




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


SCREAMING_SNAKE_CASE__       :      Dict  =   logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   {
    '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',
    },
}

SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}

@lru_cache()
def      A      (      )	-> Union[str, Any]:
       lowerCamelCase   :      Any			 =					(
           list(range(ord("!"    ) ,ord("~"    ) + 1    )    ) + list(range(ord("¡"    ) ,ord("¬"    ) + 1    )    ) + list(range(ord("®"    ) ,ord("ÿ"    ) + 1    )    )
       )
       lowerCamelCase   :      str			 =					bs[:]
       lowerCamelCase   :      Dict			 =					0
       for b in range(2**8    ):
              if b not in bs:
                     bs.append(_SCREAMING_SNAKE_CASE    )
                     cs.append(2**8 + n    )
                     n += 1
       lowerCamelCase   :      Any			 =					[chr(_SCREAMING_SNAKE_CASE    ) for n in cs]
       return dict(zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )    )

def      A      (      _SCREAMING_SNAKE_CASE    )	-> List[str]:
       lowerCamelCase   :      Tuple			 =					set()
       lowerCamelCase   :      List[Any]			 =					word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)    )
              lowerCamelCase   :      Optional[int]			 =					char
       return pairs




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	List[Any]	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	str	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	Optional[Any]	     =      ["""input_ids""", """attention_mask"""]







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__="replace"     ,  UpperCamelCase__="<s>"     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="<s>"     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__="<pad>"     ,  UpperCamelCase__="<mask>"     ,  UpperCamelCase__=False     ,  **UpperCamelCase__     ,  )     ->      List[str]:
           lowerCamelCase   :      List[Any]			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else bos_token
           lowerCamelCase   :      Optional[int]			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else eos_token
           lowerCamelCase   :      Union[str, Any]			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else sep_token
           lowerCamelCase   :      Union[str, Any]			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else cls_token
           lowerCamelCase   :      Dict			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else unk_token
           lowerCamelCase   :      str			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else pad_token

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

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

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

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           lowerCamelCase   :      List[Any]			 =					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     )     ->      List[str]:
           return len(self.encoder     )







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







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      str:
           if token in self.cache:
                  return self.cache[token]
           lowerCamelCase   :      Any			 =					tuple(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					get_pairs(UpperCamelCase__     )

           if not pairs:
                  return token

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

                         if word[i] == first and i < len(UpperCamelCase__     ) - 1 and word[i + 1] == second:
                                new_word.append(first + second     )
                                i += 2
                         else:
                                new_word.append(word[i]     )
                                i += 1
                  lowerCamelCase   :      Optional[int]			 =					tuple(UpperCamelCase__     )
                  lowerCamelCase   :      str			 =					new_word
                  if len(UpperCamelCase__     ) == 1:
                         break
                  else:
                         lowerCamelCase   :      List[str]			 =					get_pairs(UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					" ".join(UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					word
           return word







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      str:
           lowerCamelCase   :      Tuple			 =					[]
           for token in re.findall(self.pat     ,  UpperCamelCase__     ):
                  lowerCamelCase   :      str			 =					"".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(UpperCamelCase__     ).split(" "     )     )
           return bpe_tokens







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           return self.encoder.get(UpperCamelCase__     ,  self.encoder.get(self.unk_token     )     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           return self.decoder.get(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      str			 =					"".join(UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					bytearray([self.byte_decoder[c] for c in text]     ).decode("utf-8"     ,  errors=self.errors     )
           return text







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      Optional[int]			 =					os.path.join(
               UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
           lowerCamelCase   :      Optional[int]			 =					os.path.join(
               UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

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

           lowerCamelCase   :      Optional[int]			 =					0
           with open(UpperCamelCase__     ,  "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 UpperCamelCase__     : 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   :      Any			 =					token_index
                         writer.write(" ".join(UpperCamelCase__     ) + "\n"     )
                         index += 1

           return vocab_file, merge_file







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           lowerCamelCase   :      List[Any]			 =					[self.cls_token_id]
           lowerCamelCase   :      Dict			 =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     )     ->      List[int]:
           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCamelCase__     ,  token_ids_a=UpperCamelCase__     ,  already_has_special_tokens=UpperCamelCase__     )

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      Optional[Any]			 =					[self.sep_token_id]
           lowerCamelCase   :      Tuple			 =					[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     ,  UpperCamelCase__     ,  UpperCamelCase__=False     ,  **UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					kwargs.pop("add_prefix_space"     ,  self.add_prefix_space     )
           if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__     ) > 0 and not text[0].isspace()):
                  lowerCamelCase   :      Tuple			 =					" " + text
           return (text, kwargs)







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						(KDPMaDiscreteScheduler,)
			__a  : Dict			 =						10





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__      ,   beta_end=lowerCAmelCase__						)





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

							'''simple docstring'''


							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCAmelCase__						)





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

							'''simple docstring'''


							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0002						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

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

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if str(lowerCAmelCase__						).startswith('''cpu'''						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3