code: 
  import argparse
  import gc
  import os
  import sys
  from pathlib import Path
  from typing import List  # noqa: F401
  
  import pytorch_lightning as pl
  import torch
  from finetune import SummarizationModule, TranslationModule
  from finetune import main as ft_main
  from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
  from torch import nn
  
  from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
  from transformers.models.bart.modeling_bart import shift_tokens_right
  from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params
  
  
  # need the parent dir module
  sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
  from lightning_base import generic_train  # noqa
  
  
  
  
  class      __lowercase	(	A_					):
  	_A   			=      ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
  
  
  
  
  
  
  	def __init__(self							:   List[str]			,  snake_case							:   Union[str, Any]						)   -> Tuple:
  							assert Path(hparams.data_dir						).exists()
  							_lowercase					:  List[str]    			=      Path(hparams.output_dir						)
  							self.output_dir.mkdir(exist_ok=snake_case						)
  
  							_lowercase					:  Optional[Any]    			=      self.output_dir.joinpath("student"						)
  
  							_lowercase					:  Optional[int]    			=      str(snake_case						)  # Tell lightning we are training the student
  							_lowercase					:  Optional[Any]    			=      AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher						).eval()
  							use_task_specific_params(snake_case			,  hparams.task						)  # We copy good generation parameters to student by default
  							if hparams.student is not None:
  													_lowercase					:  Optional[int]    			=      AutoModelForSeqaSeqLM.from_pretrained(hparams.student						)
  													use_task_specific_params(snake_case			,  hparams.task						)
  													_lowercase			,						_lowercase					:  Optional[Any]    			=      None, None
  							else:
  													_lowercase			,						_lowercase			,						_lowercase					:  List[Any]    			=      create_student_by_copying_alternating_layers(
  													    snake_case			,  e=hparams.student_encoder_layers			,  d=hparams.student_decoder_layers			,  save_path=snake_case						)
  
  							if hparams.length_penalty != -1:
  													_lowercase					:  int    			=      hparams.length_penalty
  							_lowercase					:  Optional[int]    			=      hparams.teacher  # Use teacher's tokenizer
  							super().__init__(snake_case			,  model=snake_case			,  config=student.config						)
  							assert student.config.model_type == teacher.config.model_type, (
  							    F"""teacher, student model types should be the same, got {student.config.model_type} !="""
  							    F""" {teacher.config.model_type}"""
  							)
  
  							if student.config.model_type == "t5":
  													_lowercase					:  List[Any]    			=      len(student.get_encoder().block						)
  													_lowercase					:  int    			=      len(student.get_decoder().block						)
  													_lowercase					:  str    			=      len(teacher.get_encoder().block						)
  													_lowercase					:  List[Any]    			=      len(teacher.get_decoder().block						)
  							else:
  													_lowercase					:  Optional[Any]    			=      student.config.encoder_layers
  													_lowercase					:  List[Any]    			=      student.config.decoder_layers
  													_lowercase					:  Optional[int]    			=      teacher.config.encoder_layers
  													_lowercase					:  Any    			=      teacher.config.decoder_layers
  
  							_lowercase					:  Union[str, Any]    			=      not (hparams.student is None or hparams.teacher == hparams.student)
  							_lowercase					:  int    			=      (not self.different_base_models) and hparams.alpha_hid > 0
  							_lowercase					:  Optional[int]    			=      self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
  							# self.different_encoder determines whether we need to run the teacher encoder
  							_lowercase					:  str    			=      teacher
  							freeze_params(self.teacher						)
  
  							if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
  													try:
  																			del self.teacher.model.encoder
  													except AttributeError:  # T5
  																			del self.teacher.encoder
  
  							if e_layer_ids is None:
  													_lowercase					:  List[str]    			=      list(range(snake_case						)						)
  							if d_layer_ids is None:
  													_lowercase					:  int    			=      list(range(snake_case						)						)
  
