





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


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

_lowerCamelCase :    Optional[int]  							= {
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "nllb-moe"
							UpperCAmelCase_      						=   ["past_key_values"]
							UpperCAmelCase_      						=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_2_8_1_1_2,			_UpperCAmelCase     :					Any=1_0_2_4,			_UpperCAmelCase     :					str=1_2,			_UpperCAmelCase     :					List[Any]=4_0_9_6,			_UpperCAmelCase     :					Any=1_6,			_UpperCAmelCase     :					List[str]=1_2,			_UpperCAmelCase     :					Dict=4_0_9_6,			_UpperCAmelCase     :					Union[str, Any]=1_6,			_UpperCAmelCase     :					Any=0.05,			_UpperCAmelCase     :					Optional[int]=0.05,			_UpperCAmelCase     :					str=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]="relu",			_UpperCAmelCase     :					Any=1_0_2_4,			_UpperCAmelCase     :					Any=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[Any]=0.0,			_UpperCAmelCase     :					Optional[Any]=0.02,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=True,			_UpperCAmelCase     :					Union[str, Any]=False,			_UpperCAmelCase     :					Dict="float32",			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					List[Any]=1_2_8,			_UpperCAmelCase     :					Tuple=6_4,			_UpperCAmelCase     :					Tuple=4,			_UpperCAmelCase     :					str=4,			_UpperCAmelCase     :					Any=0.001,			_UpperCAmelCase     :					List[Any]=0.001,			_UpperCAmelCase     :					Tuple="all",			_UpperCAmelCase     :					Dict=False,			_UpperCAmelCase     :					Optional[int]=False,			_UpperCAmelCase     :					List[Any]=1.0,			_UpperCAmelCase     :					Optional[Any]=0.2,			_UpperCAmelCase     :					Union[str, Any]=1,			_UpperCAmelCase     :					int=0,			_UpperCAmelCase     :					Union[str, Any]=2,			_UpperCAmelCase     :					str=False,			**_UpperCAmelCase     :					Tuple,			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						vocab_size
														SCREAMING_SNAKE_CASE__       :      Dict     =						max_position_embeddings
														SCREAMING_SNAKE_CASE__       :      List[str]     =						d_model
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						encoder_ffn_dim
														SCREAMING_SNAKE_CASE__       :      str     =						encoder_layers
														SCREAMING_SNAKE_CASE__       :      Any     =						encoder_attention_heads
														SCREAMING_SNAKE_CASE__       :      int     =						decoder_ffn_dim
														SCREAMING_SNAKE_CASE__       :      str     =						decoder_layers
														SCREAMING_SNAKE_CASE__       :      int     =						decoder_attention_heads
														SCREAMING_SNAKE_CASE__       :      Tuple     =						dropout
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						attention_dropout
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						activation_dropout
														SCREAMING_SNAKE_CASE__       :      Tuple     =						activation_function
														SCREAMING_SNAKE_CASE__       :      int     =						init_std
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						encoder_layerdrop
														SCREAMING_SNAKE_CASE__       :      Tuple     =						decoder_layerdrop
														SCREAMING_SNAKE_CASE__       :      str     =						use_cache
														SCREAMING_SNAKE_CASE__       :      Any     =						encoder_layers
														SCREAMING_SNAKE_CASE__       :      Tuple     =						scale_embedding  # scale factor will be sqrt(d_model) if True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						router_z_loss_coef
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						router_aux_loss_coef
														SCREAMING_SNAKE_CASE__       :      List[str]     =						decoder_sparse_step
														SCREAMING_SNAKE_CASE__       :      str     =						encoder_sparse_step
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						num_experts
														SCREAMING_SNAKE_CASE__       :      Any     =						expert_capacity
														SCREAMING_SNAKE_CASE__       :      List[str]     =						router_bias
														if router_dtype not in ["float32", "float16", "bfloat16"]:
																					raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						router_dtype

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						router_ignore_padding_tokens
														SCREAMING_SNAKE_CASE__       :      int     =						batch_prioritized_routing
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						second_expert_policy
														SCREAMING_SNAKE_CASE__       :      Any     =						normalize_router_prob_before_dropping
														SCREAMING_SNAKE_CASE__       :      str     =						moe_eval_capacity_token_fraction
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						moe_token_dropout
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						output_router_logits
														super().__init__(
														    pad_token_id=_UpperCAmelCase,			bos_token_id=_UpperCAmelCase,			eos_token_id=_UpperCAmelCase,			is_encoder_decoder=_UpperCAmelCase,			decoder_start_token_id=_UpperCAmelCase,			**_UpperCAmelCase,			)













from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						analyze_text(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						list(" " + ascii_lowercase       )
							# what is our total sum of probabilities.
							SCREAMING_SNAKE_CASE__       :      str     =						sum(single_char_strings.values()       )

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

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

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

    # 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 (  SCREAMING_SNAKE_CASE__      :  str       )	->    tuple[dict, dict]:

							'''simple docstring'''






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




def _a (  )	->    str:

							'''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()






