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,
)


__a  :					Optional[int]          =  logging.getLogger(__name__)




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None						)					-> List[Any]:

							'''simple docstring'''


							if current_layer == 0:
											__lowercase			      =							self.embedding_hidden_mapping_in(lowerCAmelCase__						)
							else:
											__lowercase			      =							hidden_states[0]

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

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

							__lowercase			      =							self.albert_layer_groups[group_idx](
							    lowerCAmelCase__				,       lowerCAmelCase__				,       head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]				,       )
							__lowercase			      =							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.'''						,_UpperCAmelCase						,)
class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__						)

							__lowercase			      =							AlbertTransformerWithPabee(lowerCAmelCase__						)

							self.init_weights()
							__lowercase			      =							0
							__lowercase			      =							0
							__lowercase			      =							0

							__lowercase			      =							0





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							threshold





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							patience





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							0
							__lowercase			      =							0





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							self.inference_layers_num / self.inference_instances_num
							__lowercase			      =							(
							    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(lowerCAmelCase__						)





			@add_start_docstrings_to_model_forward(lowerCAmelCase__						)
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=False				,       )					-> int:

							'''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:
											__lowercase			      =							input_ids.size()
							elif inputs_embeds is not None:
											__lowercase			      =							inputs_embeds.size()[:-1]
							else:
											raise ValueError('''You have to specify either input_ids or inputs_embeds'''						)

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

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

							__lowercase			      =							attention_mask.unsqueeze(1						).unsqueeze(2						)
							__lowercase			      =							extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
							__lowercase			      =							(1.0 - extended_attention_mask) * -1_0000.0
							__lowercase			      =							self.get_head_mask(lowerCAmelCase__				,       self.config.num_hidden_layers						)

							__lowercase			      =							self.embeddings(
							    lowerCAmelCase__				,       position_ids=lowerCAmelCase__				,       token_type_ids=lowerCAmelCase__				,       inputs_embeds=lowerCAmelCase__						)
							__lowercase			      =							embedding_output

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

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

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

															__lowercase			      =							logits
															if patient_counter == self.patience:
																			break
											__lowercase			      =							[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. '''						,_UpperCAmelCase						,)
class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__						)
							__lowercase			      =							config.num_labels

							__lowercase			      =							AlbertModelWithPabee(lowerCAmelCase__						)
							__lowercase			      =							nn.Dropout(config.classifier_dropout_prob						)
							__lowercase			      =							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(lowerCAmelCase__						)
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       )					-> Tuple:

							'''simple docstring'''


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

							__lowercase			      =							(logits[-1],)

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

							return outputs
import timeit




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):  # Initializes a matrix identical to the one provided.
											__lowercase			      =							arg
											__lowercase			      =							len(lowerCAmelCase__						)
							else:  # Initializes a square matrix of the given size and set values to zero.
											__lowercase			      =							arg
											__lowercase			      =							[[0 for _ in range(self.n						)] for _ in range(self.n						)]





			def __mul__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							Matrix(self.n						)
							for i in range(self.n						):
											for j in range(self.n						):
															for k in range(self.n						):
																			matrix.t[i][j] += self.t[i][k] * b.t[k][j]
							return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							Matrix([[1, 0], [0, 1]]							)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa
				__lowercase			      =							Matrix([[1, 1], [1, 0]]							)
				__lowercase			      =							modular_exponentiation(lowercase							,      n - 2							)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa

				__lowercase			      =							fa
				__lowercase			      =							fa
				n -= 2

				while n > 0:
								__lowercase      ,					__lowercase			      =							fn_a + fn_a, fn_a
								n -= 1

				return fn_a





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
				__lowercase			      =							'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print('''With matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import simple_fibonacci
'''
				__lowercase			      =							'''simple_fibonacci(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print(
				    '''Without matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
		main()