  							_lowercase			,						_lowercase					:  str    			=      e_layer_ids, d_layer_ids  # type: List[int], List[int]
  
  							if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
  													if hparams.supervise_forward:
  																			_lowercase					:  List[str]    			=      get_layers_to_supervise(
  																			    n_student=len(self.e_layer_ids						)			,  n_teacher=snake_case						)
  																			_lowercase					:  List[Any]    			=      get_layers_to_supervise(
  																			    n_student=len(self.d_layer_ids						)			,  n_teacher=snake_case						)
  													else:  # student layer should emulate hidden states of the teacher layer it was copied from
  																			_lowercase					:  List[str]    			=      self.e_layer_ids
  																			_lowercase					:  Optional[Any]    			=      self.d_layer_ids
  							else:
  													_lowercase					:  Optional[int]    			=      None
  													_lowercase					:  Any    			=      None
  
  							_lowercase					:  str    			=      nn.KLDivLoss(reduction="batchmean"						)
  							_lowercase					:  Tuple    			=      2.0
  							_lowercase					:  List[Any]    			=      hparams.alpha_mlm
  							_lowercase					:  Any    			=      hparams.alpha_ce
  							_lowercase					:  List[Any]    			=      hparams.alpha_hid
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   Optional[int]			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]						)   -> str:
  							_lowercase					:  List[str]    			=      mask[:, :, None].expand_as(snake_case						)
  							_lowercase					:  Any    			=      s_logits.size(-1						)
  							_lowercase					:  Union[str, Any]    			=      torch.masked_select(snake_case			,  snake_case						)  # (bs * seq_length * voc_size) modulo the 1s in mask
  							_lowercase					:  Optional[Any]    			=      torch.masked_select(snake_case			,  snake_case						)  # (bs * seq_length * voc_size) modulo the 1s in mask
  							_lowercase					:  List[Any]    			=      s_logits_slct.view(-1			,  snake_case						)  # (bs * seq_length, voc_size) modulo the 1s in mask
  							_lowercase					:  Optional[Any]    			=      t_logits_slct.view(-1			,  snake_case						)  # (bs * seq_length, voc_size) modulo the 1s in mask
  							assert t_logits_slct.size() == s_logits_slct.size()
  							_lowercase					:  List[str]    			=      (
  							    self.ce_loss_fct(
  							        nn.functional.log_softmax(s_logits_slct / self.temperature			,  dim=-1						)			,  nn.functional.softmax(t_logits_slct / self.temperature			,  dim=-1						)			,  )
  							    * (self.temperature) ** 2
  							)
  							return loss_ce
  
  
  
  
  
  
  	@staticmethod
  	def 			_a(snake_case							:   int			,  snake_case							:   Any						)   -> Any:
  							SummarizationModule.add_model_specific_args(snake_case			,  snake_case						)
  							add_distill_args(snake_case						)
  							return parser
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   Optional[int]						)   -> Any:
  							_lowercase					:  List[Any]    			=      self.tokenizer.pad_token_id
  							_lowercase			,						_lowercase			,						_lowercase					:  List[str]    			=      batch["input_ids"], batch["attention_mask"], batch["labels"]
  							if isinstance(self.model			,  snake_case						):
  													_lowercase					:  Optional[Any]    			=      self.model._shift_right(snake_case						)
  							else:
  													_lowercase					:  Optional[Any]    			=      shift_tokens_right(snake_case			,  snake_case						)
  
  							# noinspection PyCallingNonCallable
  							_lowercase					:  Any    			=      self(
  							    snake_case			,  attention_mask=snake_case			,  decoder_input_ids=snake_case			,  output_hidden_states=self.do_calc_hidden_loss			,  output_attentions=snake_case			,  use_cache=snake_case			,  )
  							_lowercase					:  Optional[int]    			=      student_outputs["logits"]
  
  							# Same cross entropy vs. label smoothing logic as finetune.py
  							assert lm_logits.shape[-1] == self.model.config.vocab_size
  							if self.hparams.label_smoothing == 0:
  													# Same behavior as modeling_bart.py, besides ignoring pad_token_id
  													_lowercase					:  str    			=      nn.CrossEntropyLoss(ignore_index=snake_case						)
  													_lowercase					:  Any    			=      loss_fct(lm_logits.view(-1			,  lm_logits.shape[-1]						)			,  labels.view(-1						)						)
  							else:
  													_lowercase					:  Optional[Any]    			=      nn.functional.log_softmax(snake_case			,  dim=-1						)
  													_lowercase			,						_lowercase					:  List[str]    			=      label_smoothed_nll_loss(
  													    snake_case			,  snake_case			,  self.hparams.label_smoothing			,  ignore_index=snake_case						)
  
  							def zero_tensor():
  													return torch.tensor(0.0						).type_as(snake_case						)
  
