
"""simple docstring"""


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


__A       						=       logging.get_logger(__name__)

__A       						=       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A       						=       {
    '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'},
}

__A       						=       {
    'openai-gpt': 512,
}



def     _lowerCamelCase(__UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=text.strip()
	if not text:
		return []
	_lowerCAmelCase								=text.split()
	return tokens







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=True     ,     ) ->     List[str]:
				if never_split is None:
					_lowerCAmelCase								=[]
				_lowerCAmelCase								=do_lower_case
				_lowerCAmelCase								=set(__UpperCAmelCase  )
				_lowerCAmelCase								=tokenize_chinese_chars
				_lowerCAmelCase								=strip_accents
				_lowerCAmelCase								=do_split_on_punc



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     Any:
				_lowerCAmelCase								=self.never_split.union(set(__UpperCAmelCase  )  ) if never_split else self.never_split
				_lowerCAmelCase								=self._clean_text(__UpperCAmelCase  )

				# 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:
					_lowerCAmelCase								=self._tokenize_chinese_chars(__UpperCAmelCase  )
				# prevents treating the same character with different unicode codepoints as different characters
				_lowerCAmelCase								=unicodedata.normalize("""NFC"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=whitespace_tokenize(__UpperCAmelCase  )
				_lowerCAmelCase								=[]
				for token in orig_tokens:
					if token not in never_split:
						if self.do_lower_case:
							_lowerCAmelCase								=token.lower()
							if self.strip_accents is not False:
								_lowerCAmelCase								=self._run_strip_accents(__UpperCAmelCase  )
						elif self.strip_accents:
							_lowerCAmelCase								=self._run_strip_accents(__UpperCAmelCase  )
					split_tokens.extend(self._run_split_on_punc(__UpperCAmelCase     ,     __UpperCAmelCase  )  )

				_lowerCAmelCase								=whitespace_tokenize(""" """.join(__UpperCAmelCase  )  )
				return output_tokens



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Tuple:
				_lowerCAmelCase								=unicodedata.normalize("""NFD"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=[]
				for char in text:
					_lowerCAmelCase								=unicodedata.category(__UpperCAmelCase  )
					if cat == "Mn":
						continue
					output.append(__UpperCAmelCase  )
				return "".join(__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     Any:
				if not self.do_split_on_punc or (never_split is not None and text in never_split):
					return [text]
				_lowerCAmelCase								=list(__UpperCAmelCase  )
				_lowerCAmelCase								=0
				_lowerCAmelCase								=True
				_lowerCAmelCase								=[]
				while i < len(__UpperCAmelCase  ):
					_lowerCAmelCase								=chars[i]
					if _is_punctuation(__UpperCAmelCase  ):
						output.append([char]  )
						_lowerCAmelCase								=True
					else:
						if start_new_word:
							output.append([]  )
						_lowerCAmelCase								=False
						output[-1].append(__UpperCAmelCase  )
					i += 1

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     str:
				_lowerCAmelCase								=[]
				for char in text:
					_lowerCAmelCase								=ord(__UpperCAmelCase  )
					if self._is_chinese_char(__UpperCAmelCase  ):
						output.append(""" """  )
						output.append(__UpperCAmelCase  )
						output.append(""" """  )
					else:
						output.append(__UpperCAmelCase  )
				return "".join(__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Tuple:
				# 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 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Optional[Any]:
				_lowerCAmelCase								=[]
				for char in text:
					_lowerCAmelCase								=ord(__UpperCAmelCase  )
					if cp == 0 or cp == 0xf_f_f_d or _is_control(__UpperCAmelCase  ):
						continue
					if _is_whitespace(__UpperCAmelCase  ):
						output.append(""" """  )
					else:
						output.append(__UpperCAmelCase  )
				return "".join(__UpperCAmelCase  )







def     _lowerCamelCase(__UpperCamelCase       )				-> Union[str, Any]:
	_lowerCAmelCase								=set()
	_lowerCAmelCase								=word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)       )
		_lowerCAmelCase								=char
	return pairs



