





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_UpperCAmelCase				       = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCAmelCase				       = {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_UpperCAmelCase				       = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_UpperCAmelCase				       = sorted(arg_to_scheduler.keys())
_UpperCAmelCase				       = '{' + ', '.join(arg_to_scheduler_choices) + '}'



class 							_UpperCamelCase    (		pl.LightningModule   ):

       def __init__(						self:       Dict    ,      _SCREAMING_SNAKE_CASE:       Tuple    ,      _SCREAMING_SNAKE_CASE:       List[str]=None    ,      _SCREAMING_SNAKE_CASE:       Tuple="base"    ,      _SCREAMING_SNAKE_CASE:       List[str]=None    ,      _SCREAMING_SNAKE_CASE:       Any=None    ,      _SCREAMING_SNAKE_CASE:       Tuple=None    ,      **_SCREAMING_SNAKE_CASE:       Optional[Any]    ,      )				->       str:




              """simple docstring"""


              super().__init__()
              # TODO: move to self.save_hyperparameters()
              # self.save_hyperparameters()
              # can also expand arguments into trainer signature for easier reading

              self.save_hyperparameters(__lowercase						)
              UpperCamelCase_      				=     0
              UpperCamelCase_      				=     Path(self.hparams.output_dir						)
              UpperCamelCase_      				=     self.hparams.cache_dir if self.hparams.cache_dir else None
              if config is None:
                     UpperCamelCase_      				=     AutoConfig.from_pretrained(
                         self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,      **({"num_labels": num_labels} if num_labels is not None else {})    ,      cache_dir=__lowercase    ,      **__lowercase    ,      )
              else:
                     UpperCamelCase_      				=     config

              UpperCamelCase_      				=     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
              for p in extra_model_params:
                     if getattr(self.hparams    ,      __lowercase    ,      __lowercase						):
                            assert hasattr(self.config    ,      __lowercase						), f'''model config doesn\'t have a `{p}` attribute'''
                            setattr(self.config    ,      __lowercase    ,      getattr(self.hparams    ,      __lowercase						)						)

              if tokenizer is None:
                     UpperCamelCase_      				=     AutoTokenizer.from_pretrained(
                         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,      cache_dir=__lowercase    ,      )
              else:
                     UpperCamelCase_      				=     tokenizer
              UpperCamelCase_      				=     MODEL_MODES[mode]
              if model is None:
                     UpperCamelCase_      				=     self.model_type.from_pretrained(
                         self.hparams.model_name_or_path    ,      from_tf=bool(".ckpt" in self.hparams.model_name_or_path						)    ,      config=self.config    ,      cache_dir=__lowercase    ,      )
              else:
                     UpperCamelCase_      				=     model

       def lowercase       (						self:       Any    ,      *_SCREAMING_SNAKE_CASE:       Optional[Any]    ,      **_SCREAMING_SNAKE_CASE:       int						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     self.model_type.from_pretrained(*__lowercase    ,      **__lowercase						)

       def lowercase       (						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     arg_to_scheduler[self.hparams.lr_scheduler]
              UpperCamelCase_      				=     get_schedule_func(
                  self.opt    ,      num_warmup_steps=self.hparams.warmup_steps    ,      num_training_steps=self.total_steps()						)
              UpperCamelCase_      				=     {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
              return scheduler

       def lowercase       (						self:       Tuple						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     self.model
              UpperCamelCase_      				=     ['bias', 'LayerNorm.weight']
              UpperCamelCase_      				=     [
                  {
                      'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
                      'weight_decay': self.hparams.weight_decay,
                  },
                  {
                      'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
                      'weight_decay': 0.0,
                  },
              ]
              if self.hparams.adafactor:
                     UpperCamelCase_      				=     Adafactor(
                         __lowercase    ,      lr=self.hparams.learning_rate    ,      scale_parameter=__lowercase    ,      relative_step=__lowercase						)

              else:
                     UpperCamelCase_      				=     AdamW(
                         __lowercase    ,      lr=self.hparams.learning_rate    ,      eps=self.hparams.adam_epsilon						)
              UpperCamelCase_      				=     optimizer

