
import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


lowercase__		:Optional[Any]			  =   logging.get_logger(__name__)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						nn.ModuleList([src_layers[i] for i in layers_to_copy]		)
					assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		), f'{len(lowerCAmelCase__		)} != {len(lowerCAmelCase__		)}'
					dest_layers.load_state_dict(layers_to_copy.state_dict()		)


lowercase__		:Dict			  =   {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowercase__		:List[str]			  =   {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					try:
										lowercase						     =						LAYERS_TO_COPY[n_teacher][n_student]
										return val
					except KeyError:
										if n_student != n_teacher:
															warnings.warn(
															    f'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
															    f' {n_student}'		)
										return list(range(lowerCAmelCase__		)		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if n_student > n_teacher:
										raise ValueError(f'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'		)
					elif n_teacher == n_student:
										return list(range(lowerCAmelCase__		)		)
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = "student"			,      lowerCAmelCase__ = None			,      lowerCAmelCase__ = None			,      lowerCAmelCase__=False			,      lowerCAmelCase__=None			,      lowerCAmelCase__=None			,      **lowerCAmelCase__			,      ):
					'''simple docstring'''
					lowercase						     =						'''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
					assert (e is not None) or (d is not None), _msg
					if isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
										AutoTokenizer.from_pretrained(lowerCAmelCase__		).save_pretrained(lowerCAmelCase__		)  # purely for convenience
										lowercase						     =						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__		).eval()
					else:
										assert isinstance(lowerCAmelCase__			,      lowerCAmelCase__		), f'teacher must be a model or string got type {type(lowerCAmelCase__		)}'
					lowercase						     =						teacher.config.to_diff_dict()

					try:
										lowercase     ,						lowercase						     =						teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															lowercase						     =						teacher_e
										if d is None:
															lowercase						     =						teacher_d
										init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}		)
					except AttributeError:  # T5
										if hasattr(teacher.config			,      '''num_encoder_layers'''		):
															lowercase     ,						lowercase						     =						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															lowercase     ,						lowercase						     =						teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															lowercase						     =						teacher_e
										if d is None:
															lowercase						     =						teacher_d
										if hasattr(teacher.config			,      '''num_encoder_layers'''		):
															init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}		)
										else:
															init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}		)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
					init_kwargs.update(lowerCAmelCase__		)

					# Copy weights
					lowercase						     =						teacher.config_class(**lowerCAmelCase__		)
					lowercase						     =						AutoModelForSeqaSeqLM.from_config(lowerCAmelCase__		)
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					lowercase						     =						student.load_state_dict(teacher.state_dict()			,      strict=lowerCAmelCase__		)
					assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

					if copy_first_teacher_layers:  # Our copying is done. We just log and save
										lowercase     ,						lowercase						     =						list(range(lowerCAmelCase__		)		), list(range(lowerCAmelCase__		)		)
										logger.info(
										    f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
										    f' {save_path}'		)
										student.save_pretrained(lowerCAmelCase__		)
										return student, e_layers_to_copy, d_layers_to_copy

					# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
					if e_layers_to_copy is None:
										lowercase						     =						pick_layers_to_copy(lowerCAmelCase__			,      lowerCAmelCase__		)
					if d_layers_to_copy is None:
										lowercase						     =						pick_layers_to_copy(lowerCAmelCase__			,      lowerCAmelCase__		)

					try:
										if hasattr(
										    lowerCAmelCase__			,      '''prophetnet'''		):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers			,      student.prophetnet.encoder.layers			,      lowerCAmelCase__		)
															copy_layers(teacher.prophetnet.decoder.layers			,      student.prophetnet.decoder.layers			,      lowerCAmelCase__		)
										else:
															copy_layers(teacher.model.encoder.layers			,      student.model.encoder.layers			,      lowerCAmelCase__		)
															copy_layers(teacher.model.decoder.layers			,      student.model.decoder.layers			,      lowerCAmelCase__		)
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block			,      student.encoder.block			,      lowerCAmelCase__		)
										copy_layers(teacher.decoder.block			,      student.decoder.block			,      lowerCAmelCase__		)
					logger.info(
					    f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'		)
					lowercase						     =						{
					    '''teacher_type''': teacher.config.model_type,
					    '''copied_encoder_layers''': e_layers_to_copy,
					    '''copied_decoder_layers''': d_layers_to_copy,
					}
					student.save_pretrained(lowerCAmelCase__		)
					# Save information about copying for easier reproducibility

					return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
							fire.Fire(create_student_by_copying_alternating_layers)






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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						torch.exp(lowerCAmelCase__		)
					lowercase						     =						torch.sum(lowerCAmelCase__			,      dim=1		)  # sum of exp(x_i)
					lowercase						     =						torch.sum(x * exp_x			,      dim=1		)  # sum of x_i * exp(x_i)
					return torch.log(lowerCAmelCase__		) - B / A







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						config.output_attentions
												lowercase						     =						config.output_hidden_states
												lowercase						     =						nn.ModuleList([BertLayer(A__) for _ in range(config.num_hidden_layers)])
												lowercase						     =						nn.ModuleList([BertHighway(A__) for _ in range(config.num_hidden_layers)])

												lowercase						     =						[-1 for _ in range(config.num_hidden_layers)]




							def 			A__				(    self       ,A__):
												if (type(A__) is float) or (type(A__) is int):
																	for i in range(len(self.early_exit_entropy)):
																						lowercase						     =						x
												else:
																	lowercase						     =						x




							def 			A__				(    self       ,A__):
												lowercase						     =						pooler.state_dict()
												for highway in self.highway:
																	for name, param in highway.pooler.state_dict().items():
																						param.copy_(loaded_model[name])





