






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_mbartaa import MBartaaTokenizer
else:
							A__     :      Dict             =			None


A__     :      Optional[int]             =			logging.get_logger(__name__)

A__     :      Dict             =			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A__     :      Union[str, Any]             =			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

A__     :      Tuple             =			{
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
A__     :      Union[str, Any]             =			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']
class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	VOCAB_FILES_NAMES
				lowercase__    							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__    							=	PRETRAINED_VOCAB_FILES_MAP
				lowercase__    							=	["""input_ids""", """attention_mask"""]
				lowercase__    							=	MBartaaTokenizer

				lowercase__    							=	[]
				lowercase__    							=	[]




				def __init__(					self						:   Optional[Any],  lowerCamelCase						:   List[Any]=None,  lowerCamelCase						:   Optional[int]=None,  lowerCamelCase						:   Tuple=None,  lowerCamelCase						:   Dict=None,  lowerCamelCase						:   Dict="</s>",  lowerCamelCase						:   Dict="</s>",  lowerCamelCase						:   str="<s>",  lowerCamelCase						:   int="<unk>",  lowerCamelCase						:   int="<pad>",  lowerCamelCase						:   int="<mask>",  **lowerCamelCase						:   Union[str, Any],  ):

								'''simple docstring'''
								# Mask token behave like a normal word, i.e. include the space before it
								lowercase__       			=      AddedToken(lowerCamelCase,  lstrip=lowerCamelCase,  rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,  lowerCamelCase  ) else mask_token

								lowercase__       			=      kwargs.get('''additional_special_tokens''',  []  )
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    lowerCamelCase,  src_lang=lowerCamelCase,  tgt_lang=lowerCamelCase,  tokenizer_file=lowerCamelCase,  eos_token=lowerCamelCase,  sep_token=lowerCamelCase,  cls_token=lowerCamelCase,  unk_token=lowerCamelCase,  pad_token=lowerCamelCase,  mask_token=lowerCamelCase,  **lowerCamelCase,  )

								lowercase__       			=      vocab_file
								lowercase__       			=      False if not self.vocab_file else True

								lowercase__       			=      {
								    lang_code: self.convert_tokens_to_ids(lowerCamelCase  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								lowercase__       			=      src_lang if src_lang is not None else '''en_XX'''
								lowercase__       			=      tgt_lang
								lowercase__       			=      self.lang_code_to_id[self._src_lang]
								self.set_src_lang_special_tokens(self._src_lang  )




				@property
				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								return self._src_lang




				@src_lang.setter
				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      new_src_lang
								self.set_src_lang_special_tokens(self._src_lang  )




				def     lowercase__       (					self						:   Dict,  lowerCamelCase						:   List[int],  lowerCamelCase						:   Optional[List[int]] = None  ):

								'''simple docstring'''
								if token_ids_a is None:
												return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




				def     lowercase__       (					self						:   Dict,  lowerCamelCase						:   List[str],  lowerCamelCase						:   str = "en_XX",  lowerCamelCase						:   Optional[List[str]] = None,  lowerCamelCase						:   str = "ro_RO",  **lowerCamelCase						:   Optional[int],  ):

								'''simple docstring'''
								lowercase__       			=      src_lang
								lowercase__       			=      tgt_lang
								return super().prepare_seqaseq_batch(lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								return self.set_src_lang_special_tokens(self.src_lang  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								return self.set_tgt_lang_special_tokens(self.tgt_lang  )




				def     lowercase__       (					self						:   Dict,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.convert_tokens_to_ids(lowerCamelCase  )
								lowercase__       			=      [self.cur_lang_code_id]
								lowercase__       			=      [self.eos_token_id]

								lowercase__       			=      self.convert_ids_to_tokens(self.prefix_tokens  )
								lowercase__       			=      self.convert_ids_to_tokens(self.suffix_tokens  )

								lowercase__       			=      processors.TemplateProcessing(
								    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,  pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,  self.prefix_tokens + self.suffix_tokens  )  ),  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.convert_tokens_to_ids(lowerCamelCase  )
								lowercase__       			=      [self.cur_lang_code_id]
								lowercase__       			=      [self.eos_token_id]

								lowercase__       			=      self.convert_ids_to_tokens(self.prefix_tokens  )
								lowercase__       			=      self.convert_ids_to_tokens(self.suffix_tokens  )

								lowercase__       			=      processors.TemplateProcessing(
								    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,  pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,  self.prefix_tokens + self.suffix_tokens  )  ),  )




				def     lowercase__       (					self						:   int,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[str],  lowerCamelCase						:   Optional[str],  **lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								if src_lang is None or tgt_lang is None:
												raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
								lowercase__       			=      src_lang
								lowercase__       			=      self(lowerCamelCase,  add_special_tokens=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase  )
								lowercase__       			=      self.convert_tokens_to_ids(lowerCamelCase  )
								lowercase__       			=      tgt_lang_id
								return inputs




