





"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 					A__				(			UpperCamelCase       ):
							A,	A		=  analyze_text(UpperCamelCase       )
							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(UpperCamelCase       )  # 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(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 					A__				(			UpperCamelCase       ):
							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(UpperCamelCase       ) - 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()









"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_snake_case     :		List[Any]       						= logging.get_logger(__name__)

_snake_case     :		int       						= {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''marian'''
					UpperCamelCase      =							['''past_key_values''']
					UpperCamelCase      =							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(					self			:int     ,       __UpperCamelCase			:Any=5_81_01     ,       __UpperCamelCase			:int=None     ,       __UpperCamelCase			:Union[str, Any]=10_24     ,       __UpperCamelCase			:Union[str, Any]=12     ,       __UpperCamelCase			:str=40_96     ,       __UpperCamelCase			:int=16     ,       __UpperCamelCase			:int=12     ,       __UpperCamelCase			:Optional[Any]=40_96     ,       __UpperCamelCase			:Optional[Any]=16     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:str=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:Any="gelu"     ,       __UpperCamelCase			:Any=10_24     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Optional[Any]=0.0     ,       __UpperCamelCase			:Union[str, Any]=0.0     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:List[str]=5_81_00     ,       __UpperCamelCase			:str=False     ,       __UpperCamelCase			:Optional[int]=5_81_00     ,       __UpperCamelCase			:List[Any]=0     ,       __UpperCamelCase			:List[str]=0     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  vocab_size
												A		=  decoder_vocab_size or vocab_size
												A		=  max_position_embeddings
												A		=  d_model
												A		=  encoder_ffn_dim
												A		=  encoder_layers
												A		=  encoder_attention_heads
												A		=  decoder_ffn_dim
												A		=  decoder_layers
												A		=  decoder_attention_heads
												A		=  dropout
												A		=  attention_dropout
												A		=  activation_dropout
												A		=  activation_function
												A		=  init_std
												A		=  encoder_layerdrop
												A		=  decoder_layerdrop
												A		=  use_cache
												A		=  encoder_layers
												A		=  scale_embedding  # scale factor will be sqrt(d_model) if True
												A		=  share_encoder_decoder_embeddings
												super().__init__(
												    pad_token_id=__UpperCamelCase     ,       eos_token_id=__UpperCamelCase     ,       is_encoder_decoder=__UpperCamelCase     ,       decoder_start_token_id=__UpperCamelCase     ,       forced_eos_token_id=__UpperCamelCase     ,       **__UpperCamelCase     ,       )







class 					_UpperCAmelCase   (	lowercase_ ):


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 	lowerCamelCase       (					self			:List[str]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)

																			if self.use_past:
																										A		=  {0: "batch"}
																										A		=  {0: "batch", 1: "past_decoder_sequence + sequence"}
																			else:
																										A		=  {0: "batch", 1: "decoder_sequence"}
																										A		=  {0: "batch", 1: "decoder_sequence"}

																			if self.use_past:
																										self.fill_with_past_key_values_(__UpperCamelCase     ,       direction="inputs"		)
												elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(__UpperCamelCase		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												else:
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																			        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																			    ]		)

												return common_inputs


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def 	lowerCamelCase       (					self			:List[str]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super().outputs
												else:
																			A		=  super(__UpperCamelCase     ,       self		).outputs
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(__UpperCamelCase		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												return common_outputs


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)

												# Generate decoder inputs
												A		=  seq_length if not self.use_past else 1
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)
												A		=  {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												A		=  dict(**__UpperCamelCase     ,       **__UpperCamelCase		)

												if self.use_past:
																			if not is_torch_available():
																										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
																			else:
																										import torch
																			A,	A		=  common_inputs["input_ids"].shape
																			A		=  common_inputs["decoder_input_ids"].shape[1]
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    encoder_seq_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)
																			A		=  decoder_seq_length + 3
																			A		=  (
																			    batch,
																			    num_decoder_attention_heads,
																			    decoder_past_length,
																			    self._config.hidden_size // num_decoder_attention_heads,
																			)

																			A		=  torch.cat(
																			    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase     ,       __UpperCamelCase		)]     ,       dim=1		)

																			A		=  []
																			# If the number of encoder and decoder layers are present in the model configuration, both are considered
																			A,	A		=  self.num_layers
																			A		=  min(__UpperCamelCase     ,       __UpperCamelCase		)
																			A		=  max(__UpperCamelCase     ,       __UpperCamelCase		) - min_num_layers
																			A		=  "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																			for _ in range(__UpperCamelCase		):
																										common_inputs["past_key_values"].append(
																										    (
																										        torch.zeros(__UpperCamelCase		),
																										        torch.zeros(__UpperCamelCase		),
																										        torch.zeros(__UpperCamelCase		),
																										        torch.zeros(__UpperCamelCase		),
																										    )		)
																			# TODO: test this.
																			A		=  encoder_shape if remaining_side_name == "encoder" else decoder_shape
																			for _ in range(__UpperCamelCase     ,       __UpperCamelCase		):
																										common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase		), torch.zeros(__UpperCamelCase		))		)
												return common_inputs


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)

												if self.use_past:
																			if not is_torch_available():
																										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
																			else:
																										import torch
																			A,	A		=  common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			A		=  seqlen + 2
																			A,	A		=  self.num_layers
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)

																			A		=  common_inputs["attention_mask"].dtype
																			A		=  torch.cat(
																			    [common_inputs["attention_mask"], torch.ones(__UpperCamelCase     ,       __UpperCamelCase     ,       dtype=__UpperCamelCase		)]     ,       dim=1		)
																			A		=  [
																			    (torch.zeros(__UpperCamelCase		), torch.zeros(__UpperCamelCase		)) for _ in range(__UpperCamelCase		)
																			]
												return common_inputs


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												# Copied from OnnxConfig.generate_dummy_inputs
												# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												A		=  compute_effective_axis_dimension(
												    __UpperCamelCase     ,       fixed_dimension=OnnxConfig.default_fixed_batch     ,       num_token_to_add=0		)

												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												A		=  tokenizer.num_special_tokens_to_add(__UpperCamelCase		)
												A		=  compute_effective_axis_dimension(
												    __UpperCamelCase     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=__UpperCamelCase		)

												# Generate dummy inputs according to compute batch and sequence
												A		=  [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
												A		=  dict(tokenizer(__UpperCamelCase     ,       return_tensors=__UpperCamelCase		)		)
												return common_inputs


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    __UpperCamelCase     ,       batch_size=__UpperCamelCase     ,       seq_length=__UpperCamelCase     ,       is_pair=__UpperCamelCase     ,       framework=__UpperCamelCase		)

												else:
																			A		=  self._generate_dummy_inputs_for_causal_lm(
																			    __UpperCamelCase     ,       batch_size=__UpperCamelCase     ,       seq_length=__UpperCamelCase     ,       is_pair=__UpperCamelCase     ,       framework=__UpperCamelCase		)

												return common_inputs


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:str		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super()._flatten_past_key_values_(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)
												else:
																			A		=  super(__UpperCamelCase     ,       self		)._flatten_past_key_values_(
																			    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					@property
					def 	lowerCamelCase       (					self			:List[str]		):
												return 1e-4


