



'''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


_lowercase       :						List[Any]						=logging.get_logger(__name__)

_lowercase       :						Optional[Any]						={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowercase       :						Tuple						={
    "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"},
}

_lowercase       :						Tuple						={
    "openai-gpt": 512,
}


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]			)		->						Optional[int]:
				snake_case__      :   Optional[int]      =    text.strip()
				if not text:
								return []
				snake_case__      :   Union[str, Any]      =    text.split()
				return tokens






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Union[str, Any]=True    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Optional[int]=True    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Dict=True    ,			)      ->      Any:
							"""simple docstring"""





							if never_split is None:
											snake_case__      :   int      =    []
							snake_case__      :   Optional[Any]      =    do_lower_case
							snake_case__      :   Dict      =    set(__UpperCamelCase			)
							snake_case__      :   List[str]      =    tokenize_chinese_chars
							snake_case__      :   Any      =    strip_accents
							snake_case__      :   Union[str, Any]      =    do_split_on_punc



			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Any=None			)      ->      int:
							"""simple docstring"""





							snake_case__      :   int      =    self.never_split.union(set(__UpperCamelCase			)			) if never_split else self.never_split
							snake_case__      :   Optional[Any]      =    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:
											snake_case__      :   Union[str, Any]      =    self._tokenize_chinese_chars(__UpperCamelCase			)
							# prevents treating the same character with different unicode codepoints as different characters
							snake_case__      :   str      =    unicodedata.normalize('''NFC'''    ,			__UpperCamelCase			)
							snake_case__      :   Any      =    whitespace_tokenize(__UpperCamelCase			)
							snake_case__      :   List[str]      =    []
							for token in orig_tokens:
											if token not in never_split:
															if self.do_lower_case:
																			snake_case__      :   Any      =    token.lower()
																			if self.strip_accents is not False:
																							snake_case__      :   List[Any]      =    self._run_strip_accents(__UpperCamelCase			)
															elif self.strip_accents:
																			snake_case__      :   Dict      =    self._run_strip_accents(__UpperCamelCase			)
											split_tokens.extend(self._run_split_on_punc(__UpperCamelCase    ,			__UpperCamelCase			)			)

							snake_case__      :   Dict      =    whitespace_tokenize(''' '''.join(__UpperCamelCase			)			)
							return output_tokens



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Dict      =    unicodedata.normalize('''NFD'''    ,			__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    []
							for char in text:
											snake_case__      :   int      =    unicodedata.category(__UpperCamelCase			)
											if cat == "Mn":
															continue
											output.append(__UpperCamelCase			)
							return "".join(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[Any]=None			)      ->      Union[str, Any]:
							"""simple docstring"""





							if not self.do_split_on_punc or (never_split is not None and text in never_split):
											return [text]
							snake_case__      :   Optional[Any]      =    list(__UpperCamelCase			)
							snake_case__      :   str      =    0
							snake_case__      :   str      =    True
							snake_case__      :   Dict      =    []
							while i < len(__UpperCamelCase			):
											snake_case__      :   List[str]      =    chars[i]
											if _is_punctuation(__UpperCamelCase			):
															output.append([char]			)
															snake_case__      :   Optional[int]      =    True
											else:
															if start_new_word:
																			output.append([]			)
															snake_case__      :   Any      =    False
															output[-1].append(__UpperCamelCase			)
											i += 1

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



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						Tuple			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    []
							for char in text:
											snake_case__      :   str      =    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		:						Union[str, Any]    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							if (
							    (cp >= 0X4E_00 and cp <= 0X9F_FF)
							    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
							    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
							    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
							    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
							    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
							    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
							    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
							):  #
											return True

							return False







			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						str			)      ->      Any:
							"""simple docstring"""





							snake_case__      :   str      =    []
							for char in text:
											snake_case__      :   List[Any]      =    ord(__UpperCamelCase			)
											if cp == 0 or cp == 0XFF_FD or _is_control(__UpperCamelCase			):
															continue
											if _is_whitespace(__UpperCamelCase			):
															output.append(''' '''			)
											else:
															output.append(__UpperCamelCase			)
							return "".join(__UpperCamelCase			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]			)		->						Union[str, Any]:
				snake_case__      :   Dict      =    set()
				snake_case__      :   Tuple      =    word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)			)
								snake_case__      :   Dict      =    char
				return pairs


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple			)		->						Dict:
				snake_case__      :   int      =    text.replace('''—'''   ,				'''-'''			)
				snake_case__      :   Optional[int]      =    text.replace('''–'''   ,				'''-'''			)
				snake_case__      :   Union[str, Any]      =    text.replace('''―'''   ,				'''-'''			)
				snake_case__      :   Any      =    text.replace('''…'''   ,				'''...'''			)
				snake_case__      :   Tuple      =    text.replace('''´'''   ,				'''\''''			)
				snake_case__      :   int      =    re.sub(R'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''   ,				R''' \1 '''   ,				UpperCamelCase__			)
				snake_case__      :   Optional[int]      =    re.sub(R'''\s*\n\s*'''   ,				''' \n '''   ,				UpperCamelCase__			)
				snake_case__      :   List[Any]      =    re.sub(R'''[^\S\n]+'''   ,				''' '''   ,				UpperCamelCase__			)
				return text.strip()






