






"""simple docstring"""



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 	a					(       a_     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 0.9_3							,		_lowerCamelCase = True							,		):
      lowercase							   =	mixing_coeff
      lowercase							   =	leapfrog_steps

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

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = False	):
      lowercase							   =	self.tokenizer(
          _lowerCamelCase							,		padding='max_length'							,		max_length=self.tokenizer.model_max_length							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)

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

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

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

        lowercase							   =	self.tokenizer(
            _lowerCamelCase							,		padding='max_length'							,		max_length=self.tokenizer.model_max_length							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)

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

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

      return prompt_embeds







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
      lowercase							   =	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

      return [x, y]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
      lowercase							   =	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

      return [x, y]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      # as self.alphas_cumprod is always in cpu
      lowercase							   =	int(_lowerCamelCase	)

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

      return alpha_prod, 1 - alpha_prod







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							   =	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      lowercase   ,					lowercase							   =	self._get_alpha_and_beta(_lowerCamelCase	)
      lowercase   ,					lowercase							   =	self._get_alpha_and_beta(_lowerCamelCase	)

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

      lowercase							   =	(base - b_t * model_output) / a_t

      return model_input, next_model_input.to(base.dtype	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							   =	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      lowercase   ,					lowercase							   =	self._get_alpha_and_beta(_lowerCamelCase	)
      lowercase   ,					lowercase							   =	self._get_alpha_and_beta(_lowerCamelCase	)

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

      return model_input, next_model_input.to(base.dtype	)







    @torch.no_grad()
    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	1 / self.vae.config.scaling_factor * latents
      lowercase							   =	self.vae.decode(_lowerCamelCase	).sample
      lowercase							   =	(image / 2 + 0.5).clamp(0							,		1	)
      return image







    @torch.no_grad()
    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		):
      lowercase							   =	guidance_scale > 1.0

      lowercase							   =	image.to(device=self.device							,		dtype=text_embeds.dtype	)
      lowercase							   =	self.vae.encode(_lowerCamelCase	).latent_dist.sample(_lowerCamelCase	)

      lowercase							   =	self.vae.config.scaling_factor * latent

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

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

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

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

          lowercase							   =	coupled_latents[j]
          lowercase							   =	coupled_latents[k]

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

          lowercase							   =	self.unet(_lowerCamelCase							,		_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase	).sample

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

          lowercase   ,					lowercase							   =	self.noise_step(
              base=_lowerCamelCase							,		model_input=_lowerCamelCase							,		model_output=_lowerCamelCase							,		timestep=_lowerCamelCase							,		)

          lowercase							   =	model_input

      return coupled_latents







    @torch.no_grad()
    def __call__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 3.0							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = 0.8							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = "pil"							,		):
      lowercase							   =	guidance_scale > 1.0

      lowercase							   =	self.image_processor.preprocess(_lowerCamelCase	)

      lowercase							   =	self._encode_prompt(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)
      lowercase							   =	self._encode_prompt(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

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

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

      lowercase							   =	self.prepare_latents(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

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

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

          lowercase							   =	coupled_latents[j]
          lowercase							   =	coupled_latents[k]

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

          lowercase							   =	self.unet(_lowerCamelCase							,		_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase	).sample

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

          lowercase   ,					lowercase							   =	self.denoise_step(
              base=_lowerCamelCase							,		model_input=_lowerCamelCase							,		model_output=_lowerCamelCase							,		timestep=_lowerCamelCase							,		)

          lowercase							   =	model_input

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

      # either one is fine
      lowercase							   =	coupled_latents[0]

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

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

      return image










"""simple docstring"""



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

_UpperCamelCase		:  Union[str, Any]						=		logging.get_logger(__name__)

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

_UpperCamelCase		:  Any						=		{
    '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',
    },
}

_UpperCamelCase		:  str						=		{
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}
@lru_cache()
def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





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





  '''simple docstring'''





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



class 	a					(       a_     ):
    UpperCAmelCase_			:					int     					=VOCAB_FILES_NAMES
    UpperCAmelCase_			:					List[str]     					=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_			:					Tuple     					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_			:					Any     					=["input_ids", "attention_mask"]







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="replace"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase=None							,		_lowerCamelCase=False							,		_lowerCamelCase=False							,		**_lowerCamelCase							,		):
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else bos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else eos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else unk_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else pad_token
      super().__init__(
          errors=_lowerCamelCase							,		unk_token=_lowerCamelCase							,		bos_token=_lowerCamelCase							,		eos_token=_lowerCamelCase							,		pad_token=_lowerCamelCase							,		add_prefix_space=_lowerCamelCase							,		add_bos_token=_lowerCamelCase							,		**_lowerCamelCase							,		)
      lowercase							   =	add_bos_token

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowercase							   =	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	):
      return len(self.encoder	)







    def       UpperCamelCase_				(      self	):
      return dict(self.encoder							,		**self.added_tokens_encoder	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if token in self.cache:
        return self.cache[token]
      lowercase							   =	tuple(_lowerCamelCase	)
      lowercase							   =	get_pairs(_lowerCamelCase	)

      if not pairs:
        return token

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

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=None	):
      if self.add_bos_token:
        lowercase							   =	[self.bos_token_id]
      else:
        lowercase							   =	[]

      lowercase							   =	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							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = False	):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCamelCase							,		token_ids_a=_lowerCamelCase							,		already_has_special_tokens=_lowerCamelCase	)

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

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	[]
      for token in re.findall(self.pat							,		_lowerCamelCase	):
        lowercase							   =	''.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(_lowerCamelCase	).split(' '	)	)
      return bpe_tokens







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.encoder.get(_lowerCamelCase							,		self.encoder.get(self.unk_token	)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.decoder.get(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	''.join(_lowerCamelCase	)
      lowercase							   =	bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'							,		errors=self.errors	)
      return text







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if not os.path.isdir(_lowerCamelCase	):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
        return
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

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

      lowercase							   =	0
      with open(_lowerCamelCase							,		'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!'	)
            lowercase							   =	token_index
          writer.write(' '.join(_lowerCamelCase	) + '\n'	)
          index += 1

      return vocab_file, merge_file







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.pop('add_prefix_space'							,		self.add_prefix_space	)
      if is_split_into_words or add_prefix_space:
        lowercase							   =	' ' + text
      return (text, kwargs)







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


