
"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__   :					Optional[Any]							=   logging.get_logger(__name__)


A__   :					List[Any]							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
A__   :					List[str]							=   {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

A__   :					Dict							=   {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}




@lru_cache()
def  _lowerCAmelCase		(		):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     set()
  _lowercase:			Dict       =     word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)						)
    _lowercase:			Tuple       =     char
  return pairs







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       def __init__(     self ,				A_ ,				A_ ,				A_="replace" ,				A_="<s>" ,				A_="</s>" ,				A_="</s>" ,				A_="<s>" ,				A_="<unk>" ,				A_="<pad>" ,				A_="<mask>" ,				A_=False ,				**A_ ,				)     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else bos_token
         _lowercase:			Optional[Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else eos_token
         _lowercase:			List[Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else sep_token
         _lowercase:			List[str]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else cls_token
         _lowercase:			Tuple       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else unk_token
         _lowercase:			List[str]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         _lowercase:			Tuple       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else mask_token

         super().__init__(
             errors=A_ ,				bos_token=A_ ,				eos_token=A_ ,				unk_token=A_ ,				sep_token=A_ ,				cls_token=A_ ,				pad_token=A_ ,				mask_token=A_ ,				add_prefix_space=A_ ,				**A_ ,				)

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

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         _lowercase:			List[str]       =     re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )
       @property
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         return len(self.encoder       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         return dict(self.encoder ,				**self.added_tokens_encoder       )
       def 							lowercase_							(     self ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         if token in self.cache:
           return self.cache[token]
         _lowercase:			int       =     tuple(A_       )
         _lowercase:			Union[str, Any]       =     get_pairs(A_       )

         if not pairs:
           return token

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

             if word[i] == first and i < len(A_       ) - 1 and word[i + 1] == second:
               new_word.append(first + second       )
               i += 2
             else:
               new_word.append(word[i]       )
               i += 1
           _lowercase:			Tuple       =     tuple(A_       )
           _lowercase:			Dict       =     new_word
           if len(A_       ) == 1:
             break
           else:
             _lowercase:			Optional[int]       =     get_pairs(A_       )
         _lowercase:			Optional[Any]       =     ''' '''.join(A_       )
         _lowercase:			str       =     word
         return word
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         _lowercase:			int       =     []
         for token in re.findall(self.pat ,				A_       ):
           _lowercase:			Tuple       =     ''''''.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(A_       ).split(''' '''       )       )
         return bpe_tokens
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         return self.encoder.get(A_ ,				self.encoder.get(self.unk_token       )       )
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         return self.decoder.get(A_       )
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Tuple       =     ''''''.join(A_       )
         _lowercase:			Optional[Any]       =     bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8''' ,				errors=self.errors       )
         return text
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




         if not os.path.isdir(A_       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
         _lowercase:			Dict       =     os.path.join(
             A_ ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         _lowercase:			Optional[int]       =     os.path.join(
             A_ ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

         _lowercase:			List[Any]       =     0
         with open(A_ ,				'''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 A_       : 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:			List[str]       =     token_index
             writer.write(''' '''.join(A_       ) + '''\n'''       )
             index += 1

         return vocab_file, merge_file
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowercase:			Union[str, Any]       =     [self.cls_token_id]
         _lowercase:			Union[str, Any]       =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = False       )     ->	List[int]:





         """simple docstring"""




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

         if token_ids_a is None:
           return [1] + ([0] * len(A_       )) + [1]
         return [1] + ([0] * len(A_       )) + [1, 1] + ([0] * len(A_       )) + [1]
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			List[str]       =     [self.sep_token_id]
         _lowercase:			Union[str, Any]       =     [self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]



       def 							lowercase_							(     self ,				A_ ,				A_=False ,				**A_       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			int       =     kwargs.pop('''add_prefix_space''' ,				self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(A_       ) > 0 and not text[0].isspace()):
           _lowercase:			Dict       =     ''' ''' + text
         return (text, kwargs)


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