  							_lowercase					:  Tuple    			=      student_outputs[
  							    "encoder_last_hidden_state"
  							]  # use this unless self.different_base_models
  							_lowercase			,						_lowercase					:  Any    			=      zero_tensor(), zero_tensor()
  							if self.different_encoder:  # compute encoder hidden state loss
  													_lowercase					:  Any    			=      self.teacher.get_encoder()(
  													    snake_case			,  attention_mask=snake_case			,  output_hidden_states=self.do_calc_hidden_loss			,  )
  													if self.different_base_models:
  																			_lowercase					:  Optional[Any]    			=      all_teacher_encoder_outputs["last_hidden_state"]
  													elif self.do_calc_hidden_loss:
  																			_lowercase					:  Optional[int]    			=      self.calc_hidden_loss(
  																			    snake_case			,  student_outputs["encoder_hidden_states"]			,  all_teacher_encoder_outputs["hidden_states"]			,  self.e_matches			,  normalize_hidden=self.hparams.normalize_hidden			,  )
  
  							_lowercase					:  Any    			=      self.teacher(
  							    snake_case			,  attention_mask=snake_case			,  encoder_outputs=(teacher_enc_outputs,)			,  decoder_input_ids=snake_case			,  output_hidden_states=self.do_calc_hidden_loss			,  use_cache=snake_case			,  )
  							_lowercase					:  List[Any]    			=      decoder_input_ids.ne(snake_case						)
  							_lowercase					:  Optional[int]    			=      self.calc_ce_loss(snake_case			,  snake_case			,  teacher_outputs["logits"]						)
  							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  													_lowercase					:  Tuple    			=      self.calc_hidden_loss(
  													    snake_case			,  student_outputs["decoder_hidden_states"]			,  teacher_outputs["decoder_hidden_states"]			,  self.d_matches			,  normalize_hidden=self.hparams.normalize_hidden			,  )
  
  							_lowercase					:  int    			=      (
  							    self.alpha_ce * loss_ce
  							    + self.alpha_mlm * student_lm_loss
  							    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
  							)
  							return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec
  
  
  
  
  
  
  	@staticmethod
  	def 			_a(snake_case							:   Dict			,  snake_case							:   Union[str, Any]			,  snake_case							:   int			,  snake_case							:   str			,  snake_case							:   Dict						)   -> Optional[Any]:
  							_lowercase					:  Tuple    			=      "expected list or tuple for hidden_states, got tensor of shape: "
  							assert not isinstance(snake_case			,  torch.Tensor						), F"""{msg}{hidden_states.shape}"""
  							assert not isinstance(snake_case			,  torch.Tensor						), F"""{msg}{hidden_states_T.shape}"""
  							_lowercase					:  Union[str, Any]    			=      attention_mask.to(hidden_states[0]						)
  							_lowercase					:  Optional[int]    			=      mask.sum() * hidden_states[0].size(-1						)
  							_lowercase					:  Dict    			=      torch.stack([hidden_states[i] for i in range(len(snake_case						)						)]						)
  							_lowercase					:  str    			=      torch.stack([hidden_states_T[j] for j in matches]						)
  							assert student_states.shape == teacher_states.shape, F"""{student_states.shape} != {teacher_states.shape}"""
  							if normalize_hidden:
  													_lowercase					:  Optional[Any]    			=      nn.functional.layer_norm(snake_case			,  student_states.shape[1:]						)
  													_lowercase					:  List[str]    			=      nn.functional.layer_norm(snake_case			,  teacher_states.shape[1:]						)
  							_lowercase					:  int    			=      nn.functional.mse_loss(snake_case			,  snake_case			,  reduction="none"						)
  							_lowercase					:  Dict    			=      (mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
  							return masked_mse
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[int]     )      -> str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						parser.add_argument("--teacher"   , type=lowercase__     )
  						parser.add_argument("--alpha_ce"   , default=0.8   , type=lowercase__     )
  						parser.add_argument("--alpha_mlm"   , default=0.2   , type=lowercase__     )
  						parser.add_argument("--alpha_hid"   , default=0.0   , type=lowercase__   , required=lowercase__     )
  						parser.add_argument("--student"   , type=lowercase__   , required=lowercase__     )
  						parser.add_argument("--student_decoder_layers"   , default=12   , type=lowercase__   , required=lowercase__     )
  						parser.add_argument("--student_encoder_layers"   , default=12   , type=lowercase__   , required=lowercase__     )
  						parser.add_argument("--no_teacher"   , action="store_true"   , default=lowercase__     )
  						parser.add_argument("--length_penalty"   , type=lowercase__   , default=-1     )
  						parser.add_argument("--supervise_forward"   , action="store_true"   , default=lowercase__     )
  						parser.add_argument("--normalize_hidden"   , action="store_true"   , default=lowercase__     )
  
