






"""simple docstring"""



from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_UpperCamelCase		:  List[str]						=		getLogger(__name__)


_UpperCamelCase		:  Dict						=		4
_UpperCamelCase		:  Dict						=		7

_UpperCamelCase		:  Any						=		{
    'float16': torch.floataa,
    'float32': torch.floataa,
}
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Union[str, Any]   ):





  '''simple docstring'''





  if isinstance(__snake_case		, torch.Tensor   ):
    return image
  elif isinstance(__snake_case		, PIL.Image.Image   ):
    lowercase							   =	[image]

  if isinstance(image[0]		, PIL.Image.Image   ):
    lowercase   ,					lowercase							   =	image[0].size
    lowercase   ,					lowercase							   =	(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

    lowercase							   =	[np.array(i.resize((w, h)   )   )[None, :] for i in image]
    lowercase							   =	np.concatenate(__snake_case		, axis=0   )
    lowercase							   =	np.array(__snake_case   ).astype(np.floataa   ) / 255.0
    lowercase							   =	image.transpose(0		, 3		, 1		, 2   )
    lowercase							   =	2.0 * image - 1.0
    lowercase							   =	torch.from_numpy(__snake_case   )
  elif isinstance(image[0]		, torch.Tensor   ):
    lowercase							   =	torch.cat(__snake_case		, dim=0   )

  return image



class 	a					(       a_     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 3_5_0							,		):
      super().__init__(
          vae=_lowerCamelCase							,		text_encoder=_lowerCamelCase							,		tokenizer=_lowerCamelCase							,		unet=_lowerCamelCase							,		low_res_scheduler=_lowerCamelCase							,		scheduler=_lowerCamelCase							,		safety_checker=_lowerCamelCase							,		feature_extractor=_lowerCamelCase							,		watermarker=_lowerCamelCase							,		max_noise_level=_lowerCamelCase							,		)







    def __call__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 7_5							,		_lowerCamelCase = 9.0							,		_lowerCamelCase = 2_0							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		_lowerCamelCase = 0.0							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		):
      self.check_inputs(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

      # 2. Define call parameters
      if prompt is not None and isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	1
      elif prompt is not None and isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	len(_lowerCamelCase	)
      else:
        lowercase							   =	prompt_embeds.shape[0]

      lowercase							   =	self._execution_device
      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      lowercase							   =	guidance_scale > 1.0

      # 3. Encode input prompt
      lowercase							   =	self._encode_prompt(
          _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		prompt_embeds=_lowerCamelCase							,		negative_prompt_embeds=_lowerCamelCase							,		)

      lowercase							   =	ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]

      # 4. Preprocess image
      lowercase							   =	preprocess(_lowerCamelCase	)
      lowercase							   =	image.cpu()

      # 5. set timesteps
      self.scheduler.set_timesteps(_lowerCamelCase							,		device=_lowerCamelCase	)
      lowercase							   =	self.scheduler.timesteps

      # 5. Add noise to image
      lowercase							   =	torch.tensor([noise_level]							,		dtype=torch.long							,		device=_lowerCamelCase	)
      lowercase							   =	torch.randn(image.shape							,		generator=_lowerCamelCase							,		device=_lowerCamelCase							,		dtype=_lowerCamelCase	)
      lowercase							   =	self.low_res_scheduler.add_noise(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

      lowercase							   =	2 if do_classifier_free_guidance else 1
      lowercase							   =	np.concatenate([image] * batch_multiplier * num_images_per_prompt	)
      lowercase							   =	np.concatenate([noise_level] * image.shape[0]	)

      # 6. Prepare latent variables
      lowercase   ,					lowercase							   =	image.shape[2:]
      lowercase							   =	self.prepare_latents(
          batch_size * num_images_per_prompt							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		)

      # 7. Check that sizes of image and latents match
      lowercase							   =	image.shape[1]
      if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
        raise ValueError(
            'Incorrect configuration settings! The config of `pipeline.unet` expects'
            F' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
            F' `num_channels_image`: {num_channels_image} '
            F' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
            ' `pipeline.unet` or your `image` input.'	)

      # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      lowercase							   =	self.prepare_extra_step_kwargs(_lowerCamelCase							,		_lowerCamelCase	)

      lowercase							   =	next(
          (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')							,		'tensor(float)'	)
      lowercase							   =	ORT_TO_NP_TYPE[timestep_dtype]

      # 9. Denoising loop
      lowercase							   =	len(_lowerCamelCase	) - num_inference_steps * self.scheduler.order
      with self.progress_bar(total=_lowerCamelCase	) as progress_bar:
        for i, t in enumerate(_lowerCamelCase	):
          # expand the latents if we are doing classifier free guidance
          lowercase							   =	np.concatenate([latents] * 2	) if do_classifier_free_guidance else latents

