



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   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







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

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

																					_lowerCAmelCase     :							Dict    =    self.dropout(__a)
																					_lowerCAmelCase     :							Dict    =    self.classifier(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
														except HighwayException as e:
																					_lowerCAmelCase     :							Tuple    =    e.message
																					_lowerCAmelCase     :							Union[str, Any]    =    e.exit_layer
																					_lowerCAmelCase     :							List[Any]    =    outputs[0]

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

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    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
																																			_lowerCAmelCase     :							List[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

																					if train_highway:
																												_lowerCAmelCase     :							int    =    (sum(highway_losses[:-1]),) + outputs
																												# exclude the final highway, of course
																					else:
																												_lowerCAmelCase     :							Any    =    (loss,) + outputs
														if not self.training:
																					_lowerCAmelCase     :							Optional[Any]    =    outputs + ((original_entropy, highway_entropy), exit_layer)
																					if output_layer >= 0:
																												_lowerCAmelCase     :							Optional[Any]    =    (
																												    (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 os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case		=  get_tests_dir("fixtures/test_sentencepiece_bpe.model")



class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		BartphoTokenizer
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase     :							str    =    ["▁This", "▁is", "▁a", "▁t", "est"]
														_lowerCAmelCase     :							List[str]    =    dict(zip(__a,			range(len(__a))))
														_lowerCAmelCase     :							Optional[Any]    =    {"unk_token": "<unk>"}

														_lowerCAmelCase     :							Optional[int]    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["monolingual_vocab_file"])
														with open(self.monolingual_vocab_file,			"w",			encoding="utf-8") as fp:
																					for token in vocab_tokens:
																												fp.write(f"{token} {vocab_tokens[token]}\n")

														_lowerCAmelCase     :							Optional[Any]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return BartphoTokenizer.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "This is a là test"
														_lowerCAmelCase     :							Optional[int]    =    "This is a<unk><unk> test"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														_lowerCAmelCase     :							List[Any]    =    "This is a là test"
														_lowerCAmelCase     :							str    =    "▁This ▁is ▁a ▁l à ▁t est".split()
														_lowerCAmelCase     :							str    =    tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Tuple    =    tokens + [tokenizer.unk_token]
														_lowerCAmelCase     :							List[str]    =    [4, 5, 6, 3, 3, 7, 8, 3]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),			__a)
