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 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_					,					A_					,					A_					,					A_					,					A_ = 0.93					,					A_ = True					,					)			->					List[Any]:






           """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 		UpperCamelCase_    (       self					,					A_					,					A_ = None					,					A_ = False    )			->					int:






           """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 		UpperCamelCase_    (       self					,					A_					,					A_    )			->					List[Any]:






           """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 		UpperCamelCase_    (       self					,					A_					,					A_    )			->					Dict:






           """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 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           # as self.alphas_cumprod is always in cpu
           _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 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_					,					)			->					Optional[Any]:






           """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 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_					,					)			->					Any:






           """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 		UpperCamelCase_    (       self					,					A_    )			->					List[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 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_					,					A_ = 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					,					A_					,					A_					,					A_					,					A_ = 3.0					,					A_ = 50					,					A_ = 0.8					,					A_ = None					,					A_ = None					,					A_ = "pil"					,					)			->					List[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
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


snake_case__						      =			logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case__						      =			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

snake_case__						      =			{
    'RUCAIBox/mvp': 1024,
}
@lru_cache()
def    __magic_name__(	)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					(
          list(range(ord('''!'''		)			,    ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)			,    ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)			,    ord('''ÿ'''		) + 1		)		)
      )
      _lowerCamelCase           =					bs[:]
      _lowerCamelCase           =					0
      for b in range(2**8		):
            if b not in bs:
                  bs.append(__UpperCAmelCase		)
                  cs.append(2**8 + n		)
                  n += 1
      _lowerCamelCase           =					[chr(__UpperCAmelCase		) for n in cs]
      return dict(zip(__UpperCAmelCase			,    __UpperCAmelCase		)		)
def    __magic_name__(	__UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

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




class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	VOCAB_FILES_NAMES
     A_					    =	PRETRAINED_VOCAB_FILES_MAP
     A_					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_					    =	['input_ids', 'attention_mask']



     def __init__(       self					,					A_					,					A_					,					A_="replace"					,					A_="<s>"					,					A_="</s>"					,					A_="</s>"					,					A_="<s>"					,					A_="<unk>"					,					A_="<pad>"					,					A_="<mask>"					,					A_=False					,					**A_					,					)			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else bos_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else eos_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else sep_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else cls_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else unk_token
           _lowerCamelCase           =					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
           _lowerCamelCase           =					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:
                 _lowerCamelCase           =					json.load(A_    )
           _lowerCamelCase           =					{v: k for k, v in self.encoder.items()}
           _lowerCamelCase           =					errors  # how to handle errors in decoding
           _lowerCamelCase           =					bytes_to_unicode()
           _lowerCamelCase           =					{v: k for k, v in self.byte_encoder.items()}
           with open(A_					,					encoding='''utf-8'''    ) as merges_handle:
                 _lowerCamelCase           =					merges_handle.read().split('''\n'''    )[1:-1]
           _lowerCamelCase           =					[tuple(merge.split()    ) for merge in bpe_merges]
           _lowerCamelCase           =					dict(zip(A_					,					range(len(A_    )    )    )    )
           _lowerCamelCase           =					{}
           _lowerCamelCase           =					add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           _lowerCamelCase           =					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    )			->					Dict:






           """simple docstring"""
           return len(self.encoder    )



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           return dict(self.encoder					,					**self.added_tokens_encoder    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           if token in self.cache:
                 return self.cache[token]
           _lowerCamelCase           =					tuple(A_    )
           _lowerCamelCase           =					get_pairs(A_    )

           if not pairs:
                 return token

           while True:
                 _lowerCamelCase           =					min(A_					,					key=lambda A_    : self.bpe_ranks.get(A_					,					float('''inf'''    )    )    )
                 if bigram not in self.bpe_ranks:
                       break
                 _lowerCamelCase     ,       _lowerCamelCase           =					bigram
                 _lowerCamelCase           =					[]
                 _lowerCamelCase           =					0
                 while i < len(A_    ):
                       try:
                             _lowerCamelCase           =					word.index(A_					,					A_    )
                       except ValueError:
                             new_word.extend(word[i:]    )
                             break
                       else:
                             new_word.extend(word[i:j]    )
                             _lowerCamelCase           =					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
                 _lowerCamelCase           =					tuple(A_    )
                 _lowerCamelCase           =					new_word
                 if len(A_    ) == 1:
                       break
                 else:
                       _lowerCamelCase           =					get_pairs(A_    )
           _lowerCamelCase           =					''' '''.join(A_    )
           _lowerCamelCase           =					word
           return word



     def 		UpperCamelCase_    (       self					,					A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					[]
           for token in re.findall(self.pat					,					A_    ):
                 _lowerCamelCase           =					''''''.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 		UpperCamelCase_    (       self					,					A_    )			->					Optional[Any]:






           """simple docstring"""
           return self.encoder.get(A_					,					self.encoder.get(self.unk_token    )    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Any:






           """simple docstring"""
           return self.decoder.get(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					''''''.join(A_    )
           _lowerCamelCase           =					bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''					,					errors=self.errors    )
           return text



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Tuple[str]:






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

           _lowerCamelCase           =					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 A_    : 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(A_    ) + '''\n'''    )
                       index += 1

           return vocab_file, merge_file



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					List[int]:






           """simple docstring"""
           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]
           _lowerCamelCase           =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None					,					A_ = False    )			->					List[int]:






           """simple docstring"""
           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 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					List[int]:






           """simple docstring"""
           _lowerCamelCase           =					[self.sep_token_id]
           _lowerCamelCase           =					[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 		UpperCamelCase_    (       self					,					A_					,					A_=False					,					**A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					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()):
                 _lowerCamelCase           =					''' ''' + text
           return (text, kwargs)