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







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']



			def __init__(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase="<unk>"     ,     **__UpperCAmelCase  ) ->     Union[str, Any]:
				super().__init__(unk_token=__UpperCAmelCase     ,     **__UpperCAmelCase  )

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

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

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



			@property
			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				return True



			@property
			def 					_lowerCAmelCase		(							self  ) ->     int:
				return len(self.encoder  )



			def 					_lowerCAmelCase		(							self  ) ->     int:
				return dict(self.encoder     ,     **self.added_tokens_encoder  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Tuple:
				_lowerCAmelCase								=tuple(token[:-1]  ) + (token[-1] + """</w>""",)
				if token in self.cache:
					return self.cache[token]
				_lowerCAmelCase								=get_pairs(__UpperCAmelCase  )

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

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

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=[]
				if self.fix_text is None:
					# Using BERT's BasicTokenizer
					_lowerCAmelCase								=self.nlp.tokenize(__UpperCAmelCase  )
					for token in text:
						split_tokens.extend(list(self.bpe(__UpperCAmelCase  ).split(""" """  )  )  )
				else:
					# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
					_lowerCAmelCase								=self.nlp(text_standardize(self.fix_text(__UpperCAmelCase  )  )  )
					for token in text:
						split_tokens.extend(list(self.bpe(token.text.lower()  ).split(""" """  )  )  )
				return split_tokens



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Union[str, Any]:
				return self.encoder.get(__UpperCAmelCase     ,     self.encoder.get(self.unk_token  )  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     List[Any]:
				return self.decoder.get(__UpperCAmelCase     ,     self.unk_token  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Dict:
				_lowerCAmelCase								="""""".join(__UpperCAmelCase  ).replace("""</w>"""     ,     """ """  ).strip()
				return out_string



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				if not os.path.isdir(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

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

				_lowerCAmelCase								=0
				with open(__UpperCAmelCase     ,     """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 __UpperCAmelCase  : 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!"""  )
							_lowerCAmelCase								=token_index
						writer.write(""" """.join(__UpperCAmelCase  ) + """\n"""  )
						index += 1

				return vocab_file, merge_file









from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


UpperCamelCase__              =						getLogger(__name__)


UpperCamelCase__              =						4
UpperCamelCase__              =						7

UpperCamelCase__              =						{
    'float16': torch.floataa,
    'float32': torch.floataa,
}






def 					snake_case					(							A			)							->							Optional[int]:




	'''simple docstring'''


	if isinstance(A						,   torch.Tensor			):
		return image
	elif isinstance(A						,   PIL.Image.Image			):
		lowerCamelCase_							:		List[str]							   =							[image]

	if isinstance(image[0]						,   PIL.Image.Image			):
		lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							image[0].size
		lowerCamelCase_   ,			lowerCamelCase_							:		str							   =							(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

		lowerCamelCase_							:		List[Any]							   =							[np.array(i.resize((w, h)			)			)[None, :] for i in image]
		lowerCamelCase_							:		Dict							   =							np.concatenate(A						,   axis=0			)
		lowerCamelCase_							:		Any							   =							np.array(A			).astype(np.floataa			) / 255.0
		lowerCamelCase_							:		Any							   =							image.transpose(0						,   3						,   1						,   2			)
		lowerCamelCase_							:		List[Any]							   =							2.0 * image - 1.0
		lowerCamelCase_							:		str							   =							torch.from_numpy(A			)
	elif isinstance(image[0]						,   torch.Tensor			):
		lowerCamelCase_							:		Union[str, Any]							   =							torch.cat(A						,   dim=0			)

	return image



class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = 350 ,       ):







						'''simple docstring'''




						super().__init__(
						    vae=__lowercase ,       text_encoder=__lowercase ,       tokenizer=__lowercase ,       unet=__lowercase ,       low_res_scheduler=__lowercase ,       scheduler=__lowercase ,       safety_checker=__lowercase ,       feature_extractor=__lowercase ,       watermarker=__lowercase ,       max_noise_level=__lowercase ,       )

					def __call__(				self ,       __lowercase ,       __lowercase ,       __lowercase = 75 ,       __lowercase = 9.0 ,       __lowercase = 20 ,       __lowercase = None ,       __lowercase = 1 ,       __lowercase = 0.0 ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = "pil" ,       __lowercase = True ,       __lowercase = None ,       __lowercase = 1 ,       ):







