from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """				,       a				,       )
class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							RobertaConfig
							lowercase__						:       Optional[Any]								=							"""roberta"""




							def __init__(						self   :					int	,							_UpperCamelCase   :					Tuple      )					->				int:



												'''simple docstring'''

												super().__init__(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       RobertaEmbeddings(_UpperCamelCase      )
												self.init_weights()



@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """				,       a				,       )
class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							RobertaConfig
							lowercase__						:       List[str]								=							"""roberta"""




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       config.num_labels
												SCREAMING_SNAKE_CASE    			=       config.num_hidden_layers

												SCREAMING_SNAKE_CASE    			=       DeeRobertaModel(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       nn.Dropout(config.hidden_dropout_prob      )
												SCREAMING_SNAKE_CASE    			=       nn.Linear(config.hidden_size	,							self.config.num_labels      )





							@add_start_docstrings_to_model_forward(_UpperCamelCase      )
							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					str=None	,							_UpperCamelCase   :					Dict=None	,							_UpperCamelCase   :					Any=None	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					Optional[int]=None	,							_UpperCamelCase   :					Tuple=-1	,							_UpperCamelCase   :					int=False	,							)					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_layers
												try:
																	SCREAMING_SNAKE_CASE    			=       self.roberta(
																	    _UpperCamelCase	,							attention_mask=_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							position_ids=_UpperCamelCase	,							head_mask=_UpperCamelCase	,							inputs_embeds=_UpperCamelCase	,							)

																	SCREAMING_SNAKE_CASE    			=       outputs[1]

																	SCREAMING_SNAKE_CASE    			=       self.dropout(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       self.classifier(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       (logits,) + outputs[2:]  # add hidden states and attention if they are here
												except HighwayException as e:
																	SCREAMING_SNAKE_CASE    			=       e.message
																	SCREAMING_SNAKE_CASE    			=       e.exit_layer
																	SCREAMING_SNAKE_CASE    			=       outputs[0]

												if not self.training:
																	SCREAMING_SNAKE_CASE    			=       entropy(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       []
																	SCREAMING_SNAKE_CASE    			=       []
												if labels is not None:
																	if self.num_labels == 1:
																						#  We are doing regression
																						SCREAMING_SNAKE_CASE    			=       MSELoss()
																						SCREAMING_SNAKE_CASE    			=       loss_fct(logits.view(-1      )	,							labels.view(-1      )      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       CrossEntropyLoss()
																						SCREAMING_SNAKE_CASE    			=       loss_fct(logits.view(-1	,							self.num_labels      )	,							labels.view(-1      )      )

																	# work with highway exits
																	SCREAMING_SNAKE_CASE    			=       []
																	for highway_exit in outputs[-1]:
																						SCREAMING_SNAKE_CASE    			=       highway_exit[0]
																						if not self.training:
																											highway_logits_all.append(_UpperCamelCase      )
																											highway_entropy.append(highway_exit[2]      )
																						if self.num_labels == 1:
																											#  We are doing regression
																											SCREAMING_SNAKE_CASE    			=       MSELoss()
																											SCREAMING_SNAKE_CASE    			=       loss_fct(highway_logits.view(-1      )	,							labels.view(-1      )      )
																						else:
																											SCREAMING_SNAKE_CASE    			=       CrossEntropyLoss()
																											SCREAMING_SNAKE_CASE    			=       loss_fct(highway_logits.view(-1	,							self.num_labels      )	,							labels.view(-1      )      )
																						highway_losses.append(_UpperCamelCase      )

																	if train_highway:
																						SCREAMING_SNAKE_CASE    			=       (sum(highway_losses[:-1]      ),) + outputs
																						# exclude the final highway, of course
																	else:
																						SCREAMING_SNAKE_CASE    			=       (loss,) + outputs
												if not self.training:
																	SCREAMING_SNAKE_CASE    			=       outputs + ((original_entropy, highway_entropy), exit_layer)
																	if output_layer >= 0:
																						SCREAMING_SNAKE_CASE    			=       (
																						    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																						)  # use the highway of the last layer

												return outputs  # (loss), logits, (hidden_states), (attentions), entropy





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