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[str] = None  ):

								'''simple docstring'''
								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''  )

								if not os.path.isdir(lowerCamelCase  ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
												return
								lowercase__       			=      os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowerCamelCase  ):
												copyfile(self.vocab_file,  lowerCamelCase  )

								return (out_vocab_file,)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    RemBertConfig,
							    RemBertForCausalLM,
							    RemBertForMaskedLM,
							    RemBertForMultipleChoice,
							    RemBertForQuestionAnswering,
							    RemBertForSequenceClassification,
							    RemBertForTokenClassification,
							    RemBertModel,
							)
							from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Optional[int],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   int=13,  lowerCamelCase						:   str=7,  lowerCamelCase						:   int=True,  lowerCamelCase						:   Optional[Any]=True,  lowerCamelCase						:   List[str]=True,  lowerCamelCase						:   str=True,  lowerCamelCase						:   List[str]=99,  lowerCamelCase						:   Any=32,  lowerCamelCase						:   int=18,  lowerCamelCase						:   Optional[int]=43,  lowerCamelCase						:   Optional[Any]=5,  lowerCamelCase						:   int=4,  lowerCamelCase						:   Union[str, Any]=37,  lowerCamelCase						:   List[str]="gelu",  lowerCamelCase						:   List[str]=0.1,  lowerCamelCase						:   Dict=0.1,  lowerCamelCase						:   Dict=512,  lowerCamelCase						:   str=16,  lowerCamelCase						:   Optional[Any]=2,  lowerCamelCase						:   int=0.02,  lowerCamelCase						:   Optional[int]=3,  lowerCamelCase						:   Optional[int]=4,  lowerCamelCase						:   Union[str, Any]=None,  ):

								'''simple docstring'''
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      seq_length
								lowercase__       			=      is_training
								lowercase__       			=      use_input_mask
								lowercase__       			=      use_token_type_ids
								lowercase__       			=      use_labels
								lowercase__       			=      vocab_size
								lowercase__       			=      hidden_size
								lowercase__       			=      input_embedding_size
								lowercase__       			=      output_embedding_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      num_attention_heads
								lowercase__       			=      intermediate_size
								lowercase__       			=      hidden_act
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      max_position_embeddings
								lowercase__       			=      type_vocab_size
								lowercase__       			=      type_sequence_label_size
								lowercase__       			=      initializer_range
								lowercase__       			=      num_labels
								lowercase__       			=      num_choices
								lowercase__       			=      scope




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__       			=      None
								if self.use_input_mask:
												lowercase__       			=      random_attention_mask([self.batch_size, self.seq_length]  )

								lowercase__       			=      None
								if self.use_token_type_ids:
												lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.type_vocab_size  )

								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      None
								if self.use_labels:
												lowercase__       			=      ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__       			=      ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__       			=      RemBertConfig(
								    vocab_size=self.vocab_size,  hidden_size=self.hidden_size,  input_embedding_size=self.input_embedding_size,  output_embedding_size=self.output_embedding_size,  num_hidden_layers=self.num_hidden_layers,  num_attention_heads=self.num_attention_heads,  intermediate_size=self.intermediate_size,  hidden_act=self.hidden_act,  hidden_dropout_prob=self.hidden_dropout_prob,  attention_probs_dropout_prob=self.attention_probs_dropout_prob,  max_position_embeddings=self.max_position_embeddings,  type_vocab_size=self.type_vocab_size,  is_decoder=lowerCamelCase,  initializer_range=self.initializer_range,  )

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      self.prepare_config_and_inputs()

								lowercase__       			=      True
								lowercase__       			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  vocab_size=2  )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   int,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      RemBertModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   str,  lowerCamelCase						:   Any,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Any,  ):

