


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    UpperCAmelCase					(       __snake_case    ):
			lowercase						= None
			lowercase						= None
			lowercase						= None
			lowercase						= None




class    UpperCAmelCase					(       __snake_case    ):




			def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]=1       ,       __magic_name__							:		List[Any]=0       ,       __magic_name__							:		Optional[Any]=2       ,       __magic_name__							:		List[Any]=5_1_2       ,       __magic_name__							:		Union[str, Any]="cls"       ,       __magic_name__							:		Any=False       ,       __magic_name__							:		str=True       ,       **__magic_name__							:		Optional[int]       ,       ):







						"""simple docstring"""







						super().__init__(pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					project_dim
						UpperCamelCase         =					pooler_fn
						UpperCamelCase         =					learn_encoder
						UpperCamelCase         =					use_attention_mask




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




			def __init__(						self							:		List[str]       ,       __magic_name__							:		Dict						):







						"""simple docstring"""







						super().__init__(__magic_name__						)
						UpperCamelCase         =					XLMRobertaModel(__magic_name__						)
						UpperCamelCase         =					nn.Linear(config.hidden_size       ,       config.project_dim						)
						UpperCamelCase         =					getattr(__magic_name__       ,       """has_pre_transformation"""       ,       __magic_name__						)
						if self.has_pre_transformation:
									UpperCamelCase         =					nn.Linear(config.hidden_size       ,       config.project_dim						)
									UpperCamelCase         =					nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps						)
						self.post_init()





			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[torch.Tensor] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       ):







						"""simple docstring"""







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

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

						if self.has_pre_transformation:
									UpperCamelCase         =					outputs["""hidden_states"""][-2]
									UpperCamelCase         =					self.pre_LN(__magic_name__						)
									UpperCamelCase         =					self.transformation_pre(__magic_name__						)

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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