  
  
  
  class      __lowercase	(	A_					):
  	_A   			=      '''translation'''
  	_A   			=      ['''bleu''']
  	_A   			=      '''bleu'''
  
  
  
  
  
  
  	def __init__(self							:   Any			,  snake_case							:   Any			,  **snake_case							:   Any						)   -> Dict:
  							super().__init__(snake_case			,  **snake_case						)
  							assert hparams.src_lang is not None
  							assert hparams.tgt_lang is not None
  							_lowercase					:  int    			=      hparams.src_lang
  							_lowercase					:  int    			=      hparams.tgt_lang
  							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,  snake_case						):
  													_lowercase					:  Optional[Any]    			=      self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  
  
  
  
  	def 			_a(self							:   int			,  snake_case							:   Union[str, Any]			,  snake_case							:   Optional[Any]						)   -> str:
  							return calculate_bleu(snake_case			,  snake_case						)
  
  
  
  
  
  
  	@staticmethod
  	def 			_a(snake_case							:   Optional[Any]			,  snake_case							:   int						)   -> Optional[Any]:
  							TranslationModule.add_model_specific_args(snake_case			,  snake_case						)
  							add_distill_args(snake_case						)
  							return parser
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Union[str, Any]     )      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if args.no_teacher:
  												_lowercase					:  int    			=      TranslationModule if "translation" in args.task else SummarizationModule
  						else:  # DISTILL WITH TEACHER
  												_lowercase					:  str    			=      TranslationDistiller if "translation" in args.task else SummarizationDistiller
  						_lowercase					:  str    			=      module_cls.__name__
  						print(f"""using module {args.setup_cls}"""     )
  						_lowercase					:  str    			=      module_cls(lowercase__     )
  						return model
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      List[Any]     )      -> Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						Path(args.output_dir     ).mkdir(exist_ok=lowercase__     )
  						check_output_dir(lowercase__   , expected_items=3     )
  
  						_lowercase					:  int    			=      create_module(lowercase__     )
  						return ft_main(lowercase__   , model=lowercase__     )
  
  
  if __name__ == "__main__":
  							UpperCamelCase_	:    List[Any]  					=       argparse.ArgumentParser()
  							UpperCamelCase_	:    List[str]  					=       pl.Trainer.add_argparse_args(parser)
  							UpperCamelCase_	:    int  					=       SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  							UpperCamelCase_	:    Dict  					=       parser.parse_args()
  
  							distill_main(args)
  
  
  
