'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase	: Any    		=  logging.get_logger(__name__)

__UpperCamelCase	: Union[str, Any]    		=  {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class       lowercase__		(     __lowerCAmelCase):
						UpperCamelCase_  =	"bert"


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       List[str]=3_0522					,	UpperCamelCase__			:       Union[str, Any]=768					,	UpperCamelCase__			:       Tuple=12					,	UpperCamelCase__			:       Tuple=12					,	UpperCamelCase__			:       Any=3072					,	UpperCamelCase__			:       Union[str, Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       Any=0.1					,	UpperCamelCase__			:       Tuple=512					,	UpperCamelCase__			:       Dict=2					,	UpperCamelCase__			:       Dict=0.02					,	UpperCamelCase__			:       Union[str, Any]=1E-12					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       Tuple="absolute"					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       int=None					,	**UpperCamelCase__			:       Tuple					,	):





												'''simple docstring'''





												super().__init__(pad_token_id=lowerCamelCase__					,	**lowerCamelCase__		)

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






class       lowercase__		(     __lowerCAmelCase):


						@property
						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							analyze_text(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							list(''' ''' + ascii_lowercase		)
						# what is our total sum of probabilities.
						SCREAMING_SNAKE_CASE							:					Tuple           =							sum(single_char_strings.values()		)

						# one length string
						SCREAMING_SNAKE_CASE							:					Tuple           =							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:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							single_char_strings[ch]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							my_str / all_sum
																		my_fir_sum += prob * math.loga(_lowercase		)  # entropy formula.

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

						# two len string
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sum(two_char_strings.values()		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							0
						# for each alpha (two in size) calculate entropy.
						for cha in my_alphas:
												for cha in my_alphas:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							cha + cha
																		if sequence in two_char_strings:
																								SCREAMING_SNAKE_CASE							:					Any           =							two_char_strings[sequence]
																								SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		) / all_sum
																								my_sec_sum += prob * math.loga(_lowercase		)

    # 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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							Counter()  # type: ignore
						SCREAMING_SNAKE_CASE							:					Any           =							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(_lowercase		) - 1		):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings
def    A			(					):
						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()





