





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_snake_case    :       Optional[int]		       =    16
_snake_case    :       Tuple		       =    32

def     a_       (					lowerCAmelCase_ :     Accelerator,      lowerCAmelCase_ :     int = 16,      lowerCAmelCase_ :     str = "bert-base-cased"      ):
   __lowerCAmelCase				      =      AutoTokenizer.from_pretrained(lowerCAmelCase_      )
   __lowerCAmelCase				      =      load_dataset('glue',      'mrpc'      )

   def tokenize_function(lowerCAmelCase_ :     List[Any]      ):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCAmelCase				      =      tokenizer(examples['sentence1'],      examples['sentence2'],      truncation=lowerCAmelCase_,      max_length=lowerCAmelCase_      )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __lowerCAmelCase				      =      datasets.map(
       lowerCAmelCase_,      batched=lowerCAmelCase_,      remove_columns=['idx', 'sentence1', 'sentence2'],      load_from_cache_file=lowerCAmelCase_      )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   __lowerCAmelCase				      =      tokenized_datasets.rename_column('label',      'labels'      )

   def collate_fn(lowerCAmelCase_ :     str      ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowerCAmelCase_,      padding='max_length',      max_length=128,      return_tensors='pt'      )
      return tokenizer.pad(lowerCAmelCase_,      padding='longest',      return_tensors='pt'      )

   # Instantiate dataloaders.
   __lowerCAmelCase				      =      DataLoader(
       tokenized_datasets['train'],      shuffle=lowerCAmelCase_,      collate_fn=lowerCAmelCase_,      batch_size=lowerCAmelCase_      )
   __lowerCAmelCase				      =      DataLoader(
       tokenized_datasets['validation'],      shuffle=lowerCAmelCase_,      collate_fn=lowerCAmelCase_,      batch_size=lowerCAmelCase_      )

   return train_dataloader, eval_dataloader

def     a_       (					lowerCAmelCase_ :     Tuple,      lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     Any      ):
   model.eval()
   __lowerCAmelCase				      =      0
   for step, batch in enumerate(lowerCAmelCase_      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device      )
      with torch.no_grad():
         __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
      __lowerCAmelCase				      =      outputs.logits.argmax(dim=-1      )
      # It is slightly faster to call this once, than multiple times
      __lowerCAmelCase      ,      __lowerCAmelCase				      =      accelerator.gather(
          (predictions, batch['labels'])      )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(lowerCAmelCase_      ) - 1:
            __lowerCAmelCase				      =      predictions[: len(eval_dataloader.dataset      ) - samples_seen]
            __lowerCAmelCase				      =      references[: len(eval_dataloader.dataset      ) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=lowerCAmelCase_,      references=lowerCAmelCase_,      )

   __lowerCAmelCase				      =      metric.compute()
   return eval_metric["accuracy"]

def     a_       (					lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     Optional[int]      ):
   # Initialize accelerator
   __lowerCAmelCase				      =      Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCAmelCase				      =      config['lr']
   __lowerCAmelCase				      =      int(config['num_epochs']      )
   __lowerCAmelCase				      =      int(config['seed']      )
   __lowerCAmelCase				      =      int(config['batch_size']      )
   __lowerCAmelCase				      =      args.model_name_or_path

   set_seed(lowerCAmelCase_      )
   __lowerCAmelCase      ,      __lowerCAmelCase				      =      get_dataloaders(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCAmelCase				      =      AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_,      return_dict=lowerCAmelCase_      )

   # Instantiate optimizer
   __lowerCAmelCase				      =      (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowerCAmelCase				      =      optimizer_cls(params=model.parameters(),      lr=lowerCAmelCase_      )

   if accelerator.state.deepspeed_plugin is not None:
      __lowerCAmelCase				      =      accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      __lowerCAmelCase				      =      1
   __lowerCAmelCase				      =      (len(lowerCAmelCase_      ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      __lowerCAmelCase				      =      get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase_,      num_warmup_steps=0,      num_training_steps=lowerCAmelCase_,      )
   else:
      __lowerCAmelCase				      =      DummyScheduler(lowerCAmelCase_,      total_num_steps=lowerCAmelCase_,      warmup_num_steps=0      )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      accelerator.prepare(
       lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

