
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[Any]										=    {
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"gptsan-japanese"
	snake_case_    =					[
	    "past_key_values",
	]
	snake_case_    =					{
	    "hidden_size": "d_model",
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	}






	def __init__(	self :			List[str]							,A :			List[str]=3_60_00							,A :			Tuple=12_80							,A :			Dict=10_24							,A :			Tuple=81_92							,A :			Tuple=40_96							,A :			Tuple=1_28							,A :			Union[str, Any]=10							,A :			int=0							,A :			str=16							,A :			Dict=16							,A :			Optional[int]=1_28							,A :			Union[str, Any]=0.0							,A :			Tuple=1E-5							,A :			Dict=False							,A :			Optional[int]=0.0							,A :			List[str]="float32"							,A :			int=False							,A :			Any=False							,A :			Any=False							,A :			str=0.0_02							,A :			Tuple=False							,A :			Union[str, Any]=True							,A :			Any=3_59_98							,A :			int=3_59_95							,A :			Union[str, Any]=3_59_99							,**A :			Union[str, Any]							,):
					__A             =			vocab_size
					__A             =			max_position_embeddings
					__A             =			d_model
					__A             =			d_ff
					__A             =			d_ext
					__A             =			d_spout
					__A             =			num_switch_layers
					__A             =			num_ext_layers
					__A             =			num_switch_layers + num_ext_layers
					__A             =			num_heads
					__A             =			num_experts
					__A             =			expert_capacity
					__A             =			dropout_rate
					__A             =			layer_norm_epsilon
					__A             =			router_bias
					__A             =			router_jitter_noise
					__A             =			router_dtype
					__A             =			router_ignore_padding_tokens
					__A             =			output_hidden_states
					__A             =			output_attentions
					__A             =			initializer_factor
					__A             =			output_router_logits
					__A             =			use_cache

					super().__init__(
					    separator_token_id=A							,pad_token_id=A							,eos_token_id=A							,**A							,)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			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:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

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

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

    # 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 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			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(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""simple docstring"""



				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()
