

'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase__											=	logging.getLogger(__name__)






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str
  SCREAMING_SNAKE_CASE    :  List[str]
  SCREAMING_SNAKE_CASE    :  Optional[List[str]]






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[int]
  SCREAMING_SNAKE_CASE    :  List[int]
  SCREAMING_SNAKE_CASE    :  Optional[List[int]]							=			None
  SCREAMING_SNAKE_CASE    :  Optional[List[int]]							=			None






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			'train'
  SCREAMING_SNAKE_CASE    :  int							=			'dev'
  SCREAMING_SNAKE_CASE    :  List[str]							=			'test'






class 			lowercase_	:




  """simple docstring"""




  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     Optional[Any]					,lowercase__   :     Union[Split, str]			):
   raise NotImplementedError






  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     str			):
   raise NotImplementedError






  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     List[InputExample]					,lowercase__   :     List[str]					,lowercase__   :     int					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int=False					,lowercase__   :     List[Any]="[CLS]"					,lowercase__   :     int=1					,lowercase__   :     Optional[int]="[SEP]"					,lowercase__   :     int=False					,lowercase__   :     Any=False					,lowercase__   :     Optional[Any]=0					,lowercase__   :     Any=0					,lowercase__   :     Optional[int]=-1_0_0					,lowercase__   :     str=0					,lowercase__   :     Optional[int]=True					,):
   __lowercase							  = {label: i for i, label in enumerate(lowercase__			)}

   __lowercase							  = []
   for ex_index, example in enumerate(lowercase__			):
    if ex_index % 1_0_0_0_0 == 0:
     logger.info('''Writing example %d of %d'''					,lowercase__					,len(lowercase__			)			)

    __lowercase							  = []
    __lowercase							  = []
    for word, label in zip(example.words					,example.labels			):
     __lowercase							  = tokenizer.tokenize(lowercase__			)

     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
     if len(lowercase__			) > 0:
      tokens.extend(lowercase__			)
      # Use the real label id for the first token of the word, and padding ids for the remaining tokens
      label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase__			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
    __lowercase							  = tokenizer.num_special_tokens_to_add()
    if len(lowercase__			) > max_seq_length - special_tokens_count:
     __lowercase							  = tokens[: (max_seq_length - special_tokens_count)]
     __lowercase							  = label_ids[: (max_seq_length - special_tokens_count)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids:   0   0   0   0  0     0   0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens += [sep_token]
    label_ids += [pad_token_label_id]
    if sep_token_extra:
     # roberta uses an extra separator b/w pairs of sentences
     tokens += [sep_token]
     label_ids += [pad_token_label_id]
    __lowercase							  = [sequence_a_segment_id] * len(lowercase__			)

    if cls_token_at_end:
     tokens += [cls_token]
     label_ids += [pad_token_label_id]
     segment_ids += [cls_token_segment_id]
    else:
     __lowercase							  = [cls_token] + tokens
     __lowercase							  = [pad_token_label_id] + label_ids
     __lowercase							  = [cls_token_segment_id] + segment_ids

    __lowercase							  = tokenizer.convert_tokens_to_ids(lowercase__			)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    __lowercase							  = [1 if mask_padding_with_zero else 0] * len(lowercase__			)

    # Zero-pad up to the sequence length.
    __lowercase							  = max_seq_length - len(lowercase__			)
    if pad_on_left:
     __lowercase							  = ([pad_token] * padding_length) + input_ids
     __lowercase							  = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
     __lowercase							  = ([pad_token_segment_id] * padding_length) + segment_ids
     __lowercase							  = ([pad_token_label_id] * padding_length) + label_ids
    else:
     input_ids += [pad_token] * padding_length
     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
     segment_ids += [pad_token_segment_id] * padding_length
     label_ids += [pad_token_label_id] * padding_length

    assert len(lowercase__			) == max_seq_length
    assert len(lowercase__			) == max_seq_length
    assert len(lowercase__			) == max_seq_length
    assert len(lowercase__			) == max_seq_length