          # concat latents, mask, masked_image_latents in the channel dimension
          lowercase							   =	self.scheduler.scale_model_input(_lowerCamelCase							,		_lowerCamelCase	)
          lowercase							   =	np.concatenate([latent_model_input, image]							,		axis=1	)

          # timestep to tensor
          lowercase							   =	np.array([t]							,		dtype=_lowerCamelCase	)

          # predict the noise residual
          lowercase							   =	self.unet(
              sample=_lowerCamelCase							,		timestep=_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase							,		class_labels=noise_level.astype(np.intaa	)							,		)[0]

          # perform guidance
          if do_classifier_free_guidance:
            lowercase   ,					lowercase							   =	np.split(_lowerCamelCase							,		2	)
          lowercase							   =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          lowercase							   =	self.scheduler.step(
              torch.from_numpy(_lowerCamelCase	)							,		_lowerCamelCase							,		_lowerCamelCase							,		**_lowerCamelCase	).prev_sample

          # call the callback, if provided
          if i == len(_lowerCamelCase	) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
            progress_bar.update()
            if callback is not None and i % callback_steps == 0:
              callback(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

        # 10. Post-processing
      lowercase							   =	self.decode_latents(latents.float()	)

      # 11. Convert to PIL
      if output_type == "pil":
        lowercase							   =	self.numpy_to_pil(_lowerCamelCase	)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	1 / 0.0_8_3_3_3 * latents
      lowercase							   =	self.vae(latent_sample=_lowerCamelCase	)[0]
      lowercase							   =	np.clip(image / 2 + 0.5							,		0							,		1	)
      lowercase							   =	image.transpose((0, 2, 3, 1)	)
      return image







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		):
      if prompt is not None and isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	1
      elif prompt is not None and isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	len(_lowerCamelCase	)
      else:
        lowercase							   =	prompt_embeds.shape[0]

      if prompt_embeds is None:
        lowercase							   =	self.tokenizer(
            _lowerCamelCase							,		padding='max_length'							,		max_length=self.tokenizer.model_max_length							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)
        lowercase							   =	text_inputs.input_ids
        lowercase							   =	self.tokenizer(_lowerCamelCase							,		padding='longest'							,		return_tensors='pt'	).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
            _lowerCamelCase							,		_lowerCamelCase	):
          lowercase							   =	self.tokenizer.batch_decode(
              untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]	)
          logger.warning(
              'The following part of your input was truncated because CLIP can only handle sequences up to'
              F' {self.tokenizer.model_max_length} tokens: {removed_text}'	)

        # no positional arguments to text_encoder
        lowercase							   =	self.text_encoder(
            input_ids=text_input_ids.int().to(_lowerCamelCase	)							,		)
        lowercase							   =	prompt_embeds[0]

      lowercase   ,					lowercase   ,					lowercase							   =	prompt_embeds.shape
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      lowercase							   =	prompt_embeds.repeat(1							,		_lowerCamelCase	)
      lowercase							   =	prompt_embeds.reshape(bs_embed * num_images_per_prompt							,		_lowerCamelCase							,		-1	)

      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
        lowercase							   =	42
        if negative_prompt is None:
          lowercase							   =	[''] * batch_size
        elif type(_lowerCamelCase	) is not type(_lowerCamelCase	):
          raise TypeError(
              F'`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCamelCase	)} !='
              F' {type(_lowerCamelCase	)}.'	)
        elif isinstance(_lowerCamelCase							,		_lowerCamelCase	):
          lowercase							   =	[negative_prompt]
        elif batch_size != len(_lowerCamelCase	):
          raise ValueError(
              F'`negative_prompt`: {negative_prompt} has batch size {len(_lowerCamelCase	)}, but `prompt`:'
              F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
              ' the batch size of `prompt`.'	)
        else:
          lowercase							   =	negative_prompt

        lowercase							   =	text_input_ids.shape[-1]
        lowercase							   =	self.tokenizer(
            _lowerCamelCase							,		padding='max_length'							,		max_length=_lowerCamelCase							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)

        # if hasattr(uncond_input, "attention_mask"):
        #     attention_mask = uncond_input.attention_mask.to(device)
        # else:
        #     attention_mask = None

        lowercase							   =	self.text_encoder(
            input_ids=uncond_input.input_ids.int().to(_lowerCamelCase	)							,		)
        lowercase							   =	uncond_embeddings[0]

      if do_classifier_free_guidance:
        lowercase							   =	uncond_embeddings.shape[1]
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        lowercase							   =	uncond_embeddings.repeat(1							,		_lowerCamelCase	)
        lowercase							   =	uncond_embeddings.reshape(batch_size * num_images_per_prompt							,		_lowerCamelCase							,		-1	)

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        lowercase							   =	np.concatenate([uncond_embeddings, prompt_embeds]	)

      return prompt_embeds










"""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