              UpperCamelCase_      				=     self.get_lr_scheduler()

              return [optimizer], [scheduler]

       def lowercase       (						self:       str    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]						)				->       Any:




              """simple docstring"""


              return self.validation_step(__lowercase    ,      __lowercase						)

       def lowercase       (						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Optional[int]						)				->       Union[str, Any]:




              """simple docstring"""


              return self.validation_end(__lowercase						)

       def lowercase       (						self:       int						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     max(1    ,      self.hparams.gpus						)  # TODO: consider num_tpu_cores
              UpperCamelCase_      				=     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
              return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

       def lowercase       (						self:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       int						)				->       Optional[Any]:




              """simple docstring"""


              if stage == "test":
                     UpperCamelCase_      				=     len(self.test_dataloader().dataset						)
              else:
                     UpperCamelCase_      				=     self.get_dataloader("train"    ,      self.hparams.train_batch_size    ,      shuffle=__lowercase						)
                     UpperCamelCase_      				=     len(self.train_dataloader().dataset						)

       def lowercase       (						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       Dict    ,      _SCREAMING_SNAKE_CASE:       List[str] = False						)				->       Tuple:




              """simple docstring"""


              raise NotImplementedError("You must implement this for your task"						)

       def lowercase       (						self:       Optional[int]						)				->       List[Any]:




              """simple docstring"""


              return self.train_loader

       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              return self.get_dataloader("dev"    ,      self.hparams.eval_batch_size    ,      shuffle=__lowercase						)

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




              """simple docstring"""


              return self.get_dataloader("test"    ,      self.hparams.eval_batch_size    ,      shuffle=__lowercase						)

       def lowercase       (						self:       int    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]						)				->       List[Any]:




              """simple docstring"""


              return os.path.join(
                  self.hparams.data_dir    ,      "cached_{}_{}_{}".format(
                      __lowercase    ,      list(filter(__lowercase    ,      self.hparams.model_name_or_path.split("/"						)						)						).pop()    ,      str(self.hparams.max_seq_length						)    ,      )    ,      )

       @pl.utilities.rank_zero_only
       def lowercase       (						self:       Dict    ,      _SCREAMING_SNAKE_CASE:       Any						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     self.output_dir.joinpath("best_tfmr"						)
              UpperCamelCase_      				=     self.step_count
              self.model.save_pretrained(__lowercase						)
              self.tokenizer.save_pretrained(__lowercase						)



       @staticmethod
       def lowercase       (						_SCREAMING_SNAKE_CASE:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Optional[int]						)				->       Union[str, Any]:




              """simple docstring"""


              parser.add_argument(
                  "--model_name_or_path"    ,      default=__lowercase    ,      type=__lowercase    ,      required=__lowercase    ,      help="Path to pretrained model or model identifier from huggingface.co/models"    ,      )
              parser.add_argument(
                  "--config_name"    ,      default=""    ,      type=__lowercase    ,      help="Pretrained config name or path if not the same as model_name"						)
              parser.add_argument(
                  "--tokenizer_name"    ,      default=__lowercase    ,      type=__lowercase    ,      help="Pretrained tokenizer name or path if not the same as model_name"    ,      )
              parser.add_argument(
                  "--cache_dir"    ,      default=""    ,      type=__lowercase    ,      help="Where do you want to store the pre-trained models downloaded from huggingface.co"    ,      )
              parser.add_argument(
                  "--encoder_layerdrop"    ,      type=__lowercase    ,      help="Encoder layer dropout probability (Optional). Goes into model.config"    ,      )
              parser.add_argument(
                  "--decoder_layerdrop"    ,      type=__lowercase    ,      help="Decoder layer dropout probability (Optional). Goes into model.config"    ,      )
              parser.add_argument(
                  "--dropout"    ,      type=__lowercase    ,      help="Dropout probability (Optional). Goes into model.config"    ,      )
              parser.add_argument(
                  "--attention_dropout"    ,      type=__lowercase    ,      help="Attention dropout probability (Optional). Goes into model.config"    ,      )
              parser.add_argument("--learning_rate"    ,      default=5e-5    ,      type=__lowercase    ,      help="The initial learning rate for Adam."						)
              parser.add_argument(
                  "--lr_scheduler"    ,      default="linear"    ,      choices=__lowercase    ,      metavar=__lowercase    ,      type=__lowercase    ,      help="Learning rate scheduler"    ,      )
              parser.add_argument("--weight_decay"    ,      default=0.0    ,      type=__lowercase    ,      help="Weight decay if we apply some."						)
              parser.add_argument("--adam_epsilon"    ,      default=1e-8    ,      type=__lowercase    ,      help="Epsilon for Adam optimizer."						)
              parser.add_argument("--warmup_steps"    ,      default=0    ,      type=__lowercase    ,      help="Linear warmup over warmup_steps."						)
              parser.add_argument("--num_workers"    ,      default=4    ,      type=__lowercase    ,      help="kwarg passed to DataLoader"						)
              parser.add_argument("--num_train_epochs"    ,      dest="max_epochs"    ,      default=3    ,      type=__lowercase						)
              parser.add_argument("--train_batch_size"    ,      default=32    ,      type=__lowercase						)
              parser.add_argument("--eval_batch_size"    ,      default=32    ,      type=__lowercase						)
              parser.add_argument("--adafactor"    ,      action="store_true"						)