    if ex_index < 5:
     logger.info('''*** Example ***'''			)
     logger.info('''guid: %s'''					,example.guid			)
     logger.info('''tokens: %s'''					,''' '''.join([str(lowercase__			) for x in tokens]			)			)
     logger.info('''input_ids: %s'''					,''' '''.join([str(lowercase__			) for x in input_ids]			)			)
     logger.info('''input_mask: %s'''					,''' '''.join([str(lowercase__			) for x in input_mask]			)			)
     logger.info('''segment_ids: %s'''					,''' '''.join([str(lowercase__			) for x in segment_ids]			)			)
     logger.info('''label_ids: %s'''					,''' '''.join([str(lowercase__			) for x in label_ids]			)			)

    if "token_type_ids" not in tokenizer.model_input_names:
     __lowercase							  = None

    features.append(
        InputFeatures(
            input_ids=lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,label_ids=lowercase__			)			)
   return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset






 class 			lowercase_	(lowerCamelCase__      ):




   """simple docstring"""




   SCREAMING_SNAKE_CASE    :  List[InputFeatures]
   SCREAMING_SNAKE_CASE    :  int							=			nn.CrossEntropyLoss().ignore_index






   def __init__(      self   :     List[str]					,lowercase__   :     TokenClassificationTask					,lowercase__   :     str					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     List[str]					,lowercase__   :     str					,lowercase__   :     Optional[int] = None					,lowercase__   :     Any=False					,lowercase__   :     Split = Split.train					,):
    # Load data features from cache or dataset file
    __lowercase							  = os.path.join(
        lowercase__					,'''cached_{}_{}_{}'''.format(mode.value					,tokenizer.__class__.__name__					,str(lowercase__			)			)					,)

    # Make sure only the first process in distributed training processes the dataset,
    # and the others will use the cache.
    __lowercase							  = cached_features_file + '''.lock'''
    with FileLock(lowercase__			):
     if os.path.exists(lowercase__			) and not overwrite_cache:
      logger.info(F"Loading features from cached file {cached_features_file}"			)
      __lowercase							  = torch.load(lowercase__			)
     else:
      logger.info(F"Creating features from dataset file at {data_dir}"			)
      __lowercase							  = token_classification_task.read_examples_from_file(lowercase__					,lowercase__			)
      # TODO clean up all this to leverage built-in features of tokenizers
      __lowercase							  = token_classification_task.convert_examples_to_features(
          lowercase__					,lowercase__					,lowercase__					,lowercase__					,cls_token_at_end=bool(model_type in ['''xlnet''']			)					,cls_token=tokenizer.cls_token					,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,sep_token=tokenizer.sep_token					,sep_token_extra=lowercase__					,pad_on_left=bool(tokenizer.padding_side == '''left'''			)					,pad_token=tokenizer.pad_token_id					,pad_token_segment_id=tokenizer.pad_token_type_id					,pad_token_label_id=self.pad_token_label_id					,)
      logger.info(F"Saving features into cached file {cached_features_file}"			)
      torch.save(self.features					,lowercase__			)






   def __len__(      self   :     List[str]			):
    return len(self.features			)






   def __getitem__(      self   :     List[Any]					,lowercase__   :     Optional[Any]			):
    return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class 			lowercase_	:




   """simple docstring"""




   SCREAMING_SNAKE_CASE    :  List[InputFeatures]
   SCREAMING_SNAKE_CASE    :  int							=			-1_0_0