						'''simple docstring'''




						self.check_inputs(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

						# 2. Define call parameters
						if prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Any							   =							1
						elif prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		List[Any]							   =							len(__lowercase					)
						else:
							lowerCamelCase_							:		Optional[Any]							   =							prompt_embeds.shape[0]

						lowerCamelCase_							:		List[Any]							   =							self._execution_device
						# 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.
						lowerCamelCase_							:		int							   =							guidance_scale > 1.0

						# 3. Encode input prompt
						lowerCamelCase_							:		Optional[int]							   =							self._encode_prompt(
						    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       prompt_embeds=__lowercase ,       negative_prompt_embeds=__lowercase ,       )

						lowerCamelCase_							:		str							   =							ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

						# 4. Preprocess image
						lowerCamelCase_							:		List[str]							   =							preprocess(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							image.cpu()

						# 5. set timesteps
						self.scheduler.set_timesteps(__lowercase ,       device=__lowercase					)
						lowerCamelCase_							:		int							   =							self.scheduler.timesteps

						# 5. Add noise to image
						lowerCamelCase_							:		Optional[Any]							   =							torch.tensor([noise_level] ,       dtype=torch.long ,       device=__lowercase					)
						lowerCamelCase_							:		List[Any]							   =							torch.randn(image.shape ,       generator=__lowercase ,       device=__lowercase ,       dtype=__lowercase					)
						lowerCamelCase_							:		Tuple							   =							self.low_res_scheduler.add_noise(__lowercase ,       __lowercase ,       __lowercase					)

						lowerCamelCase_							:		Dict							   =							2 if do_classifier_free_guidance else 1
						lowerCamelCase_							:		Any							   =							np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
						lowerCamelCase_							:		str							   =							np.concatenate([noise_level] * image.shape[0]					)

						# 6. Prepare latent variables
						lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							image.shape[2:]
						lowerCamelCase_							:		Tuple							   =							self.prepare_latents(
						    batch_size * num_images_per_prompt ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       )

						# 7. Check that sizes of image and latents match
						lowerCamelCase_							:		List[str]							   =							image.shape[1]
						if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
							raise ValueError(
							    'Incorrect configuration settings! The config of `pipeline.unet` expects'
							    f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
							    f" `num_channels_image`: {num_channels_image} "
							    f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
							    ' `pipeline.unet` or your `image` input.'					)

						# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
						lowerCamelCase_							:		Dict							   =							self.prepare_extra_step_kwargs(__lowercase ,       __lowercase					)

						lowerCamelCase_							:		int							   =							next(
						    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep') ,       'tensor(float)'					)
						lowerCamelCase_							:		Any							   =							ORT_TO_NP_TYPE[timestep_dtype]

						# 9. Denoising loop
						lowerCamelCase_							:		List[Any]							   =							len(__lowercase					) - num_inference_steps * self.scheduler.order
						with self.progress_bar(total=__lowercase					) as progress_bar:
							for i, t in enumerate(__lowercase					):
								# expand the latents if we are doing classifier free guidance
								lowerCamelCase_							:		Optional[int]							   =							np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

								# concat latents, mask, masked_image_latents in the channel dimension
								lowerCamelCase_							:		List[Any]							   =							self.scheduler.scale_model_input(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		Optional[Any]							   =							np.concatenate([latent_model_input, image] ,       axis=1					)

								# timestep to tensor
								lowerCamelCase_							:		Optional[int]							   =							np.array([t] ,       dtype=__lowercase					)

								# predict the noise residual
								lowerCamelCase_							:		List[str]							   =							self.unet(
								    sample=__lowercase ,       timestep=__lowercase ,       encoder_hidden_states=__lowercase ,       class_labels=noise_level.astype(np.intaa					) ,       )[0]