class 							_UpperCamelCase    (		pl.Callback   ):

       def lowercase       (						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       List[str]						)				->       Dict:




              """simple docstring"""


              if (
                  trainer.is_global_zero and trainer.global_rank == 0
              ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                     pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class 							_UpperCamelCase    (		pl.Callback   ):

       def lowercase       (						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Dict    ,      _SCREAMING_SNAKE_CASE:       List[Any]						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     trainer.lr_schedulers[0]['scheduler']
              UpperCamelCase_      				=     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
              pl_module.logger.log_metrics(__lowercase						)

       def lowercase       (						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       List[str]						)				->       Optional[Any]:




              """simple docstring"""


              rank_zero_info("***** Validation results *****"						)
              UpperCamelCase_      				=     trainer.callback_metrics
              # Log results
              for key in sorted(__lowercase						):
                     if key not in ["log", "progress_bar"]:
                            rank_zero_info("{} = {}\n".format(__lowercase    ,      str(metrics[key]						)						)						)



       def lowercase       (						self:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       int    ,      _SCREAMING_SNAKE_CASE:       Optional[int]						)				->       Optional[int]:




              """simple docstring"""


              rank_zero_info("***** Test results *****"						)
              UpperCamelCase_      				=     trainer.callback_metrics
              # Log and save results to file
              UpperCamelCase_      				=     os.path.join(pl_module.hparams.output_dir    ,      "test_results.txt"						)
              with open(__lowercase    ,      "w"						) as writer:
                     for key in sorted(__lowercase						):
                            if key not in ["log", "progress_bar"]:
                                   rank_zero_info("{} = {}\n".format(__lowercase    ,      str(metrics[key]						)						)						)
                                   writer.write("{} = {}\n".format(__lowercase    ,      str(metrics[key]						)						)						)




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						None:
       parser.add_argument(
           "--output_dir"    ,						default=UpperCamelCase_    ,						type=UpperCamelCase_    ,						required=UpperCamelCase_    ,						help="The output directory where the model predictions and checkpoints will be written."    ,						)
       parser.add_argument(
           "--fp16"    ,						action="store_true"    ,						help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"    ,						)

       parser.add_argument(
           "--fp16_opt_level"    ,						type=UpperCamelCase_    ,						default="O2"    ,						help=(
               "For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."
               "See details at https://nvidia.github.io/apex/amp.html"
           )    ,						)
       parser.add_argument("--n_tpu_cores"    ,						dest="tpu_cores"    ,						type=UpperCamelCase_     )
       parser.add_argument("--max_grad_norm"    ,						dest="gradient_clip_val"    ,						default=1.0    ,						type=UpperCamelCase_    ,						help="Max gradient norm"     )
       parser.add_argument("--do_train"    ,						action="store_true"    ,						help="Whether to run training."     )
       parser.add_argument("--do_predict"    ,						action="store_true"    ,						help="Whether to run predictions on the test set."     )
       parser.add_argument(
           "--gradient_accumulation_steps"    ,						dest="accumulate_grad_batches"    ,						type=UpperCamelCase_    ,						default=1    ,						help="Number of updates steps to accumulate before performing a backward/update pass."    ,						)
       parser.add_argument("--seed"    ,						type=UpperCamelCase_    ,						default=42    ,						help="random seed for initialization"     )
       parser.add_argument(
           "--data_dir"    ,						default=UpperCamelCase_    ,						type=UpperCamelCase_    ,						required=UpperCamelCase_    ,						help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."    ,						)







