

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_					  =   logging.get_logger(__name__)

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

lowerCamelCase_					  =   {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
}

lowerCamelCase_					  =   {
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}



@lru_cache()
def   UpperCamelCase(   )      ->							str:





  '''simple docstring'''





  snake_case_					= (
      list(range(ord("""!"""				)     ,   ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				)     ,   ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				)     ,   ord("""ÿ"""				) + 1				)				)
  )
  snake_case_					= bs[:]
  snake_case_					= 0
  for b in range(2**8				):
    if b not in bs:
      bs.append(a__				)
      cs.append(2**8 + n				)
      n += 1
  snake_case_					= [chr(a__				) for n in cs]
  return dict(zip(a__     ,   a__				)				)



def   UpperCamelCase(   lowercase_				)      ->							List[str]:





  '''simple docstring'''





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







class 					__lowerCamelCase							(      __lowercase      ):
      lowerCamelCase_      : Any  			=   VOCAB_FILES_NAMES
      lowerCamelCase_      : Union[str, Any]  			=   PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase_      : Union[str, Any]  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase_      : Optional[int]  			=   ['input_ids', 'attention_mask']







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase="replace"				,    lowerCamelCase="<s>"				,    lowerCamelCase="</s>"				,    lowerCamelCase="</s>"				,    lowerCamelCase="<s>"				,    lowerCamelCase="<unk>"				,    lowerCamelCase="<pad>"				,    lowerCamelCase="<mask>"				,    lowerCamelCase=False				,    **lowerCamelCase				,    )		->			Union[str, Any]:
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else bos_token
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else eos_token
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else sep_token
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else cls_token
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else unk_token
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        snake_case_					= AddedToken(__A				,    lstrip=__A				,    rstrip=__A  ) if isinstance(__A				,    __A  ) else mask_token

        super().__init__(
            errors=__A				,    bos_token=__A				,    eos_token=__A				,    unk_token=__A				,    sep_token=__A				,    cls_token=__A				,    pad_token=__A				,    mask_token=__A				,    add_prefix_space=__A				,    **__A				,    )

        with open(__A				,    encoding="""utf-8"""  ) as vocab_handle:
          snake_case_					= json.load(__A  )
        snake_case_					= {v: k for k, v in self.encoder.items()}
        snake_case_					= errors  # how to handle errors in decoding
        snake_case_					= bytes_to_unicode()
        snake_case_					= {v: k for k, v in self.byte_encoder.items()}
        with open(__A				,    encoding="""utf-8"""  ) as merges_handle:
          snake_case_					= merges_handle.read().split("""\n"""  )[1:-1]
        snake_case_					= [tuple(merge.split()  ) for merge in bpe_merges]
        snake_case_					= dict(zip(__A				,    range(len(__A  )  )  )  )
        snake_case_					= {}
        snake_case_					= add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        snake_case_					= re.compile(r"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""  )







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







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        return dict(self.encoder				,    **self.added_tokens_encoder  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			Any:
        if token in self.cache:
          return self.cache[token]
        snake_case_					= tuple(__A  )
        snake_case_					= get_pairs(__A  )

        if not pairs:
          return token

        while True:
          snake_case_					= min(__A				,    key=lambda lowerCamelCase  : self.bpe_ranks.get(__A				,    float("""inf"""  )  )  )
          if bigram not in self.bpe_ranks:
            break
          snake_case_		,							snake_case_					= bigram
          snake_case_					= []
          snake_case_					= 0
          while i < len(__A  ):
            try:
              snake_case_					= word.index(__A				,    __A  )
            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(__A  ) - 1 and word[i + 1] == second:
              new_word.append(first + second  )
              i += 2
            else:
              new_word.append(word[i]  )
              i += 1
          snake_case_					= tuple(__A  )
          snake_case_					= new_word
          if len(__A  ) == 1:
            break
          else:
            snake_case_					= get_pairs(__A  )
        snake_case_					= """ """.join(__A  )
        snake_case_					= word
        return word







