




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

import regex as re

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


_UpperCAmelCase		:  Optional[int]	=   logging.get_logger(__name__)


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

_UpperCAmelCase		:  Optional[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"
        )
    },
}

_UpperCAmelCase		:  Optional[Any]	=   {"facebook/blenderbot_small-90M": 512}



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Optional[Any]							=  set()
  lowercase			:Any							=  word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)      )
    lowercase			:str							=  char

  lowercase			:List[Any]							=  set(_SCREAMING_SNAKE_CASE      )
  return pairs







class       __lowerCAmelCase				(							lowerCAmelCase_):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a           =  ["input_ids", "attention_mask"]






       def __init__(      self:    Any				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Dict="__start__"				,    _lowerCAmelCase:    int="__end__"				,    _lowerCAmelCase:    List[str]="__unk__"				,    _lowerCAmelCase:    int="__null__"				,    **_lowerCAmelCase:    List[str]				,    ):
         super().__init__(unk_token=lowerCamelCase__				,    bos_token=lowerCamelCase__				,    eos_token=lowerCamelCase__				,    pad_token=lowerCamelCase__				,    **lowerCamelCase__ )

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






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return len(self.encoder )






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






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str ):


         if token in self.cache:
           return self.cache[token]
         lowercase			:Any							=  re.sub("([.,!?()])"				,    r" \1"				,    lowerCamelCase__ )
         lowercase			:Dict							=  re.sub("(')"				,    r" \1 "				,    lowerCamelCase__ )
         lowercase			:Optional[int]							=  re.sub(r"\s{2,}"				,    " "				,    lowerCamelCase__ )
         if "\n" in token:
           lowercase			:Dict							=  token.replace("\n"				,    " __newln__" )

         lowercase			:int							=  token.split(" " )
         lowercase			:Dict							=  []
         for token in tokens:
           if not len(lowerCamelCase__ ):
             continue

           lowercase			:int							=  token.lower()
           lowercase			:Tuple							=  tuple(lowerCamelCase__ )
           lowercase			:Any							=  tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
           lowercase			:Tuple							=  get_pairs(lowerCamelCase__ )

           if not pairs:
             words.append(lowerCamelCase__ )
             continue

           while True:
             lowercase			:Dict							=  min(lowerCamelCase__				,    key=lambda _lowerCAmelCase : self.bpe_ranks.get(lowerCamelCase__				,    float("inf" ) ) )
             if bigram not in self.bpe_ranks:
               break
             lowercase		,			lowercase			:Union[str, Any]							=  bigram
             lowercase			:Union[str, Any]							=  []
             lowercase			:Dict							=  0

             while i < len(lowerCamelCase__ ):
               try:
                 lowercase			:Dict							=  word.index(lowerCamelCase__				,    lowerCamelCase__ )
                 new_word.extend(word[i:j] )
                 lowercase			:Union[str, Any]							=  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
             lowercase			:List[str]							=  tuple(lowerCamelCase__ )
             lowercase			:Tuple							=  new_word
             if len(lowerCamelCase__ ) == 1:
               break
             else:
               lowercase			:Tuple							=  get_pairs(lowerCamelCase__ )
           lowercase			:Union[str, Any]							=  "@@ ".join(lowerCamelCase__ )
           lowercase			:List[str]							=  word[:-4]

           lowercase			:Optional[int]							=  word
           words.append(lowerCamelCase__ )
         return " ".join(lowerCamelCase__ )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str ):
         lowercase			:Union[str, Any]							=  []

         lowercase			:Optional[int]							=  re.findall(r"\S+\n?"				,    lowerCamelCase__ )

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






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    str ):
         lowercase			:Union[str, Any]							=  token.lower()
         return self.encoder.get(lowerCamelCase__				,    self.encoder.get(self.unk_token ) )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    int ):
         return self.decoder.get(lowerCamelCase__				,    self.unk_token )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    List[str] ):
         lowercase			:Union[str, Any]							=  " ".join(lowerCamelCase__ ).replace("@@ "				,    "" ).strip()
         return out_string






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):


         if not os.path.isdir(lowerCamelCase__ ):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
           return
         lowercase			:Dict							=  os.path.join(
             lowerCamelCase__				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
         lowercase			:int							=  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			:str							=  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			:Optional[Any]							=  token_index
             writer.write(" ".join(lowerCamelCase__ ) + "\n" )
             index += 1

         return vocab_file, merge_file










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=3						,out_channels=3						,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,)
           return model






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""						,return_dict=lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE		=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

@slow
@require_torch
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=20						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