								# perform guidance
								if do_classifier_free_guidance:
									lowerCamelCase_   ,			lowerCamelCase_							:		int							   =							np.split(__lowercase ,       2					)
								lowerCamelCase_							:		Tuple							   =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								lowerCamelCase_							:		str							   =							self.scheduler.step(
								    torch.from_numpy(__lowercase					) ,       __lowercase ,       __lowercase ,       **__lowercase					).prev_sample

								# call the callback, if provided
								if i == len(__lowercase					) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
										callback(__lowercase ,       __lowercase ,       __lowercase					)

        # 10. Post-processing
						lowerCamelCase_							:		Dict							   =							self.decode_latents(latents.float()					)

						# 11. Convert to PIL
						if output_type == "pil":
							lowerCamelCase_							:		List[str]							   =							self.numpy_to_pil(__lowercase					)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							1 / 0.0_83_33 * latents
						lowerCamelCase_							:		List[Any]							   =							self.vae(latent_sample=__lowercase					)[0]
						lowerCamelCase_							:		List[Any]							   =							np.clip(image / 2 + 0.5 ,       0 ,       1					)
						lowerCamelCase_							:		List[Any]							   =							image.transpose((0, 2, 3, 1)					)
						return image





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       ):







						'''simple docstring'''




						if prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Tuple							   =							1
						elif prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Union[str, Any]							   =							len(__lowercase					)
						else:
							lowerCamelCase_							:		Any							   =							prompt_embeds.shape[0]

						if prompt_embeds is None:
							lowerCamelCase_							:		Dict							   =							self.tokenizer(
							    __lowercase ,       padding='max_length' ,       max_length=self.tokenizer.model_max_length ,       truncation=__lowercase ,       return_tensors='pt' ,       )
							lowerCamelCase_							:		List[str]							   =							text_inputs.input_ids
							lowerCamelCase_							:		Tuple							   =							self.tokenizer(__lowercase ,       padding='longest' ,       return_tensors='pt'					).input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
							    __lowercase ,       __lowercase					):
								lowerCamelCase_							:		Any							   =							self.tokenizer.batch_decode(
								    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
								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}"					)

							# no positional arguments to text_encoder
							lowerCamelCase_							:		Tuple							   =							self.text_encoder(
							    input_ids=text_input_ids.int().to(__lowercase					) ,       )
							lowerCamelCase_							:		List[str]							   =							prompt_embeds[0]

						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Optional[Any]							   =							prompt_embeds.shape
						# duplicate text embeddings for each generation per prompt, using mps friendly method
						lowerCamelCase_							:		str							   =							prompt_embeds.repeat(1 ,       __lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							prompt_embeds.reshape(bs_embed * num_images_per_prompt ,       __lowercase ,       -1					)

						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance and negative_prompt_embeds is None:
							lowerCamelCase_							:		List[str]
							if negative_prompt is None:
								lowerCamelCase_							:		Dict							   =							[''] * batch_size
							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					):
								lowerCamelCase_							:		Union[str, Any]							   =							[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:
								lowerCamelCase_							:		Any							   =							negative_prompt

							lowerCamelCase_							:		str							   =							text_input_ids.shape[-1]
							lowerCamelCase_							:		str							   =							self.tokenizer(
							    __lowercase ,       padding='max_length' ,       max_length=__lowercase ,       truncation=__lowercase ,       return_tensors='pt' ,       )

							# if hasattr(uncond_input, "attention_mask"):
							#     attention_mask = uncond_input.attention_mask.to(device)
							# else:
							#     attention_mask = None

							lowerCamelCase_							:		Dict							   =							self.text_encoder(
							    input_ids=uncond_input.input_ids.int().to(__lowercase					) ,       )
							lowerCamelCase_							:		Optional[Any]							   =							uncond_embeddings[0]

						if do_classifier_free_guidance:
							lowerCamelCase_							:		Union[str, Any]							   =							uncond_embeddings.shape[1]
							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							lowerCamelCase_							:		Any							   =							uncond_embeddings.repeat(1 ,       __lowercase					)
							lowerCamelCase_							:		Union[str, Any]							   =							uncond_embeddings.reshape(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
							lowerCamelCase_							:		Any							   =							np.concatenate([uncond_embeddings, prompt_embeds]					)

						return prompt_embeds
