






import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       _UpperCAmelCase	,			_UpperCAmelCase							=    image.size
       _UpperCAmelCase	,			_UpperCAmelCase							=    (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
       _UpperCAmelCase							=    image.resize((w, h)   ,      resample=PIL_INTERPOLATION["""lanczos"""] )
       _UpperCAmelCase							=    np.array(__snake_case ).astype(np.floataa ) / 255.0
       _UpperCAmelCase							=    image[None].transpose(0   ,      3   ,      1   ,      2 )
       _UpperCAmelCase							=    torch.from_numpy(__snake_case )
       return 2.0 * image - 1.0





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			VQModel       ,				lowerCamelCase		:			UNetaDModel       ,				lowerCamelCase		:			Union[
          DDIMScheduler,
          PNDMScheduler,
          LMSDiscreteScheduler,
          EulerDiscreteScheduler,
          EulerAncestralDiscreteScheduler,
          DPMSolverMultistepScheduler,
      ]       ,				)						->	Any:


         """simple docstring"""
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase       ,				unet=lowerCamelCase       ,				scheduler=lowerCamelCase							)





  @torch.no_grad()
  def __call__(      self		:			Tuple       ,				lowerCamelCase		:			Union[torch.Tensor, PIL.Image.Image] = None       ,				lowerCamelCase		:			Optional[int] = 1       ,				lowerCamelCase		:			Optional[int] = 100       ,				lowerCamelCase		:			Optional[float] = 0.0       ,				lowerCamelCase		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				lowerCamelCase		:			Optional[str] = "pil"       ,				lowerCamelCase		:			bool = True       ,				)						->	Union[Tuple, ImagePipelineOutput]:


         """simple docstring"""
         if isinstance(lowerCamelCase       ,				PIL.Image.Image							):
                _UpperCAmelCase							=    1
         elif isinstance(lowerCamelCase       ,				torch.Tensor							):
                _UpperCAmelCase							=    image.shape[0]
         else:
                raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase							)}"""							)

         if isinstance(lowerCamelCase       ,				PIL.Image.Image							):
                _UpperCAmelCase							=    preprocess(lowerCamelCase							)

         _UpperCAmelCase	,			_UpperCAmelCase							=    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         _UpperCAmelCase							=    (batch_size, self.unet.config.in_channels // 2, height, width)
         _UpperCAmelCase							=    next(self.unet.parameters()							).dtype

         _UpperCAmelCase							=    randn_tensor(lowerCamelCase       ,				generator=lowerCamelCase       ,				device=self.device       ,				dtype=lowerCamelCase							)

         _UpperCAmelCase							=    image.to(device=self.device       ,				dtype=lowerCamelCase							)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase       ,				device=self.device							)
         _UpperCAmelCase							=    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         _UpperCAmelCase							=    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         _UpperCAmelCase							=    """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
         _UpperCAmelCase							=    {}
         if accepts_eta:
                _UpperCAmelCase							=    eta

         for t in self.progress_bar(lowerCamelCase							):
                # concat latents and low resolution image in the channel dimension.
                _UpperCAmelCase							=    torch.cat([latents, image]       ,				dim=1							)
                _UpperCAmelCase							=    self.scheduler.scale_model_input(lowerCamelCase       ,				lowerCamelCase							)
                # predict the noise residual
                _UpperCAmelCase							=    self.unet(lowerCamelCase       ,				lowerCamelCase							).sample
                # compute the previous noisy sample x_t -> x_t-1
                _UpperCAmelCase							=    self.scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample

         # decode the image latents with the VQVAE
         _UpperCAmelCase							=    self.vqvae.decode(lowerCamelCase							).sample
         _UpperCAmelCase							=    torch.clamp(lowerCamelCase       ,				-1.0       ,				1.0							)
         _UpperCAmelCase							=    image / 2 + 0.5
         _UpperCAmelCase							=    image.cpu().permute(0       ,				2       ,				3       ,				1							).numpy()

         if output_type == "pil":
                _UpperCAmelCase							=    self.numpy_to_pil(lowerCamelCase							)

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=lowerCamelCase							)







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a:     Any   					=					logging.get_logger(__name__)


__a:     Dict   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     int   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     str   					=					{'''facebook/blenderbot_small-90M''': 512}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    set()
       _UpperCAmelCase							=    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCAmelCase							=    char

