

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

import regex as re

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


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

lowerCamelCase									= logging.get_logger(__name__)

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

lowerCamelCase									= {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
}

lowerCamelCase									= {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}




@lru_cache()
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			Dict							):





  '''simple docstring'''







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







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     str    =       VOCAB_FILES_NAMES
   lowerCamelCase__			:     Tuple    =       PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__			:     Union[str, Any]    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__			:     Any    =       ['input_ids', 'attention_mask']



   def __init__(    self,   lowercase_,   lowercase_,   lowercase_="replace",   lowercase_="<|endoftext|>",   lowercase_="<|endoftext|>",   lowercase_="<|endoftext|>",   lowercase_=None,   lowercase_=False,   lowercase_=False,   **lowercase_,   )							->   Optional[Any]:






     """simple docstring"""






     a__  =AddedToken(lowercase_,   lstrip=lowercase_,   rstrip=lowercase_	) if isinstance(lowercase_,   lowercase_	) else bos_token
     a__  =AddedToken(lowercase_,   lstrip=lowercase_,   rstrip=lowercase_	) if isinstance(lowercase_,   lowercase_	) else eos_token
     a__  =AddedToken(lowercase_,   lstrip=lowercase_,   rstrip=lowercase_	) if isinstance(lowercase_,   lowercase_	) else unk_token
     a__  =AddedToken(lowercase_,   lstrip=lowercase_,   rstrip=lowercase_	) if isinstance(lowercase_,   lowercase_	) else pad_token
     super().__init__(
         errors=lowercase_,   unk_token=lowercase_,   bos_token=lowercase_,   eos_token=lowercase_,   pad_token=lowercase_,   add_prefix_space=lowercase_,   add_bos_token=lowercase_,   **lowercase_,   )
     a__  =add_bos_token

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

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



   @property
   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     return len(self.encoder	)



   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     if token in self.cache:
       return self.cache[token]
     a__  =tuple(lowercase_	)
     a__  =get_pairs(lowercase_	)

     if not pairs:
       return token

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

         if word[i] == first and i < len(lowercase_	) - 1 and word[i + 1] == second:
           new_word.append(first + second	)
           i += 2
         else:
           new_word.append(word[i]	)
           i += 1
       a__  =tuple(lowercase_	)
       a__  =new_word
       if len(lowercase_	) == 1:
         break
       else:
         a__  =get_pairs(lowercase_	)
     a__  =''' '''.join(lowercase_	)
     a__  =word
     return word



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=None	)							->   Any:






     """simple docstring"""






     if self.add_bos_token:
       a__  =[self.bos_token_id]
     else:
       a__  =[]

     a__  =bos_token_ids + token_ids_a

     if token_ids_a is None:
       return output

     return output + bos_token_ids + token_ids_a



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   List[int]:






     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowercase_,   token_ids_a=lowercase_,   already_has_special_tokens=lowercase_	)

     if not self.add_bos_token:
       return super().get_special_tokens_mask(
           token_ids_a=lowercase_,   token_ids_a=lowercase_,   already_has_special_tokens=lowercase_	)

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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     a__  =[]
     for token in re.findall(self.pat,   lowercase_	):
       a__  =''''''.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(lowercase_	).split(''' '''	)	)
     return bpe_tokens



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Tuple:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   List[Any]:






     """simple docstring"""






     return self.decoder.get(lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''.join(lowercase_	)
     a__  =bytearray([self.byte_decoder[c] for c in text]	).decode('''utf-8''',   errors=self.errors	)
     return text



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None	)							->   Tuple[str]:






     """simple docstring"""






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

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

     a__  =0
     with open(lowercase_,   '''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 lowercase_	: 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!'''	)
           a__  =token_index
         writer.write(''' '''.join(lowercase_	) + '''\n'''	)
         index += 1

     return vocab_file, merge_file



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=False,   **lowercase_	)							->   Optional[int]:






     """simple docstring"""






     a__  =kwargs.pop('''add_prefix_space''',   self.add_prefix_space	)
     if is_split_into_words or add_prefix_space:
       a__  =''' ''' + text
     return (text, kwargs)



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   List[int]:






     """simple docstring"""






     a__  =[]
     for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(lowercase_,   add_special_tokens=lowercase_	) + [self.eos_token_id]	)
     if len(lowercase_	) > self.model_max_length:
       a__  =input_ids[-self.model_max_length :]
     return input_ids



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 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 0.93,   lowercase_ = True,   )							->   Any:






     """simple docstring"""






     a__  =mixing_coeff
     a__  =leapfrog_steps

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

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   int:






     """simple docstring"""






     a__  =self.tokenizer(
         lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

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

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

     if do_classifier_free_guidance:
       a__  ='''''' if negative_prompt is None else negative_prompt

       a__  =self.tokenizer(
           lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

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

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

     return prompt_embeds



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






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

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Dict:






     """simple docstring"""






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

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =int(lowercase_	)

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

     return alpha_prod, 1 - alpha_prod



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






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

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

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

     a__  =(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






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

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

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

     return model_input, next_model_input.to(base.dtype	)



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """simple docstring"""






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



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   )							->   str:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =image.to(device=self.device,   dtype=text_embeds.dtype	)
     a__  =self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

     a__  =self.vae.config.scaling_factor * latent

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

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

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

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

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

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

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

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

         a__,				a__  =self.noise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 3.0,   lowercase_ = 50,   lowercase_ = 0.8,   lowercase_ = None,   lowercase_ = None,   lowercase_ = "pil",   )							->   List[Any]:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =self.image_processor.preprocess(lowercase_	)

     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)
     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)

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

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

     a__  =self.prepare_latents(lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

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

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

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

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

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

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

         a__,				a__  =self.denoise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

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

     # either one is fine
     a__  =coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       a__  =(
           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''',   lowercase_,   standard_warn=lowercase_	)
       a__  ='''np'''

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

     return image