							def 			A__				(    self       ,A__       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												lowercase						     =						()
												lowercase						     =						()
												lowercase						     =						()
												for i, layer_module in enumerate(self.layer):
																	if self.output_hidden_states:
																						lowercase						     =						all_hidden_states + (hidden_states,)

																	lowercase						     =						layer_module(
																	    A__       ,A__       ,head_mask[i]       ,A__       ,A__)
																	lowercase						     =						layer_outputs[0]

																	if self.output_attentions:
																						lowercase						     =						all_attentions + (layer_outputs[1],)

																	lowercase						     =						(hidden_states,)
																	if self.output_hidden_states:
																						lowercase						     =						current_outputs + (all_hidden_states,)
																	if self.output_attentions:
																						lowercase						     =						current_outputs + (all_attentions,)

																	lowercase						     =						self.highway[i](A__)
																	# logits, pooled_output

																	if not self.training:
																						lowercase						     =						highway_exit[0]
																						lowercase						     =						entropy(A__)
																						lowercase						     =						highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																						lowercase						     =						all_highway_exits + (highway_exit,)

																						if highway_entropy < self.early_exit_entropy[i]:
																											lowercase						     =						(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																											raise HighwayException(A__       ,i + 1)
																	else:
																						lowercase						     =						all_highway_exits + (highway_exit,)

        # Add last layer
												if self.output_hidden_states:
																	lowercase						     =						all_hidden_states + (hidden_states,)

												lowercase						     =						(hidden_states,)
												if self.output_hidden_states:
																	lowercase						     =						outputs + (all_hidden_states,)
												if self.output_attentions:
																	lowercase						     =						outputs + (all_attentions,)

												lowercase						     =						outputs + (all_highway_exits,)
												return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__):
												super().__init__(A__)
												lowercase						     =						config

												lowercase						     =						BertEmbeddings(A__)
												lowercase						     =						DeeBertEncoder(A__)
												lowercase						     =						BertPooler(A__)

												self.init_weights()




							def 			A__				(    self):
												self.encoder.init_highway_pooler(self.pooler)




							def 			A__				(    self):
												return self.embeddings.word_embeddings




							def 			A__				(    self       ,A__):
												lowercase						     =						value




							def 			A__				(    self       ,A__):
												for layer, heads in heads_to_prune.items():
																	self.encoder.layer[layer].attention.prune_heads(A__)





							@add_start_docstrings_to_model_forward(A__)
							def 			A__				(    self       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												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(A__       ,device=A__)
												if encoder_attention_mask is None:
																	lowercase						     =						torch.ones(A__       ,device=A__)
												if token_type_ids is None:
																	lowercase						     =						torch.zeros(A__       ,dtype=torch.long       ,device=A__)

												# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
												# ourselves in which case we just need to make it broadcastable to all heads.
												lowercase						     =						self.get_extended_attention_mask(A__       ,A__       ,A__)

												# If a 2D ou 3D attention mask is provided for the cross-attention
												# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
												if encoder_attention_mask.dim() == 3:
																	lowercase						     =						encoder_attention_mask[:, None, :, :]
												if encoder_attention_mask.dim() == 2:
																	lowercase						     =						encoder_attention_mask[:, None, None, :]

												lowercase						     =						encoder_extended_attention_mask.to(
												    dtype=next(self.parameters()).dtype)  # fp16 compatibility
												lowercase						     =						(1.0 - encoder_extended_attention_mask) * -10000.0

												# Prepare head mask if needed
												# 1.0 in head_mask indicate we keep the head
												# attention_probs has shape bsz x n_heads x N x N
												# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
												# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
												lowercase						     =						self.get_head_mask(A__       ,self.config.num_hidden_layers)

												lowercase						     =						self.embeddings(
												    input_ids=A__       ,position_ids=A__       ,token_type_ids=A__       ,inputs_embeds=A__)
												lowercase						     =						self.encoder(
												    A__       ,attention_mask=A__       ,head_mask=A__       ,encoder_hidden_states=A__       ,encoder_attention_mask=A__       ,)
												lowercase						     =						encoder_outputs[0]
												lowercase						     =						self.pooler(A__)

												lowercase						     =						(
												    sequence_output,
												    pooled_output,
												) + encoder_outputs[
												    1:
												]  # add hidden_states and attentions if they are here
												return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__):
												lowercase						     =						message
												lowercase						     =						exit_layer  # start from 1!







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						BertPooler(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,config.num_labels)





							def 			A__				(    self       ,A__):
												# Pooler
												lowercase						     =						encoder_outputs[0]
												lowercase						     =						self.pooler(A__)
												# "return" pooler_output

												# BertModel
												lowercase						     =						(pooler_input, pooler_output) + encoder_outputs[1:]
												# "return" bmodel_output

												# Dropout and classification
												lowercase						     =						bmodel_output[1]

												lowercase						     =						self.dropout(A__)
												lowercase						     =						self.classifier(A__)

												return logits, pooled_output







@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''	,  SCREAMING_SNAKE_CASE__	,  )
class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__):
												super().__init__(A__)
												lowercase						     =						config.num_labels
												lowercase						     =						config.num_hidden_layers

												lowercase						     =						DeeBertModel(A__)
												lowercase						     =						nn.Dropout(config.hidden_dropout_prob)
												lowercase						     =						nn.Linear(config.hidden_size       ,self.config.num_labels)

												self.init_weights()





							@add_start_docstrings_to_model_forward(A__)
							def 			A__				(    self       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=-1       ,A__=False       ,):
												lowercase						     =						self.num_layers
												try:
																	lowercase						     =						self.bert(
																	    A__       ,attention_mask=A__       ,token_type_ids=A__       ,position_ids=A__       ,head_mask=A__       ,inputs_embeds=A__       ,)
																	# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																	lowercase						     =						outputs[1]

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

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

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

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

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