   def __init__(      self   :     str					,lowercase__   :     TokenClassificationTask					,lowercase__   :     str					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     List[str]					,lowercase__   :     str					,lowercase__   :     Optional[int] = None					,lowercase__   :     int=False					,lowercase__   :     Split = Split.train					,):
    __lowercase							  = token_classification_task.read_examples_from_file(lowercase__					,lowercase__			)
    # TODO clean up all this to leverage built-in features of tokenizers
    __lowercase							  = token_classification_task.convert_examples_to_features(
        lowercase__					,lowercase__					,lowercase__					,lowercase__					,cls_token_at_end=bool(model_type in ['''xlnet''']			)					,cls_token=tokenizer.cls_token					,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,sep_token=tokenizer.sep_token					,sep_token_extra=lowercase__					,pad_on_left=bool(tokenizer.padding_side == '''left'''			)					,pad_token=tokenizer.pad_token_id					,pad_token_segment_id=tokenizer.pad_token_type_id					,pad_token_label_id=self.pad_token_label_id					,)

    def gen():
     for ex in self.features:
      if ex.token_type_ids is None:
       yield (
           {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
           ex.label_ids,
       )
      else:
       yield (
           {
               "input_ids": ex.input_ids,
               "attention_mask": ex.attention_mask,
               "token_type_ids": ex.token_type_ids,
           },
           ex.label_ids,
       )

    if "token_type_ids" not in tokenizer.model_input_names:
     __lowercase							  = tf.data.Dataset.from_generator(
         lowercase__					,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,(
             {'''input_ids''': tf.TensorShape([None]			), '''attention_mask''': tf.TensorShape([None]			)},
             tf.TensorShape([None]			),
         )					,)
    else:
     __lowercase							  = tf.data.Dataset.from_generator(
         lowercase__					,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,(
             {
                 '''input_ids''': tf.TensorShape([None]			),
                 '''attention_mask''': tf.TensorShape([None]			),
                 '''token_type_ids''': tf.TensorShape([None]			),
             },
             tf.TensorShape([None]			),
         )					,)






   def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
    __lowercase							  = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

    return self.dataset






   def __len__(      self   :     Any			):
    return len(self.features			)






   def __getitem__(      self   :     Union[str, Any]					,lowercase__   :     str			):
    return self.features[i]



snake_case :		Optional[int]							      =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def   lowerCAmelCase_						(					_snake_case      :  bytes   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Tuple	   =     F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(_snake_case   )

	__magic_name__		:		Optional[int]	   =     "".join(bin(_snake_case   )[2:].zfill(8   ) for byte in data   )

	__magic_name__		:		List[Any]	   =     len(_snake_case   ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__magic_name__		:		List[str]	   =     B"=" * ((6 - len(_snake_case   ) % 6) // 2)

		# Append binary_stream with arbitrary binary digits (0's by default) to make its
		# length a multiple of 6.
		binary_stream += "0" * (6 - len(_snake_case   ) % 6)
	else:
		__magic_name__		:		List[str]	   =     B""

	# Encode every 6 binary digits to their corresponding Base64 character
	return (
	    "".join(
	        B64_CHARSET[int(binary_stream[index : index + 6]      ,   2   )]
	        for index in range(0      ,   len(_snake_case   )      ,   6   )   ).encode()
	    + padding
	)






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ) and not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		List[str]	   =     (
		    "argument should be a bytes-like object or ASCII string, "
		    F'''not \'{encoded_data.__class__.__name__}\''''
		)
		raise TypeError(_snake_case   )

	# In case encoded_data is a bytes-like object, make sure it contains only
	# ASCII characters so we convert it to a string object
	if isinstance(_snake_case      ,   _snake_case   ):
		try:
			__magic_name__		:		List[Any]	   =     encoded_data.decode("utf-8"   )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"   )

	__magic_name__		:		List[str]	   =     encoded_data.count("="   )

	# Check if the encoded string contains non base64 characters
	if padding:
		assert all(
		    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
	else:
		assert all(
		    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

	# Check the padding
	assert len(_snake_case   ) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__magic_name__		:		Optional[int]	   =     encoded_data[:-padding]

		__magic_name__		:		Dict	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
	else:
		__magic_name__		:		Union[str, Any]	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )

	__magic_name__		:		List[Any]	   =     [
	    int(binary_stream[index : index + 8]      ,   2   )
	    for index in range(0      ,   len(_snake_case   )      ,   8   )
	]

	return bytes(_snake_case   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
