


'''simple docstring'''







import itertools
import string
from collections.abc import Generator, Iterable






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       iter(_A					)
       while True:
              _lowerCAmelCase    :							int   			=       tuple(itertools.islice(_A   ,						_A					)					)
              if not chunk:
                     return
              yield chunk






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       ''.join([c.upper() for c in dirty if c in string.ascii_letters]					)
       _lowerCAmelCase    :							Tuple   			=       ''

       if len(_A					) < 2:
              return dirty

       for i in range(len(_A					) - 1					):
              clean += dirty[i]

              if dirty[i] == dirty[i + 1]:
                     clean += "X"

       clean += dirty[-1]

       if len(_A					) & 1:
              clean += "X"

       return clean






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       'ABCDEFGHIKLMNOPQRSTUVWXYZ'
       # we're using a list instead of a '2d' array because it makes the math
       # for setting up the table and doing the actual encoding/decoding simpler
       _lowerCAmelCase    :							Optional[int]   			=       []

       # copy key chars into the table if they are in `alphabet` ignoring duplicates
       for char in key.upper():
              if char not in table and char in alphabet:
                     table.append(_A					)

    # fill the rest of the table in with the remaining alphabet chars
       for char in alphabet:
              if char not in table:
                     table.append(_A					)

       return table






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       generate_table(_A					)
       _lowerCAmelCase    :							Optional[int]   			=       prepare_input(_A					)
       _lowerCAmelCase    :							Tuple   			=       ''

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(_A   ,						2					):
              _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       divmod(table.index(_A					)   ,						5					)
              _lowerCAmelCase			,     _lowerCAmelCase    :							Tuple   			=       divmod(table.index(_A					)   ,						5					)

              if rowa == rowa:
                     ciphertext += table[rowa * 5 + (cola + 1) % 5]
                     ciphertext += table[rowa * 5 + (cola + 1) % 5]
              elif cola == cola:
                     ciphertext += table[((rowa + 1) % 5) * 5 + cola]
                     ciphertext += table[((rowa + 1) % 5) * 5 + cola]
              else:  # rectangle
                     ciphertext += table[rowa * 5 + cola]
                     ciphertext += table[rowa * 5 + cola]

       return ciphertext






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[Any]   			=       generate_table(_A					)
       _lowerCAmelCase    :							Optional[Any]   			=       ''

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(_A   ,						2					):
              _lowerCAmelCase			,     _lowerCAmelCase    :							Any   			=       divmod(table.index(_A					)   ,						5					)
              _lowerCAmelCase			,     _lowerCAmelCase    :							int   			=       divmod(table.index(_A					)   ,						5					)

              if rowa == rowa:
                     plaintext += table[rowa * 5 + (cola - 1) % 5]
                     plaintext += table[rowa * 5 + (cola - 1) % 5]
              elif cola == cola:
                     plaintext += table[((rowa - 1) % 5) * 5 + cola]
                     plaintext += table[((rowa - 1) % 5) * 5 + cola]
              else:  # rectangle
                     plaintext += table[rowa * 5 + cola]
                     plaintext += table[rowa * 5 + cola]

       return plaintext





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase    :     str					=						logging.get_logger(__name__)

lowerCAmelCase    :     int					=						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "data2vec-text"







     def __init__( self      ,	snake_case__=3_0522      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__="absolute"      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(pad_token_id=snake_case__      ,	bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       vocab_size
            _lowerCAmelCase    :							Tuple   			=       hidden_size
            _lowerCAmelCase    :							Dict   			=       num_hidden_layers
            _lowerCAmelCase    :							int   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       intermediate_size
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							str   			=       max_position_embeddings
            _lowerCAmelCase    :							Any   			=       type_vocab_size
            _lowerCAmelCase    :							int   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps
            _lowerCAmelCase    :							List[Any]   			=       position_embedding_type
            _lowerCAmelCase    :							str   			=       use_cache
            _lowerCAmelCase    :							Union[str, Any]   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     @property
     def a	( self   ):

            '''simple docstring'''

            if self.task == "multiple-choice":
                   _lowerCAmelCase    :							Union[str, Any]   			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _lowerCAmelCase    :							List[Any]   			=       {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]   )

