






"""simple docstring"""






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). """    ,							_lowerCamelCase    ,							)
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							str   			=					RobertaConfig
	SCREAMING_SNAKE_CASE_:							List[str]   			=					"""roberta"""


	def __init__(      self  ,					__a						):


					"""simple docstring"""

					super().__init__(__a						)

					A__							 =       RobertaEmbeddings(__a						)
					self.init_weights()





@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """    ,							_lowerCamelCase    ,							)
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Dict   			=					RobertaConfig
	SCREAMING_SNAKE_CASE_:							str   			=					"""roberta"""


	def __init__(      self  ,					__a						):


					"""simple docstring"""

					super().__init__(__a						)
					A__							 =       config.num_labels
					A__							 =       config.num_hidden_layers

					A__							 =       DeeRobertaModel(__a						)
					A__							 =       nn.Dropout(config.hidden_dropout_prob						)
					A__							 =       nn.Linear(config.hidden_size  ,					self.config.num_labels						)






	@add_start_docstrings_to_model_forward(__a						)
	def       _UpperCAmelCase						(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=-1  ,					__a=False  ,					):


					"""simple docstring"""

					A__							 =       self.num_layers
					try:
									A__							 =       self.roberta(
									    __a  ,					attention_mask=__a  ,					token_type_ids=__a  ,					position_ids=__a  ,					head_mask=__a  ,					inputs_embeds=__a  ,					)

									A__							 =       outputs[1]

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

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

									# work with highway exits
									A__							 =       []
									for highway_exit in outputs[-1]:
													A__							 =       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
																	A__							 =       MSELoss()
																	A__							 =       loss_fct(highway_logits.view(-1						)  ,					labels.view(-1						)						)
													else:
																	A__							 =       CrossEntropyLoss()
																	A__							 =       loss_fct(highway_logits.view(-1  ,					self.num_labels						)  ,					labels.view(-1						)						)
													highway_losses.append(__a						)

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

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










"""simple docstring"""






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.getLogger(__name__)



def  __lowerCamelCase					(					):
				A__							 =       argparse.ArgumentParser(
				    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'				)
				parser.add_argument('--file_path'       ,type=lowerCAmelCase__       ,default='data/dump.txt'       ,help='The path to the data.'				)
				parser.add_argument('--tokenizer_type'       ,type=lowerCAmelCase__       ,default='bert'       ,choices=['bert', 'roberta', 'gpt2']				)
				parser.add_argument('--tokenizer_name'       ,type=lowerCAmelCase__       ,default='bert-base-uncased'       ,help='The tokenizer to use.'				)
				parser.add_argument('--dump_file'       ,type=lowerCAmelCase__       ,default='data/dump'       ,help='The dump file prefix.'				)
				A__							 =       parser.parse_args()

				logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''				)
				if args.tokenizer_type == "bert":
								A__							 =       BertTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['cls_token']  # `[CLS]`
								A__							 =       tokenizer.special_tokens_map['sep_token']  # `[SEP]`
				elif args.tokenizer_type == "roberta":
								A__							 =       RobertaTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['cls_token']  # `<s>`
								A__							 =       tokenizer.special_tokens_map['sep_token']  # `</s>`
				elif args.tokenizer_type == "gpt2":
								A__							 =       GPTaTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
								A__							 =       tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

				logger.info(f'''Loading text from {args.file_path}'''				)
				with open(args.file_path       ,'r'       ,encoding='utf8'				) as fp:
								A__							 =       fp.readlines()

				logger.info('Start encoding'				)
				logger.info(f'''{len(lowerCAmelCase__				)} examples to process.'''				)

				A__							 =       []
				A__							 =       0
				A__							 =       1_0000
				A__							 =       time.time()
				for text in data:
								A__							 =       f'''{bos} {text.strip()} {sep}'''
								A__							 =       tokenizer.encode(lowerCAmelCase__       ,add_special_tokens=lowerCAmelCase__				)
								rslt.append(lowerCAmelCase__				)

								iter += 1
								if iter % interval == 0:
												A__							 =       time.time()
												logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''				)
												A__							 =       time.time()
				logger.info('Finished binarization'				)
				logger.info(f'''{len(lowerCAmelCase__				)} examples processed.'''				)

				A__							 =       f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
				A__							 =       tokenizer.vocab_size
				if vocab_size < (1 << 16):
								A__							 =       [np.uintaa(lowerCAmelCase__				) for d in rslt]
				else:
								A__							 =       [np.intaa(lowerCAmelCase__				) for d in rslt]
				random.shuffle(rslt_				)
				logger.info(f'''Dump to {dp_file}'''				)
				with open(lowerCAmelCase__       ,'wb'				) as handle:
								pickle.dump(rslt_       ,lowerCAmelCase__       ,protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
					main()