  
  
  
style_context: 
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  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 (
  							    MODEL_FOR_PRETRAINING_MAPPING,
  							    ElectraForCausalLM,
  							    ElectraForMaskedLM,
  							    ElectraForMultipleChoice,
  							    ElectraForPreTraining,
  							    ElectraForQuestionAnswering,
  							    ElectraForSequenceClassification,
  							    ElectraForTokenClassification,
  							    ElectraModel,
  							)
  							from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   List[Any]			,  snake_case							:   Dict			,  snake_case							:   Optional[Any]=13			,  snake_case							:   Optional[Any]=7			,  snake_case							:   Optional[int]=True			,  snake_case							:   str=True			,  snake_case							:   Dict=True			,  snake_case							:   str=True			,  snake_case							:   int=99			,  snake_case							:   int=32			,  snake_case							:   Union[str, Any]=5			,  snake_case							:   Any=4			,  snake_case							:   Tuple=37			,  snake_case							:   Tuple="gelu"			,  snake_case							:   Tuple=0.1			,  snake_case							:   Optional[int]=0.1			,  snake_case							:   Optional[Any]=512			,  snake_case							:   Dict=16			,  snake_case							:   Optional[int]=2			,  snake_case							:   int=0.02			,  snake_case							:   int=3			,  snake_case							:   str=4			,  snake_case							:   Optional[Any]=None			,  )   -> List[Any]:
  							_lowercase					:  Dict    			=      parent
  							_lowercase					:  List[str]    			=      batch_size
  							_lowercase					:  int    			=      seq_length
  							_lowercase					:  Optional[Any]    			=      is_training
  							_lowercase					:  Tuple    			=      use_input_mask
  							_lowercase					:  Optional[int]    			=      use_token_type_ids
  							_lowercase					:  Optional[int]    			=      use_labels
  							_lowercase					:  List[Any]    			=      vocab_size
  							_lowercase					:  Union[str, Any]    			=      hidden_size
  							_lowercase					:  Dict    			=      num_hidden_layers
  							_lowercase					:  str    			=      num_attention_heads
  							_lowercase					:  int    			=      intermediate_size
  							_lowercase					:  Union[str, Any]    			=      hidden_act
  							_lowercase					:  Tuple    			=      hidden_dropout_prob
  							_lowercase					:  Union[str, Any]    			=      attention_probs_dropout_prob
  							_lowercase					:  Optional[Any]    			=      max_position_embeddings
  							_lowercase					:  str    			=      type_vocab_size
  							_lowercase					:  Dict    			=      type_sequence_label_size
  							_lowercase					:  List[Any]    			=      initializer_range
  							_lowercase					:  List[str]    			=      num_labels
  							_lowercase					:  Any    			=      num_choices
  							_lowercase					:  Any    			=      scope
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Tuple:
  							_lowercase					:  List[str]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size						)
  
  							_lowercase					:  Dict    			=      None
  							if self.use_input_mask:
  													_lowercase					:  List[str]    			=      random_attention_mask([self.batch_size, self.seq_length]						)
  
  							_lowercase					:  Optional[Any]    			=      None
  							if self.use_token_type_ids:
  													_lowercase					:  Tuple    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.type_vocab_size						)
  
  							_lowercase					:  str    			=      None
  							_lowercase					:  Optional[int]    			=      None
  							_lowercase					:  int    			=      None
  							if self.use_labels:
  													_lowercase					:  str    			=      ids_tensor([self.batch_size]			,  self.type_sequence_label_size						)
  													_lowercase					:  Any    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels						)
  													_lowercase					:  Any    			=      ids_tensor([self.batch_size]			,  self.num_choices						)
  													_lowercase					:  Optional[Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  1						)
  
  							_lowercase					:  Optional[Any]    			=      self.get_config()
  
  							return (
  							    config,
  							    input_ids,
  							    token_type_ids,
  							    input_mask,
  							    sequence_labels,
  							    token_labels,
  							    choice_labels,
  							    fake_token_labels,
  							)
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> Optional[Any]:
  							return ElectraConfig(
  							    vocab_size=self.vocab_size			,  hidden_size=self.hidden_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=snake_case			,  initializer_range=self.initializer_range			,  )
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Optional[Any]:
  							(
  							    (
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						
  							)					:  Any    			=      self.prepare_config_and_inputs()
  