def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_=None    ,						UpperCamelCase_=True    ,						UpperCamelCase_=None    ,						UpperCamelCase_=[]    ,						UpperCamelCase_=None    ,						UpperCamelCase_=None    ,						**UpperCamelCase_    ,						)   ->						Union[str, Any]:
       pl.seed_everything(args.seed     )

       # init model
       UpperCamelCase_      				=     Path(model.hparams.output_dir     )
       odir.mkdir(exist_ok=UpperCamelCase_     )

       # add custom checkpoints
       if checkpoint_callback is None:
              UpperCamelCase_      				=     pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir    ,						prefix="checkpoint"    ,						monitor="val_loss"    ,						mode="min"    ,						save_top_k=1     )
       if early_stopping_callback:
              extra_callbacks.append(UpperCamelCase_     )
       if logging_callback is None:
              UpperCamelCase_      				=     LoggingCallback()

       UpperCamelCase_      				=     {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              UpperCamelCase_      				=     16
              # train_params["amp_level"] = args.fp16_opt_level

       if args.gpus > 1:
              UpperCamelCase_      				=     'auto'  # "ddp"
              UpperCamelCase_      				=     'ddp'

       UpperCamelCase_      				=     args.accumulate_grad_batches
       UpperCamelCase_      				=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       UpperCamelCase_      				=     'auto'

       UpperCamelCase_      				=     pl.Trainer.from_argparse_args(
           UpperCamelCase_    ,						weights_summary=UpperCamelCase_    ,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]    ,						logger=UpperCamelCase_    ,						**UpperCamelCase_    ,						)

       if args.do_train:
              trainer.fit(UpperCamelCase_     )

       return trainer







import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=13 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=24 ,       __lowercase=32 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=16 ,       __lowercase=2 ,       __lowercase=0.02 ,       __lowercase=4 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Union[str, Any]							   =							seq_length
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Any							   =							use_attention_mask
						lowerCamelCase_							:		Any							   =							use_token_type_ids
						lowerCamelCase_							:		Any							   =							use_labels
						lowerCamelCase_							:		Any							   =							vocab_size
						lowerCamelCase_							:		Any							   =							hidden_size
						lowerCamelCase_							:		Optional[Any]							   =							embedding_size
						lowerCamelCase_							:		Dict							   =							num_hidden_layers
						lowerCamelCase_							:		Optional[Any]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		int							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							type_vocab_size
						lowerCamelCase_							:		int							   =							type_sequence_label_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		int							   =							num_choices

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_attention_mask:
							lowerCamelCase_							:		Tuple							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:		List[Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size					)

						lowerCamelCase_							:		List[str]							   =							ElectraConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       embedding_size=self.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 ,       initializer_range=self.initializer_range ,       )

						return config, input_ids, token_type_ids, attention_mask





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							config_and_inputs
						lowerCamelCase_							:		Union[str, Any]							   =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = True

					_lowerCamelCase		  = (
					    (
					        FlaxElectraModel,
					        FlaxElectraForCausalLM,
					        FlaxElectraForMaskedLM,
					        FlaxElectraForPreTraining,
					        FlaxElectraForTokenClassification,
					        FlaxElectraForQuestionAnswering,
					        FlaxElectraForMultipleChoice,
					        FlaxElectraForSequenceClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							FlaxElectraModelTester(self					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase_							:		Union[str, Any]							   =							model_class_name.from_pretrained('google/electra-small-generator'					)
							else:
								lowerCamelCase_							:		List[Any]							   =							model_class_name.from_pretrained('google/electra-small-discriminator'					)
							lowerCamelCase_							:		Dict							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)
