"""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). "   ,					__UpperCAmelCase   ,					)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				RobertaConfig
					snake_case__       					=				"roberta"







					def __init__(       self     :			List[str]					,lowerCamelCase__     :			Dict    ):
									super().__init__(lowerCamelCase__    )

									UpperCAmelCase__    =							RobertaEmbeddings(lowerCamelCase__    )
									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. "   ,					__UpperCAmelCase   ,					)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				RobertaConfig
					snake_case__       					=				"roberta"







					def __init__(       self     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]    ):
									super().__init__(lowerCamelCase__    )
									UpperCAmelCase__    =							config.num_labels
									UpperCAmelCase__    =							config.num_hidden_layers

									UpperCAmelCase__    =							DeeRobertaModel(lowerCamelCase__    )
									UpperCAmelCase__    =							nn.Dropout(config.hidden_dropout_prob    )
									UpperCAmelCase__    =							nn.Linear(config.hidden_size					,self.config.num_labels    )







					@add_start_docstrings_to_model_forward(lowerCamelCase__    )
					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Union[str, Any]=None					,lowerCamelCase__     :			List[str]=None					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Optional[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int=-1					,lowerCamelCase__     :			Any=False					,):
									UpperCAmelCase__    =							self.num_layers
									try:
													UpperCAmelCase__    =							self.roberta(
													    lowerCamelCase__					,attention_mask=lowerCamelCase__					,token_type_ids=lowerCamelCase__					,position_ids=lowerCamelCase__					,head_mask=lowerCamelCase__					,inputs_embeds=lowerCamelCase__					,)

													UpperCAmelCase__    =							outputs[1]

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

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

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

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

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__						:						Any		  =			get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				XLMProphetNetTokenizer
					snake_case__       					=				False
					snake_case__       					=				True







					def 	__lowerCAmelCase		(       self     :			Any    ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									UpperCAmelCase__    =							XLMProphetNetTokenizer(lowerCamelCase__					,keep_accents=lowerCamelCase__    )
									tokenizer.save_pretrained(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							'[PAD]'
									UpperCAmelCase__    =							0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__    )					,lowerCamelCase__    )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__    )					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							list(self.get_tokenizer().get_vocab().keys()    )

									self.assertEqual(vocab_keys[0]					,'[PAD]'    )
									self.assertEqual(vocab_keys[1]					,'[CLS]'    )
									self.assertEqual(vocab_keys[-1]					,'j'    )
									self.assertEqual(len(lowerCamelCase__    )					,1_012    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									self.assertEqual(self.get_tokenizer().vocab_size					,1_012    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							XLMProphetNetTokenizer(lowerCamelCase__					,keep_accents=lowerCamelCase__    )

									UpperCAmelCase__    =							tokenizer.tokenize('This is a test'    )
									self.assertListEqual(lowerCamelCase__					,['▁This', '▁is', '▁a', '▁t', 'est']    )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCamelCase__    )					,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,)

									UpperCAmelCase__    =							tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
									self.assertListEqual(
									    lowerCamelCase__					,[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '9',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        'é',
									        '.',
									    ]					,)
									UpperCAmelCase__    =							tokenizer.convert_tokens_to_ids(lowerCamelCase__    )
									self.assertListEqual(
									    lowerCamelCase__					,[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
									    ]					,)

									UpperCAmelCase__    =							tokenizer.convert_ids_to_tokens(lowerCamelCase__    )
									self.assertListEqual(
									    lowerCamelCase__					,[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '[UNK]',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        '[UNK]',
									        '.',
									    ]					,)







					@cached_property
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							'Hello World!'
									UpperCAmelCase__    =							[35_389, 6_672, 49, 2]
									self.assertListEqual(lowerCamelCase__					,self.big_tokenizer.encode(lowerCamelCase__    )    )







					@slow
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									# fmt: off
									UpperCAmelCase__    =							{'input_ids': [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCamelCase__					,model_name='microsoft/xprophetnet-large-wiki100-cased'					,revision='1acad1643ddd54a44df6a1b797ada8373685d90e'					,)