  							_lowercase					:  Union[str, Any]    			=      True
  							_lowercase					:  Dict    			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
  							_lowercase					:  Tuple    			=      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 			_a(self							:   Optional[Any]			,  snake_case							:   Optional[int]			,  snake_case							:   Dict			,  snake_case							:   Dict			,  snake_case							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   List[Any]			,  snake_case							:   Optional[int]			,  snake_case							:   Tuple			,  )   -> str:
  							_lowercase					:  Tuple    			=      ElectraModel(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  str    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case						)
  							_lowercase					:  Optional[Any]    			=      model(snake_case			,  token_type_ids=snake_case						)
  							_lowercase					:  Optional[int]    			=      model(snake_case						)
  							self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   int			,  snake_case							:   Dict			,  snake_case							:   Any			,  snake_case							:   int			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]			,  snake_case							:   str			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]			,  )   -> Dict:
  							_lowercase					:  Tuple    			=      True
  							_lowercase					:  Optional[int]    			=      ElectraModel(snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  int    			=      model(
  							    snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  encoder_hidden_states=snake_case			,  encoder_attention_mask=snake_case			,  )
  							_lowercase					:  Optional[Any]    			=      model(
  							    snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  encoder_hidden_states=snake_case			,  )
  							_lowercase					:  int    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case						)
  							self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   int			,  snake_case							:   Tuple			,  snake_case							:   List[Any]			,  snake_case							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   Dict			,  snake_case							:   Dict			,  snake_case							:   Optional[Any]			,  )   -> Any:
  							_lowercase					:  Dict    			=      ElectraForMaskedLM(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  Optional[int]    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  labels=snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   Dict			,  snake_case							:   Optional[int]			,  snake_case							:   Any			,  snake_case							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   List[str]			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   str			,  )   -> Any:
  							_lowercase					:  int    			=      ElectraForCausalLM(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  Union[str, Any]    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  labels=snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   Union[str, Any]			,  snake_case							:   List[str]			,  snake_case							:   Any			,  snake_case							:   Dict			,  snake_case							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   Optional[int]			,  )   -> Tuple:
  							_lowercase					:  List[Any]    			=      self.num_labels
  							_lowercase					:  Dict    			=      ElectraForTokenClassification(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  Tuple    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  labels=snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   str			,  snake_case							:   Union[str, Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Dict			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int			,  snake_case							:   Union[str, Any]			,  )   -> Union[str, Any]:
  							_lowercase					:  List[Any]    			=      self.num_labels
  							_lowercase					:  int    			=      ElectraForPreTraining(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  List[str]    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  labels=snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length)						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Tuple			,  snake_case							:   Optional[int]			,  snake_case							:   str			,  snake_case							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   Union[str, Any]			,  snake_case							:   Dict			,  )   -> List[Any]:
  							_lowercase					:  List[str]    			=      self.num_labels
  							_lowercase					:  Tuple    			=      ElectraForSequenceClassification(snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  Any    			=      model(snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  labels=snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   Optional[int]			,  snake_case							:   List[Any]			,  snake_case							:   List[str]			,  snake_case							:   Any			,  snake_case							:   Optional[int]			,  snake_case							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   str			,  )   -> str:
  							_lowercase					:  int    			=      ElectraForQuestionAnswering(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  Tuple    			=      model(
  							    snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  start_positions=snake_case			,  end_positions=snake_case			,  )
  							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 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   Dict			,  snake_case							:   Any			,  snake_case							:   Tuple			,  snake_case							:   str			,  snake_case							:   Optional[Any]			,  snake_case							:   str			,  snake_case							:   Tuple			,  )   -> Union[str, Any]:
  							_lowercase					:  List[Any]    			=      self.num_choices
  							_lowercase					:  Optional[Any]    			=      ElectraForMultipleChoice(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  str    			=      input_ids.unsqueeze(1						).expand(-1			,  self.num_choices			,  -1						).contiguous()
  							_lowercase					:  Dict    			=      token_type_ids.unsqueeze(1						).expand(-1			,  self.num_choices			,  -1						).contiguous()
  							_lowercase					:  Optional[Any]    			=      input_mask.unsqueeze(1						).expand(-1			,  self.num_choices			,  -1						).contiguous()
  							_lowercase					:  Optional[int]    			=      model(
  							    snake_case			,  attention_mask=snake_case			,  token_type_ids=snake_case			,  labels=snake_case			,  )
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_choices)						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> Union[str, Any]:
  							_lowercase					:  Any    			=      self.prepare_config_and_inputs()
  							(
  							    (
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						
  							)					:  Optional[int]    			=      config_and_inputs
  							_lowercase					:  Optional[Any]    			=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
  							return config, inputs_dict
  
