




"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


_A													=      logging.get_logger(__name__)

_A													=      {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Any     				=     "xlm-roberta"






   def __init__(		self       :		int   ,     UpperCamelCase       :		Optional[int]=3_05_22   ,     UpperCamelCase       :		Dict=7_68   ,     UpperCamelCase       :		str=12   ,     UpperCamelCase       :		Dict=12   ,     UpperCamelCase       :		List[str]=30_72   ,     UpperCamelCase       :		str="gelu"   ,     UpperCamelCase       :		int=0.1   ,     UpperCamelCase       :		Dict=0.1   ,     UpperCamelCase       :		Optional[Any]=5_12   ,     UpperCamelCase       :		Dict=2   ,     UpperCamelCase       :		Optional[Any]=0.02   ,     UpperCamelCase       :		List[str]=1E-1_2   ,     UpperCamelCase       :		Any=1   ,     UpperCamelCase       :		Optional[Any]=0   ,     UpperCamelCase       :		Optional[int]=2   ,     UpperCamelCase       :		Any="absolute"   ,     UpperCamelCase       :		Tuple=True   ,     UpperCamelCase       :		Union[str, Any]=None   ,     **UpperCamelCase       :		Union[str, Any]   ,     )			->						Any:

         """simple docstring"""
         super().__init__(pad_token_id=UpperCamelCase   ,     bos_token_id=UpperCamelCase   ,     eos_token_id=UpperCamelCase   ,     **UpperCamelCase   )

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






class _lowerCamelCase    (					a_						):






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Mapping[str, Mapping[int, str]]:

         """simple docstring"""
         if self.task == "multiple-choice":
               lowerCAmelCase__      :			Optional[int]   =	{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
               lowerCAmelCase__      :			int   =	{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]   )











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

    # print entropy
      print(f"""{round(-1 * my_fir_sum				):.1f}"""				)

      # two len string
      lowerCAmelCase__      :			Tuple   =	sum(two_char_strings.values()				)
      lowerCAmelCase__      :			str   =	0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  lowerCAmelCase__      :			Optional[int]   =	cha + cha
                  if sequence in two_char_strings:
                        lowerCAmelCase__      :			int   =	two_char_strings[sequence]
                        lowerCAmelCase__      :			str   =	int(__UpperCAmelCase				) / all_sum
                        my_sec_sum += prob * math.loga(__UpperCAmelCase				)

    # print second entropy
      print(f"""{round(-1 * my_sec_sum				):.1f}"""				)

      # print the difference between them
      print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)				):.1f}"""				)



def 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0    ,		len(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    Any:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
  main()