       _UpperCAmelCase							=    set(__snake_case )
       return pairs





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







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


  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[str]="__start__"       ,				lowerCamelCase		:			List[Any]="__end__"       ,				lowerCamelCase		:			Any="__unk__"       ,				lowerCamelCase		:			Optional[Any]="__null__"       ,				**lowerCamelCase		:			Optional[Any]       ,				)						->	Tuple:


         """simple docstring"""
         super().__init__(unk_token=lowerCamelCase       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				pad_token=lowerCamelCase       ,				**lowerCamelCase							)

         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as vocab_handle:
                _UpperCAmelCase							=    json.load(lowerCamelCase							)
         _UpperCAmelCase							=    {v: k for k, v in self.encoder.items()}
         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as merges_handle:
                _UpperCAmelCase							=    merges_handle.read().split("""\n"""							)[1:-1]
         _UpperCAmelCase							=    [tuple(merge.split()							) for merge in merges]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    {}


  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


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


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Dict:


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


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""
         if token in self.cache:
                return self.cache[token]
         _UpperCAmelCase							=    re.sub("""([.,!?()])"""       ,				r""" \1"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub("""(')"""       ,				r""" \1 """       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub(r"""\s{2,}"""       ,				""" """       ,				lowerCamelCase							)
         if "\n" in token:
                _UpperCAmelCase							=    token.replace("""\n"""       ,				""" __newln__"""							)

         _UpperCAmelCase							=    token.split(""" """							)
         _UpperCAmelCase							=    []
         for token in tokens:
                if not len(lowerCamelCase							):
                       continue

                _UpperCAmelCase							=    token.lower()
                _UpperCAmelCase							=    tuple(lowerCamelCase							)
                _UpperCAmelCase							=    tuple(list(word[:-1]							) + [word[-1] + """</w>"""]							)
                _UpperCAmelCase							=    get_pairs(lowerCamelCase							)

                if not pairs:
                       words.append(lowerCamelCase							)
                       continue

                while True:
                       _UpperCAmelCase							=    min(lowerCamelCase       ,				key=lambda lowerCamelCase							: self.bpe_ranks.get(lowerCamelCase       ,				float("""inf"""							)							)							)
                       if bigram not in self.bpe_ranks:
                              break
                       _UpperCAmelCase	,			_UpperCAmelCase							=    bigram
                       _UpperCAmelCase							=    []
                       _UpperCAmelCase							=    0

                       while i < len(lowerCamelCase							):
                              try:
                                     _UpperCAmelCase							=    word.index(lowerCamelCase       ,				lowerCamelCase							)
                                     new_word.extend(word[i:j]							)
                                     _UpperCAmelCase							=    j
                              except ValueError:
                                     new_word.extend(word[i:]							)
                                     break

                              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
                       _UpperCAmelCase							=    tuple(lowerCamelCase							)
                       _UpperCAmelCase							=    new_word
                       if len(lowerCamelCase							) == 1:
                              break
                       else:
                              _UpperCAmelCase							=    get_pairs(lowerCamelCase							)
                _UpperCAmelCase							=    """@@ """.join(lowerCamelCase							)
                _UpperCAmelCase							=    word[:-4]

                _UpperCAmelCase							=    word
                words.append(lowerCamelCase							)
         return " ".join(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			str							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    []

         _UpperCAmelCase							=    re.findall(r"""\S+\n?"""       ,				lowerCamelCase							)

         for token in words:
                split_tokens.extend(list(self.bpe(lowerCamelCase							).split(""" """							)							)							)
         return split_tokens


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    token.lower()
         return self.encoder.get(lowerCamelCase       ,				self.encoder.get(self.unk_token							)							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int							)						->	str:


         """simple docstring"""
         return self.decoder.get(lowerCamelCase       ,				self.unk_token							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    """ """.join(lowerCamelCase							).replace("""@@ """       ,				""""""							).strip()
         return out_string





  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[str] = None							)						->	Tuple[str]:


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

         _UpperCAmelCase							=    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!"""							)
                              _UpperCAmelCase							=    token_index
                       writer.write(""" """.join(lowerCamelCase							) + """\n"""							)
                       index += 1

         return vocab_file, merge_file