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			List[Any]:
        snake_case_					= []
        for token in re.findall(self.pat				,    __A  ):
          snake_case_					= """""".join(
              self.byte_encoder[b] for b in token.encode("""utf-8"""  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A  ).split(""" """  )  )
        return bpe_tokens







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			Dict:
        return self.encoder.get(__A				,    self.encoder.get(self.unk_token  )  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			Union[str, Any]:
        return self.decoder.get(__A  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			str:
        snake_case_					= """""".join(__A  )
        snake_case_					= bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8"""				,    errors=self.errors  )
        return text







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

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

        snake_case_					= 0
        with open(__A				,    """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 lowerCamelCase  : 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(__A  ) + """\n"""  )
            index += 1

        return vocab_file, merge_file







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = None  )		->			List[int]:
        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        snake_case_					= [self.cls_token_id]
        snake_case_					= [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = None				,    lowerCamelCase = False  )		->			List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__A				,    token_ids_a=__A				,    already_has_special_tokens=__A  )

        if token_ids_a is None:
          return [1] + ([0] * len(__A  )) + [1]
        return [1] + ([0] * len(__A  )) + [1, 1] + ([0] * len(__A  )) + [1]







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = None  )		->			List[int]:
        snake_case_					= [self.sep_token_id]
        snake_case_					= [self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep  ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase=False				,    **lowerCamelCase  )		->			List[str]:
        snake_case_					= kwargs.pop("""add_prefix_space"""				,    self.add_prefix_space  )
        if (is_split_into_words or add_prefix_space) and (len(__A  ) > 0 and not text[0].isspace()):
          snake_case_					= """ """ + text
        return (text, kwargs)
'''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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

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






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






		def 		UpperCamelCase__							( self		,    __A					)			->  Dict:
								_lowerCAmelCase					    =(1 / 0.18_215) * latent
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.decode(__A					).sample
								_lowerCAmelCase					    =(img / 2 + 0.5).clamp(0		,    1					)
								_lowerCAmelCase					    =img.detach().cpu().permute(0		,    2		,    3		,    1					).numpy()
								_lowerCAmelCase					    =(img * 255).round().astype('uint8'					)
								return Image.fromarray(img[0]					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

								_lowerCAmelCase					    =self.tokenizer(
								    ''		,    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					)					)[0]

								return torch.cat([uncond_embedding, text_embedding]					)






		def __call__( self		,    __A		,    __A		,    __A = 0.3		,    __A = 0.6		,    __A = 0.5		,    __A = 42		,    __A = 50		,    __A = 7.5		,    )			->  Image.Image:
								_lowerCAmelCase					    =steps - int(kmin * steps					)
								_lowerCAmelCase					    =steps - int(kmax * steps					)

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

								torch.manual_seed(__A					)
								_lowerCAmelCase					    =torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8)		,    ).to(self.device					)

								_lowerCAmelCase					    =self.scheduler.add_noise(
								    __A		,    __A		,    timesteps=self.scheduler.timesteps[tmax]		,    )

								_lowerCAmelCase					    =torch.cat([latents] * 2					)

								_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    self.scheduler.timesteps[tmax]					)

								with torch.no_grad():
														_lowerCAmelCase					    =self.unet(
														    __A		,    self.scheduler.timesteps[tmax]		,    encoder_hidden_states=__A		,    ).sample

								_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
								_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

								_lowerCAmelCase					    =self.scheduler.step(__A		,    self.scheduler.timesteps[tmax]		,    __A					).prev_sample

								for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
														if i > tmax:
																				if i < tmin:  # layout generation phase
																										_lowerCAmelCase					    =self.scheduler.add_noise(
																										    __A		,    __A		,    timesteps=__A		,    )

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

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

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

																				with torch.no_grad():
																										_lowerCAmelCase					    =self.unet(
																										    __A		,    __A		,    encoder_hidden_states=__A		,    ).sample

																				_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
																				_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)





