


from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None

class 						A__							(		UpperCamelCase__  ):




						def __init__(       self					:      Optional[Any]				,    _a					:      Optional[int]=1				,    _a					:      int=0				,    _a					:      Tuple=2				,    _a					:      Optional[int]=512				,    _a					:      Union[str, Any]="cls"				,    _a					:      Any=False				,    _a					:      Tuple=True				,    **_a					:      str				,    )							->     Any:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )
										_SCREAMING_SNAKE_CASE			  =project_dim
										_SCREAMING_SNAKE_CASE			  =pooler_fn
										_SCREAMING_SNAKE_CASE			  =learn_encoder
										_SCREAMING_SNAKE_CASE			  =use_attention_mask

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						[R"pooler", R"logit_scale"]
						UpperCAmelCase             =						[R"position_ids", R"predictions.decoder.bias"]
						UpperCAmelCase             =						"roberta"
						UpperCAmelCase             =						RobertaSeriesConfig




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


										"""simple docstring"""





										super().__init__(_a  )
										_SCREAMING_SNAKE_CASE			  =XLMRobertaModel(_a  )
										_SCREAMING_SNAKE_CASE			  =nn.Linear(config.hidden_size				,    config.project_dim  )
										_SCREAMING_SNAKE_CASE			  =getattr(_a				,    '''has_pre_transformation'''				,    _a  )
										if self.has_pre_transformation:
														_SCREAMING_SNAKE_CASE			  =nn.Linear(config.hidden_size				,    config.project_dim  )
														_SCREAMING_SNAKE_CASE			  =nn.LayerNorm(config.hidden_size				,    eps=config.layer_norm_eps  )
										self.post_init()




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[torch.Tensor] = None				,    _a					:      Optional[bool] = None				,    _a					:      Optional[bool] = None				,    _a					:      Optional[bool] = None				,    )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =return_dict if return_dict is not None else self.config.use_return_dict

										_SCREAMING_SNAKE_CASE			  =self.base_model(
										    input_ids=_a				,    attention_mask=_a				,    token_type_ids=_a				,    position_ids=_a				,    head_mask=_a				,    inputs_embeds=_a				,    encoder_hidden_states=_a				,    encoder_attention_mask=_a				,    output_attentions=_a				,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states				,    return_dict=_a				,    )

										if self.has_pre_transformation:
														_SCREAMING_SNAKE_CASE			  =outputs['''hidden_states'''][-2]
														_SCREAMING_SNAKE_CASE			  =self.pre_LN(_a  )
														_SCREAMING_SNAKE_CASE			  =self.transformation_pre(_a  )

														return TransformationModelOutput(
														    projection_state=_a				,    last_hidden_state=outputs.last_hidden_state				,    hidden_states=outputs.hidden_states				,    attentions=outputs.attentions				,    )
										else:
														_SCREAMING_SNAKE_CASE			  =self.transformation(outputs.last_hidden_state  )
														return TransformationModelOutput(
														    projection_state=_a				,    last_hidden_state=outputs.last_hidden_state				,    hidden_states=outputs.hidden_states				,    attentions=outputs.attentions				,    )



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )