


'''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


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase__      =							{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

UpperCAmelCase__      =							{
    """roberta-base""": 5_1_2,
    """roberta-large""": 5_1_2,
    """roberta-large-mnli""": 5_1_2,
    """distilroberta-base""": 5_1_2,
    """roberta-base-openai-detector""": 5_1_2,
    """roberta-large-openai-detector""": 5_1_2,
}






@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(_SCREAMING_SNAKE_CASE   )
									cs.append(2**8 + n   )
									n += 1
			__A=  [chr(_SCREAMING_SNAKE_CASE   ) for n in cs]
			return dict(zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )   )






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int   ):






			"""simple docstring"""




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






class 	a__				(				a__      ):





			'''simple docstring'''

			A	:							Any							= VOCAB_FILES_NAMES
			A	:							str							= PRETRAINED_VOCAB_FILES_MAP
			A	:							List[str]							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A	:							Union[str, Any]							= ['''input_ids''', '''attention_mask''']
			def __init__(     self       : List[str] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Union[str, Any]="replace" ,						lowerCAmelCase_       : Optional[Any]="<s>" ,						lowerCAmelCase_       : Optional[int]="</s>" ,						lowerCAmelCase_       : List[str]="</s>" ,						lowerCAmelCase_       : List[Any]="<s>" ,						lowerCAmelCase_       : Any="<unk>" ,						lowerCAmelCase_       : int="<pad>" ,						lowerCAmelCase_       : Any="<mask>" ,						lowerCAmelCase_       : str=False ,						**lowerCAmelCase_       : Optional[Any] ,						)			->		List[Any]:
						__A=  AddedToken(__A ,						lstrip=__A ,						rstrip=__A  ) if isinstance(__A ,						__A  ) else bos_token
						__A=  AddedToken(__A ,						lstrip=__A ,						rstrip=__A  ) if isinstance(__A ,						__A  ) else eos_token
						__A=  AddedToken(__A ,						lstrip=__A ,						rstrip=__A  ) if isinstance(__A ,						__A  ) else sep_token
						__A=  AddedToken(__A ,						lstrip=__A ,						rstrip=__A  ) if isinstance(__A ,						__A  ) else cls_token
						__A=  AddedToken(__A ,						lstrip=__A ,						rstrip=__A  ) if isinstance(__A ,						__A  ) else unk_token
						__A=  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
						__A=  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:
									__A=  json.load(__A  )
						__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(__A ,						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(__A ,						range(len(__A  )  )  )  )
						__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 				lowerCAmelCase    (     self       : Dict  )			->		Optional[Any]:
						return len(self.encoder  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		Tuple:
						return dict(self.encoder ,						**self.added_tokens_encoder  )
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Optional[Any]  )			->		List[Any]:







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

						if not pairs:
									return token

						while True:
									__A=  min(__A ,						key=lambda lowerCAmelCase_  : self.bpe_ranks.get(__A ,						float('inf'  )  )  )
									if bigram not in self.bpe_ranks:
												break
									__A=  bigram
									__A=  []
									__A=  0
									while i < len(__A  ):
												try:
															__A=  word.index(__A ,						__A  )
												except ValueError:
															new_word.extend(word[i:]  )
															break
												else:
															new_word.extend(word[i:j]  )
															__A=  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
									__A=  tuple(__A  )
									__A=  new_word
									if len(__A  ) == 1:
												break
									else:
												__A=  get_pairs(__A  )
						__A=  " ".join(__A  )
						__A=  word
						return word
			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Tuple  )			->		List[str]:
						__A=  []
						for token in re.findall(self.pat ,						__A  ):
									__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(__A  ).split(' '  )  )
						return bpe_tokens
			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : List[str]  )			->		List[str]:
						return self.encoder.get(__A ,						self.encoder.get(self.unk_token  )  )
			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : Dict  )			->		Any:
						return self.decoder.get(__A  )
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Tuple  )			->		Union[str, Any]:
						__A=  "".join(__A  )
						__A=  bytearray([self.byte_decoder[c] for c in text]  ).decode('utf-8' ,						errors=self.errors  )
						return text
			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[str] = None  )			->		Tuple[str]:







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

						__A=  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 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!'  )
															__A=  token_index
												writer.write(' '.join(__A  ) + '\n'  )
												index += 1

						return vocab_file, merge_file
			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[Any] = None  )			->		List[int]:







						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						__A=  [self.cls_token_id]
						__A=  [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[str] = None ,						lowerCAmelCase_       : str = False  )			->		List[int]:







						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 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Dict = None  )			->		List[int]:
						__A=  [self.sep_token_id]
						__A=  [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 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Optional[Any]=False ,						**lowerCAmelCase_       : Tuple  )			->		Optional[Any]:
						__A=  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()):
									__A=  " " + text
						return (text, kwargs)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  hidden_act
						__A=  intermediate_size
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  initializer_range
						__A=  layer_norm_eps
						__A=  embedding_size
						__A=  trigram_input
						__A=  use_bottleneck
						__A=  intra_bottleneck_size
						__A=  use_bottleneck_attention
						__A=  key_query_shared_bottleneck
						__A=  num_feedforward_networks
						__A=  normalization_type
						__A=  classifier_activation

						if self.use_bottleneck:
									__A=  intra_bottleneck_size
						else:
									__A=  hidden_size

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

			@property
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]  )



