_r.batch_encode_candidates(__A   ,		max_length=10   ,		return_tensors="""np"""					)
																						lowerCAmelCase__  =   tokenizer_p.batch_encode_candidates(__A   ,		max_length=10   ,		return_tensors="""np"""					)

																						lowerCAmelCase__  =   (2, 2, 10)

																						self.assertEqual(encoded_sentence_r["""input_ids"""].shape   ,		__A					)
																						self.assertEqual(encoded_sentence_r["""attention_mask"""].shape   ,		__A					)
																						self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape   ,		__A					)

																						self.assertEqual(encoded_sentence_p["""input_ids"""].shape   ,		__A					)
																						self.assertEqual(encoded_sentence_p["""attention_mask"""].shape   ,		__A					)
																						self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape   ,		__A					)










'''simple docstring'''







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      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	def __init__(  self		:  Optional[Any]   ,		__A		:  AutoencoderKL   ,		__A		:  CLIPTextModel   ,		__A		:  CLIPTokenizer   ,		__A		:  UNetaDConditionModel   ,		__A		:  DDIMScheduler   ,		__A		:  float = 0.9_3   ,		__A		:  bool = True   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   mixing_coeff
								lowerCAmelCase__  =   leapfrog_steps

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

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




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  str   ,		__A		:  Optional[str] = None   ,		__A		:  bool = False					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.tokenizer(
								    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

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

															lowerCAmelCase__  =   self.tokenizer(
															    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

								return prompt_embeds




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								lowerCAmelCase__  =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




	def      lowercase__							(  self		:  Dict   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								lowerCAmelCase__  =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




	def      lowercase__							(  self		:  List[str]   ,		__A		:  torch.Tensor					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   int(__A					)

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

								return alpha_prod, 1 - alpha_prod




	def      lowercase__							(  self		:  List[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								lowerCAmelCase__  =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype					)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								return model_input, next_model_input.to(base.dtype					)




	@torch.no_grad()
	def      lowercase__							(  self		:  int   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   1 / self.vae.config.scaling_factor * latents
								lowerCAmelCase__  =   self.vae.decode(__A					).sample
								lowerCAmelCase__  =   (image / 2 + 0.5).clamp(0   ,		1					)
								return image




	@torch.no_grad()
	def      lowercase__							(  self		:  List[str]   ,		__A		:  Image.Image   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  float   ,		__A		:  Optional[torch.Generator] = None   ,		)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   image.to(device=self.device   ,		dtype=text_embeds.dtype					)
								lowerCAmelCase__  =   self.vae.encode(__A					).latent_dist.sample(__A					)

								lowerCAmelCase__  =   self.vae.config.scaling_factor * latent

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

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

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

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.noise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

								return coupled_latents




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




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   self.image_processor.preprocess(__A					)

								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)
								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)

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

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

								lowerCAmelCase__  =   self.prepare_latents(__A   ,		__A   ,		__A   ,		__A   ,		__A					)

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

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.denoise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

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

								# either one is fine
								lowerCAmelCase__  =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
															lowerCAmelCase__  =   (
															    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"""   ,		__A   ,		standard_warn=__A					)
															lowerCAmelCase__  =   """np"""

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

								return image