								'''simple docstring'''
								lowercase__       			=      True
								lowercase__       			=      RemBertModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  )
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  )
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   str,  lowerCamelCase						:   str,  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any],  ):

								'''simple docstring'''
								lowercase__       			=      RemBertForCausalLM(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def     lowercase__       (					self						:   Any,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Any,  lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      RemBertForMaskedLM(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Any,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   int,  lowerCamelCase						:   Dict,  ):

								'''simple docstring'''
								lowercase__       			=      True
								lowercase__       			=      True
								lowercase__       			=      RemBertForCausalLM(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								# first forward pass
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  use_cache=lowerCamelCase,  )
								lowercase__       			=      outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowercase__       			=      ids_tensor((self.batch_size, 3),  config.vocab_size  )
								lowercase__       			=      ids_tensor((self.batch_size, 3),  vocab_size=2  )

								# append to next input_ids and
								lowercase__       			=      torch.cat([input_ids, next_tokens],  dim=-1  )
								lowercase__       			=      torch.cat([input_mask, next_mask],  dim=-1  )

								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  output_hidden_states=lowerCamelCase,  )['''hidden_states'''][0]
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  output_hidden_states=lowerCamelCase,  )['''hidden_states'''][0]

								# select random slice
								lowercase__       			=      ids_tensor((1,),  output_from_past.shape[-1]  ).item()
								lowercase__       			=      output_from_no_past[:, -3:, random_slice_idx].detach()
								lowercase__       			=      output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-3  )  )




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      RemBertForQuestionAnswering(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  start_positions=lowerCamelCase,  end_positions=lowerCamelCase,  )
								self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      RemBertForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      RemBertForTokenClassification(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)  )




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   Any,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   int,  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.num_choices
								lowercase__       			=      RemBertForMultipleChoice(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      input_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      token_type_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      input_mask.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase,  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.prepare_config_and_inputs()
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      config_and_inputs
								lowercase__       			=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(
				    (
				        RemBertModel,
				        RemBertForMaskedLM,
				        RemBertForCausalLM,
				        RemBertForMultipleChoice,
				        RemBertForQuestionAnswering,
				        RemBertForSequenceClassification,
				        RemBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__    							=	(RemBertForCausalLM,) if is_torch_available() else ()
				lowercase__    							=	(
				    {
				        """feature-extraction""": RemBertModel,
				        """fill-mask""": RemBertForMaskedLM,
				        """question-answering""": RemBertForQuestionAnswering,
				        """text-classification""": RemBertForSequenceClassification,
				        """text-generation""": RemBertForCausalLM,
				        """token-classification""": RemBertForTokenClassification,
				        """zero-shot""": RemBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      RemBertModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												lowercase__       			=      type
												self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase  )




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								# This regression test was failing with PyTorch < 1.3
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      self.model_tester.prepare_config_and_inputs_for_decoder()

								lowercase__       			=      None

								self.model_tester.create_and_check_model_as_decoder(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  )




				@slow
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      RemBertModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )



@require_torch
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				@slow
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								# Test exact values at the last hidden layer
								lowercase__       			=      RemBertModel.from_pretrained('''google/rembert'''  )
								lowercase__       			=      torch.tensor([[312, 56_498, 313, 2_125, 313]]  )
								lowercase__       			=      torch.tensor([[0, 0, 0, 1, 1]]  )
								with torch.no_grad():
												lowercase__       			=      model(lowerCamelCase,  token_type_ids=lowerCamelCase,  output_hidden_states=lowerCamelCase  )

								lowercase__       			=      1_152

								lowercase__       			=      torch.Size((1, 5, hidden_size)  )
								self.assertEqual(output['''last_hidden_state'''].shape,  lowerCamelCase  )

								lowercase__       			=      torch.tensor(
								    [
								        [
								            [0.0754, -0.2022, 0.1904],
								            [-0.3354, -0.3692, -0.4791],
								            [-0.2314, -0.6729, -0.0749],
								            [-0.0396, -0.3105, -0.4234],
								            [-0.1571, -0.0525, 0.5353],
								        ]
								    ]  )

								# Running on the original tf implementation gives slightly different results here.
								# Not clear why this variations is present
								# TODO: Find reason for discrepancy
								# expected_original_implementation = [[
								#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
								#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
								#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
								#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
								#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
								# ]]

								self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3],  lowerCamelCase,  atol=1E-4  )  )




