

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

lowerCamelCase 	=   logging.get_logger(__name__)

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

lowerCamelCase 	=   {
    '''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''',
    },
}

lowerCamelCase 	=   {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}




@lru_cache()
def      UpperCAmelCase__							(     ):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					List[str]				):





  '''simple docstring'''







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







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  VOCAB_FILES_NAMES
   lowerCamelCase__					:      Union[str, Any]        =  PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__					:      Optional[int]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__					:      str        =  ['input_ids', 'attention_mask']



   def __init__(							self,       lowercase_,       lowercase_,       lowercase_="replace",       lowercase_="<|endoftext|>",       lowercase_="<|endoftext|>",       lowercase_="<|endoftext|>",       lowercase_=None,       lowercase_=False,       lowercase_=False,       **lowercase_,       )				->       Any:






     """simple docstring"""






     a__    =AddedToken(lowercase_,       lstrip=lowercase_,       rstrip=lowercase_			) if isinstance(lowercase_,       lowercase_			) else bos_token
     a__    =AddedToken(lowercase_,       lstrip=lowercase_,       rstrip=lowercase_			) if isinstance(lowercase_,       lowercase_			) else eos_token
     a__    =AddedToken(lowercase_,       lstrip=lowercase_,       rstrip=lowercase_			) if isinstance(lowercase_,       lowercase_			) else unk_token
     a__    =AddedToken(lowercase_,       lstrip=lowercase_,       rstrip=lowercase_			) if isinstance(lowercase_,       lowercase_			) else pad_token
     super().__init__(
         errors=lowercase_,       unk_token=lowercase_,       bos_token=lowercase_,       eos_token=lowercase_,       pad_token=lowercase_,       add_prefix_space=lowercase_,       add_bos_token=lowercase_,       **lowercase_,       )
     a__    =add_bos_token

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

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     a__    =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			)				->       Any:






     """simple docstring"""






     return len(self.encoder			)



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     return dict(self.encoder,       **self.added_tokens_encoder			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     if token in self.cache:
       return self.cache[token]
     a__    =tuple(lowercase_			)
     a__    =get_pairs(lowercase_			)

     if not pairs:
       return token

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

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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_=None			)				->       Dict:






     """simple docstring"""






     if self.add_bos_token:
       a__    =[self.bos_token_id]
     else:
       a__    =[]

     a__    =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,       lowercase_,       lowercase_ = None,       lowercase_ = False			)				->       List[int]:






     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowercase_,       token_ids_a=lowercase_,       already_has_special_tokens=lowercase_			)

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

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



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






     a__    =[]
     for token in re.findall(self.pat,       lowercase_			):
       a__    =''''''.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(lowercase_			).split(''' '''			)			)
     return bpe_tokens



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     return self.encoder.get(lowercase_,       self.encoder.get(self.unk_token			)			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     return self.decoder.get(lowercase_			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






     a__    =''''''.join(lowercase_			)
     a__    =bytearray([self.byte_decoder[c] for c in text]			).decode('''utf-8''',       errors=self.errors			)
     return text



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None			)				->       Tuple[str]:






     """simple docstring"""






     if not os.path.isdir(lowercase_			):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
       return
     a__    =os.path.join(
         lowercase_,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
     a__    =os.path.join(
         lowercase_,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

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

     a__    =0
     with open(lowercase_,       '''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 lowercase_			: 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!'''			)
           a__    =token_index
         writer.write(''' '''.join(lowercase_			) + '''\n'''			)
         index += 1

     return vocab_file, merge_file



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_=False,       **lowercase_			)				->       Optional[int]:






     """simple docstring"""






     a__    =kwargs.pop('''add_prefix_space''',       self.add_prefix_space			)
     if is_split_into_words or add_prefix_space:
       a__    =''' ''' + text
     return (text, kwargs)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[int]:






     """simple docstring"""






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



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
