






import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


lowerCamelCase_						     =  logging.getLogger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_=None			,			lowercase_=None   )					->					str:
														'''simple docstring'''

														if current_layer == 0:
																					lowerCAmelCase_										=     self.embedding_hidden_mapping_in(lowercase_   )
														else:
																					lowerCAmelCase_										=     hidden_states[0]

														lowerCAmelCase_										=     int(self.config.num_hidden_layers / self.config.num_hidden_groups   )

														# Index of the hidden group
														lowerCAmelCase_										=     int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)   )

														lowerCAmelCase_										=     self.albert_layer_groups[group_idx](
														    lowercase_			,			lowercase_			,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]			,			)
														lowerCAmelCase_										=     layer_group_output[0]

														return (hidden_states,)




@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''	,	a_	,	)
class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														super().__init__(lowercase_   )

														lowerCAmelCase_										=     AlbertTransformerWithPabee(lowercase_   )

														self.init_weights()
														lowerCAmelCase_										=     0
														lowerCAmelCase_										=     0
														lowerCAmelCase_										=     0

														lowerCAmelCase_										=     0

							def      _lowercase       (		self			,			lowercase_   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     threshold

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     patience

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     0
														lowerCAmelCase_										=     0

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.inference_layers_num / self.inference_instances_num
														lowerCAmelCase_										=     (
														    f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
														    f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
														)
														print(lowercase_   )






							@add_start_docstrings_to_model_forward(lowercase_   )
							def      _lowercase       (		self			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=False			,			)					->					Any:
														'''simple docstring'''

														if input_ids is not None and inputs_embeds is not None:
																					raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'   )
														elif input_ids is not None:
																					lowerCAmelCase_										=     input_ids.size()
														elif inputs_embeds is not None:
																					lowerCAmelCase_										=     inputs_embeds.size()[:-1]
														else:
																					raise ValueError('You have to specify either input_ids or inputs_embeds'   )

														lowerCAmelCase_										=     input_ids.device if input_ids is not None else inputs_embeds.device

														if attention_mask is None:
																					lowerCAmelCase_										=     torch.ones(lowercase_			,			device=lowercase_   )
														if token_type_ids is None:
																					lowerCAmelCase_										=     torch.zeros(lowercase_			,			dtype=torch.long			,			device=lowercase_   )

														lowerCAmelCase_										=     attention_mask.unsqueeze(1   ).unsqueeze(2   )
														lowerCAmelCase_										=     extended_attention_mask.to(dtype=self.dtype   )  # fp16 compatibility
														lowerCAmelCase_										=     (1.0 - extended_attention_mask) * -1_00_00.0
														lowerCAmelCase_										=     self.get_head_mask(lowercase_			,			self.config.num_hidden_layers   )

														lowerCAmelCase_										=     self.embeddings(
														    lowercase_			,			position_ids=lowercase_			,			token_type_ids=lowercase_			,			inputs_embeds=lowercase_   )
														lowerCAmelCase_										=     embedding_output

														if self.training:
																					lowerCAmelCase_										=     []
																					for i in range(self.config.num_hidden_layers   ):
																												lowerCAmelCase_										=     self.encoder.adaptive_forward(
																												    lowercase_			,			current_layer=lowercase_			,			attention_mask=lowercase_			,			head_mask=lowercase_			,			)

																												lowerCAmelCase_										=     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]   )   )
																												lowerCAmelCase_										=     output_layers[i](output_dropout(lowercase_   )   )
																												res.append(lowercase_   )
														elif self.patience == 0:  # Use all layers for inference
																					lowerCAmelCase_										=     self.encoder(lowercase_			,			lowercase_			,			head_mask=lowercase_   )
																					lowerCAmelCase_										=     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]   )   )
																					lowerCAmelCase_										=     [output_layers[self.config.num_hidden_layers - 1](lowercase_   )]
														else:
																					lowerCAmelCase_										=     0
																					lowerCAmelCase_										=     None
																					lowerCAmelCase_										=     0
																					for i in range(self.config.num_hidden_layers   ):
																												calculated_layer_num += 1
																												lowerCAmelCase_										=     self.encoder.adaptive_forward(
																												    lowercase_			,			current_layer=lowercase_			,			attention_mask=lowercase_			,			head_mask=lowercase_			,			)

																												lowerCAmelCase_										=     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]   )   )
																												lowerCAmelCase_										=     output_layers[i](lowercase_   )
																												if regression:
																																			lowerCAmelCase_										=     logits.detach()
																																			if patient_result is not None:
																																										lowerCAmelCase_										=     patient_result.detach()
																																			if (patient_result is not None) and torch.abs(patient_result - labels   ) < self.regression_threshold:
																																										patient_counter += 1
																																			else:
																																										lowerCAmelCase_										=     0
																												else:
																																			lowerCAmelCase_										=     logits.detach().argmax(dim=1   )
																																			if patient_result is not None:
																																										lowerCAmelCase_										=     patient_result.detach().argmax(dim=1   )
																																			if (patient_result is not None) and torch.all(labels.eq(lowercase_   )   ):
																																										patient_counter += 1
																																			else:
																																										lowerCAmelCase_										=     0

																												lowerCAmelCase_										=     logits
																												if patient_counter == self.patience:
																																			break
																					lowerCAmelCase_										=     [patient_result]
																					self.inference_layers_num += calculated_layer_num
																					self.inference_instances_num += 1

														return res




@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''	,	a_	,	)
class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_   )					->					Tuple:
														'''simple docstring'''

														super().__init__(lowercase_   )
														lowerCAmelCase_										=     config.num_labels

														lowerCAmelCase_										=     AlbertModelWithPabee(lowercase_   )
														lowerCAmelCase_										=     nn.Dropout(config.classifier_dropout_prob   )
														lowerCAmelCase_										=     nn.ModuleList(
														    [nn.Linear(config.hidden_size			,			self.config.num_labels   ) for _ in range(config.num_hidden_layers   )]   )

														self.init_weights()






							@add_start_docstrings_to_model_forward(lowercase_   )
							def      _lowercase       (		self			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			)					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     self.albert(
														    input_ids=lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			position_ids=lowercase_			,			head_mask=lowercase_			,			inputs_embeds=lowercase_			,			output_dropout=self.dropout			,			output_layers=self.classifiers			,			regression=self.num_labels == 1			,			)

														lowerCAmelCase_										=     (logits[-1],)

														if labels is not None:
																					lowerCAmelCase_										=     None
																					lowerCAmelCase_										=     0
																					for ix, logits_item in enumerate(lowercase_   ):
																												if self.num_labels == 1:
																																			#  We are doing regression
																																			lowerCAmelCase_										=     MSELoss()
																																			lowerCAmelCase_										=     loss_fct(logits_item.view(-1   )			,			labels.view(-1   )   )
																												else:
																																			lowerCAmelCase_										=     CrossEntropyLoss()
																																			lowerCAmelCase_										=     loss_fct(logits_item.view(-1			,			self.num_labels   )			,			labels.view(-1   )   )
																												if total_loss is None:
																																			lowerCAmelCase_										=     loss
																												else:
																																			total_loss += loss * (ix + 1)
																												total_weights += ix + 1
																					lowerCAmelCase_										=     (total_loss / total_weights,) + outputs

														return outputs











import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