  
  
  
  @require_torch
  class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
  	_A   			=      (
  	    (
  	        ElectraModel,
  	        ElectraForPreTraining,
  	        ElectraForMaskedLM,
  	        ElectraForCausalLM,
  	        ElectraForMultipleChoice,
  	        ElectraForTokenClassification,
  	        ElectraForSequenceClassification,
  	        ElectraForQuestionAnswering,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	_A   			=      (
  	    {
  	        "feature-extraction": ElectraModel,
  	        "fill-mask": ElectraForMaskedLM,
  	        "question-answering": ElectraForQuestionAnswering,
  	        "text-classification": ElectraForSequenceClassification,
  	        "text-generation": ElectraForCausalLM,
  	        "token-classification": ElectraForTokenClassification,
  	        "zero-shot": ElectraForSequenceClassification,
  	    }
  	    if is_torch_available()
  	    else {}
  	)
  	_A   			=      True
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   Any			,  snake_case							:   List[Any]			,  snake_case							:   Any=False						)   -> Optional[int]:
  							_lowercase					:  Any    			=      super()._prepare_for_class(snake_case			,  snake_case			,  return_labels=snake_case						)
  
  							if return_labels:
  													if model_class in get_values(snake_case						):
  																			_lowercase					:  List[str]    			=      torch.zeros(
  																			    (self.model_tester.batch_size, self.model_tester.seq_length)			,  dtype=torch.long			,  device=snake_case						)
  							return inputs_dict
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> List[Any]:
  							_lowercase					:  Tuple    			=      ElectraModelTester(self						)
  							_lowercase					:  int    			=      ConfigTester(self			,  config_class=snake_case			,  hidden_size=37						)
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> Any:
  							self.config_tester.run_common_tests()
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> str:
  							_lowercase					:  List[str]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_model(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> str:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
  							self.model_tester.create_and_check_electra_model_as_decoder(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Dict:
  							_lowercase					:  str    			=      self.model_tester.prepare_config_and_inputs()
  							for type in ["absolute", "relative_key", "relative_key_query"]:
  													_lowercase					:  List[Any]    			=      type
  													self.model_tester.create_and_check_electra_model(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> str:
  							_lowercase					:  int    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_for_masked_lm(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> Dict:
  							_lowercase					:  Optional[Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_for_token_classification(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Optional[Any]:
  							_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_for_pretraining(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> Optional[int]:
  							_lowercase					:  Dict    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_for_sequence_classification(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any						)   -> Dict:
  							_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_for_question_answering(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Tuple:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_electra_for_multiple_choice(*snake_case						)
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   List[str]						)   -> List[Any]:
  							for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													_lowercase					:  Optional[int]    			=      ElectraModel.from_pretrained(snake_case						)
  													self.assertIsNotNone(snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> List[Any]:
  							_lowercase					:  int    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
  							self.model_tester.create_and_check_electra_for_causal_lm(*snake_case						)
  
  
  
  
  @require_torch
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   str						)   -> Optional[int]:
  							_lowercase					:  int    			=      ElectraModel.from_pretrained("google/electra-small-discriminator"						)
  							_lowercase					:  Dict    			=      torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
  							_lowercase					:  Union[str, Any]    			=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
  							_lowercase					:  Optional[Any]    			=      model(snake_case			,  attention_mask=snake_case						)[0]
  							_lowercase					:  Union[str, Any]    			=      torch.Size((1, 11, 256)						)
  							self.assertEqual(output.shape			,  snake_case						)
  							_lowercase					:  List[Any]    			=      torch.tensor(
  							    [[[0.44_71, 0.68_21, -0.32_65], [0.46_27, 0.52_55, -0.36_68], [0.45_32, 0.33_13, -0.43_44]]]						)
  
  							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,  snake_case			,  atol=1e-4						)						)
  
  
  
  
  