


'''simple docstring'''



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__						:   Tuple				=    logging.get_logger(__name__)


A__						:   int				=    {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

A__						:   List[Any]				=    {
    """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__						:   Tuple				=    {"""facebook/blenderbot_small-90M""": 512}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						List[Any]:
     __lowerCamelCase  :      Dict														=						set()
     __lowerCamelCase  :      Optional[int]														=						word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          __lowerCamelCase  :      Optional[Any]														=						char

     __lowerCamelCase  :      str														=						set(UpperCAmelCase_				)
     return pairs





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     VOCAB_FILES_NAMES
    lowerCamelCase    :   Union[str, Any]					=     PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase    :   Any					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase    :   List[Any]					=     ['input_ids', 'attention_mask']







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_="__start__"     ,	SCREAMING_SNAKE_CASE_="__end__"     ,	SCREAMING_SNAKE_CASE_="__unk__"     ,	SCREAMING_SNAKE_CASE_="__null__"     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	int:
         super().__init__(unk_token=SCREAMING_SNAKE_CASE_     ,	bos_token=SCREAMING_SNAKE_CASE_     ,	eos_token=SCREAMING_SNAKE_CASE_     ,	pad_token=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         with open(SCREAMING_SNAKE_CASE_     ,	encoding='utf-8'      ) as vocab_handle:
              __lowerCamelCase  :      Tuple														=						json.load(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						{v: k for k, v in self.encoder.items()}
         with open(SCREAMING_SNAKE_CASE_     ,	encoding='utf-8'      ) as merges_handle:
              __lowerCamelCase  :      Dict														=						merges_handle.read().split('\n'      )[1:-1]
         __lowerCamelCase  :      int														=						[tuple(merge.split()      ) for merge in merges]
         __lowerCamelCase  :      Optional[int]														=						dict(zip(SCREAMING_SNAKE_CASE_     ,	range(len(SCREAMING_SNAKE_CASE_      )      )      )      )
         __lowerCamelCase  :      Any														=						{}







    @property
    def  lowercase_							(				self      )							->	int:
         return len(self.encoder      )







    def  lowercase_							(				self      )							->	Dict:
         return dict(self.encoder     ,	**self.added_tokens_encoder      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if token in self.cache:
              return self.cache[token]
         __lowerCamelCase  :      Dict														=						re.sub('([.,!?()])'     ,	r' \1'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						re.sub('(\')'     ,	r' \1 '     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						re.sub(r'\s{2,}'     ,	' '     ,	SCREAMING_SNAKE_CASE_      )
         if "\n" in token:
              __lowerCamelCase  :      List[str]														=						token.replace('\n'     ,	' __newln__'      )

         __lowerCamelCase  :      Dict														=						token.split(' '      )
         __lowerCamelCase  :      List[str]														=						[]
         for token in tokens:
              if not len(SCREAMING_SNAKE_CASE_      ):
                   continue

              __lowerCamelCase  :      Optional[int]														=						token.lower()
              __lowerCamelCase  :      List[str]														=						tuple(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      str														=						tuple(list(word[:-1]      ) + [word[-1] + '</w>']      )
              __lowerCamelCase  :      List[Any]														=						get_pairs(SCREAMING_SNAKE_CASE_      )

              if not pairs:
                   words.append(SCREAMING_SNAKE_CASE_      )
                   continue

              while True:
                   __lowerCamelCase  :      List[Any]														=						min(SCREAMING_SNAKE_CASE_     ,	key=lambda SCREAMING_SNAKE_CASE_      : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_     ,	float('inf'      )      )      )
                   if bigram not in self.bpe_ranks:
                        break
                   __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						bigram
                   __lowerCamelCase  :      Tuple														=						[]
                   __lowerCamelCase  :      Dict														=						0

                   while i < len(SCREAMING_SNAKE_CASE_      ):
                        try:
                             __lowerCamelCase  :      Any														=						word.index(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                             new_word.extend(word[i:j]      )
                             __lowerCamelCase  :      Optional[Any]														=						j
                        except ValueError:
                             new_word.extend(word[i:]      )
                             break

                        if word[i] == first and i < len(SCREAMING_SNAKE_CASE_      ) - 1 and word[i + 1] == second:
                             new_word.append(first + second      )
                             i += 2
                        else:
                             new_word.append(word[i]      )
                             i += 1
                   __lowerCamelCase  :      str														=						tuple(SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      int														=						new_word
                   if len(SCREAMING_SNAKE_CASE_      ) == 1:
                        break
                   else:
                        __lowerCamelCase  :      List[str]														=						get_pairs(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Tuple														=						'@@ '.join(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Any														=						word[:-4]

              __lowerCamelCase  :      Union[str, Any]														=						word
              words.append(SCREAMING_SNAKE_CASE_      )
         return " ".join(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      Optional[Any]														=						[]

         __lowerCamelCase  :      List[str]														=						re.findall(r'\S+\n?'     ,	SCREAMING_SNAKE_CASE_      )

         for token in words:
              split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_      ).split(' '      )      )      )
         return split_tokens







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      List[str]														=						token.lower()
         return self.encoder.get(SCREAMING_SNAKE_CASE_     ,	self.encoder.get(self.unk_token      )      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         return self.decoder.get(SCREAMING_SNAKE_CASE_     ,	self.unk_token      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Tuple														=						' '.join(SCREAMING_SNAKE_CASE_      ).replace('@@ '     ,	''      ).strip()
         return out_string







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
              return
         __lowerCamelCase  :      Dict														=						os.path.join(
             SCREAMING_SNAKE_CASE_     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
         __lowerCamelCase  :      Dict														=						os.path.join(
             SCREAMING_SNAKE_CASE_     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

         __lowerCamelCase  :      int														=						0
         with open(SCREAMING_SNAKE_CASE_     ,	'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 SCREAMING_SNAKE_CASE_      : 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  :      int														=						token_index
                   writer.write(' '.join(SCREAMING_SNAKE_CASE_      ) + '\n'      )
                   index += 1

         return vocab_file, merge_file












"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