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  VOCAB_FILES_NAMES
			A__								=  PRETRAINED_VOCAB_FILES_MAP
			A__								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__								=  ['input_ids', 'attention_mask']



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						int="<unk>"    ,			**__UpperCamelCase		:						str			)      ->      Optional[Any]:
							"""simple docstring"""





							super().__init__(unk_token=__UpperCamelCase    ,			**__UpperCamelCase			)

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

											snake_case__      :   Union[str, Any]      =    English()
											snake_case__      :   List[str]      =    _nlp.tokenizer
											snake_case__      :   Tuple      =    ftfy.fix_text
							except ImportError:
											logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''			)
											snake_case__      :   List[Any]      =    BasicTokenizer(do_lower_case=__UpperCamelCase			)
											snake_case__      :   Optional[int]      =    None

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



			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[int]:
							"""simple docstring"""





							return True



			@property
			def 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





							return len(self.encoder			)



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return dict(self.encoder    ,			**self.added_tokens_encoder			)



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Dict			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    tuple(token[:-1]			) + (token[-1] + '''</w>''',)
							if token in self.cache:
											return self.cache[token]
							snake_case__      :   Union[str, Any]      =    get_pairs(__UpperCamelCase			)

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

							while True:
											snake_case__      :   Any      =    min(__UpperCamelCase    ,			key=lambda __UpperCamelCase			: self.bpe_ranks.get(__UpperCamelCase    ,			float('''inf'''			)			)			)
											if bigram not in self.bpe_ranks:
															break
											snake_case__  ,      snake_case__      :   Any      =    bigram
											snake_case__      :   List[Any]      =    []
											snake_case__      :   Any      =    0
											while i < len(__UpperCamelCase			):
															try:
																			snake_case__      :   Any      =    word.index(__UpperCamelCase    ,			__UpperCamelCase			)
															except ValueError:
																			new_word.extend(word[i:]			)
																			break
															else:
																			new_word.extend(word[i:j]			)
																			snake_case__      :   Any      =    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
											snake_case__      :   Optional[Any]      =    tuple(__UpperCamelCase			)
											snake_case__      :   Dict      =    new_word
											if len(__UpperCamelCase			) == 1:
															break
											else:
															snake_case__      :   List[Any]      =    get_pairs(__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    ''' '''.join(__UpperCamelCase			)
							if word == "\n  </w>":
											snake_case__      :   Union[str, Any]      =    '''\n</w>'''
							snake_case__      :   List[Any]      =    word
							return word



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[Any]:
							"""simple docstring"""





							snake_case__      :   Any      =    []
							if self.fix_text is None:
											# Using BERT's BasicTokenizer
											snake_case__      :   Any      =    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)
											snake_case__      :   int      =    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		:						Tuple    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      List[Any]:
							"""simple docstring"""





							return self.encoder.get(__UpperCamelCase    ,			self.encoder.get(self.unk_token			)			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[Any]			)      ->      Optional[int]:
							"""simple docstring"""





							return self.decoder.get(__UpperCamelCase    ,			self.unk_token			)



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						int			)      ->      int:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    ''''''.join(__UpperCamelCase			).replace('''</w>'''    ,			''' '''			).strip()
							return out_string







			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Optional[str] = None			)      ->      Tuple[str]:
							"""simple docstring"""





							if not os.path.isdir(__UpperCamelCase			):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
											return
							snake_case__      :   Optional[Any]      =    os.path.join(
							    __UpperCamelCase    ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
							snake_case__      :   str      =    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'''			)

							snake_case__      :   Union[str, Any]      =    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!'''			)
																			snake_case__      :   Optional[int]      =    token_index
															writer.write(''' '''.join(__UpperCamelCase			) + '''\n'''			)
															index += 1

							return vocab_file, merge_file





"""simple docstring"""
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

        self.register_modules(vae=lowercase       ,     text_encoder=lowercase       ,     tokenizer=lowercase       ,     unet=lowercase       ,     scheduler=lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])



      def __call__(		self       ,     lowercase       ,     lowercase       ,     lowercase = 0.3       ,     lowercase = 0.6       ,     lowercase = 0.5       ,     lowercase = 42       ,     lowercase = 50       ,     lowercase = 7.5       ,     )      ->     Image.Image:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      self.scheduler.step(lowercase       ,     self.scheduler.timesteps[tmax]       ,     lowercase).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
          if i > tmax:
            if i < tmin:  # layout generation phase
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

              a__:	Dict     				=      (mix_factor * latents) + (
                  1 - mix_factor
              ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
              a__:	List[Any]     				=      torch.cat([input] * 2)

            else:  # content generation phase
              a__:	Dict     				=      torch.cat([latents] * 2)

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)