   # We need to keep track of how many total steps we have iterated over
   __lowerCAmelCase				      =      0
   # We also need to keep track of the stating epoch so files are named properly
   __lowerCAmelCase				      =      0
   __lowerCAmelCase				      =      evaluate.load('glue',      'mrpc'      )
   __lowerCAmelCase				      =      num_epochs

   if args.partial_train_epoch is not None:
      __lowerCAmelCase				      =      args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint      )
      __lowerCAmelCase				      =      args.resume_from_checkpoint.split('epoch_'      )[1]
      __lowerCAmelCase				      =      ''
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      __lowerCAmelCase				      =      int(lowerCAmelCase_      ) + 1
      __lowerCAmelCase				      =      evaluation_loop(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )
      accelerator.print('resumed checkpoint performance:',      lowerCAmelCase_      )
      accelerator.print('resumed checkpoint\'s scheduler\'s lr:',      lr_scheduler.get_lr()[0]      )
      accelerator.print('resumed optimizers\'s lr:',      optimizer.param_groups[0]['lr']      )
      with open(os.path.join(args.output_dir,      F"""state_{starting_epoch-1}.json"""      ),      'r'      ) as f:
         __lowerCAmelCase				      =      json.load(lowerCAmelCase_      )
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   __lowerCAmelCase				      =      {}
   for epoch in range(lowerCAmelCase_,      lowerCAmelCase_      ):
      model.train()
      for step, batch in enumerate(lowerCAmelCase_      ):
         __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
         __lowerCAmelCase				      =      outputs.loss
         __lowerCAmelCase				      =      loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase_      )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      __lowerCAmelCase				      =      F"""epoch_{epoch}"""
      __lowerCAmelCase				      =      os.path.join(args.output_dir,      lowerCAmelCase_      )
      accelerator.save_state(lowerCAmelCase_      )
      __lowerCAmelCase				      =      evaluation_loop(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )
      __lowerCAmelCase				      =      accuracy
      __lowerCAmelCase				      =      lr_scheduler.get_lr()[0]
      __lowerCAmelCase				      =      optimizer.param_groups[0]['lr']
      __lowerCAmelCase				      =      epoch
      __lowerCAmelCase				      =      overall_step
      accelerator.print(F"""epoch {epoch}:""",      lowerCAmelCase_      )

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir,      F"""state_{epoch}.json"""      ),      'w'      ) as f:
            json.dump(lowerCAmelCase_,      lowerCAmelCase_      )

def     a_       (					):
   __lowerCAmelCase				      =      argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'      )
   parser.add_argument(
       '--model_name_or_path',      type=lowerCAmelCase_,      default='bert-base-cased',      help='Path to pretrained model or model identifier from huggingface.co/models.',      required=lowerCAmelCase_,      )
   parser.add_argument(
       '--output_dir',      type=lowerCAmelCase_,      default='.',      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',      )
   parser.add_argument(
       '--resume_from_checkpoint',      type=lowerCAmelCase_,      default=lowerCAmelCase_,      help='If the training should continue from a checkpoint folder.',      )
   parser.add_argument(
       '--partial_train_epoch',      type=lowerCAmelCase_,      default=lowerCAmelCase_,      help='If passed, the training will stop after this number of epochs.',      )
   parser.add_argument(
       '--num_epochs',      type=lowerCAmelCase_,      default=2,      help='Number of train epochs.',      )
   __lowerCAmelCase				      =      parser.parse_args()
   __lowerCAmelCase				      =      {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

   training_function(lowerCAmelCase_,      lowerCAmelCase_      )


if __name__ == "__main__":
     main()


"""simple docstring"""


import unittest

from transformers import MraConfig, 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


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class 							a					:


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str=2 ,					__lowerCAmelCase						:	List[Any]=8 ,					__lowerCAmelCase						:	Dict=True ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	str=True ,					__lowerCAmelCase						:	Optional[Any]=99 ,					__lowerCAmelCase						:	Optional[Any]=16 ,					__lowerCAmelCase						:	Optional[int]=5 ,					__lowerCAmelCase						:	Optional[int]=2 ,					__lowerCAmelCase						:	Optional[int]=36 ,					__lowerCAmelCase						:	Any="gelu" ,					__lowerCAmelCase						:	Any=0.0 ,					__lowerCAmelCase						:	int=0.0 ,					__lowerCAmelCase						:	List[str]=512 ,					__lowerCAmelCase						:	List[str]=16 ,					__lowerCAmelCase						:	int=2 ,					__lowerCAmelCase						:	Any=0.02 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	str=4 ,					__lowerCAmelCase						:	Dict=None ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   seq_length
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   use_input_mask
      _UpperCAmelCase    			=   use_token_type_ids
      _UpperCAmelCase    			=   use_labels
      _UpperCAmelCase    			=   vocab_size
      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   max_position_embeddings
      _UpperCAmelCase    			=   type_vocab_size
      _UpperCAmelCase    			=   type_sequence_label_size
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   num_labels
      _UpperCAmelCase    			=   num_choices
      _UpperCAmelCase    			=   scope


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size       )

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

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

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

      _UpperCAmelCase    			=   self.get_config()

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


    def   lowerCAmelCase_	(       self						:	Any       ):
      return MraConfig(
          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=__lowerCAmelCase ,					initializer_range=self.initializer_range ,					)


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.get_config()
      _UpperCAmelCase    			=   300
      return config


    def   lowerCAmelCase_	(       self						:	Any       ):
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   self.prepare_config_and_inputs()

      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   MraModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					):
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   MraModel(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					encoder_attention_mask=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   MraForQuestionAnswering(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForSequenceClassification(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForTokenClassification(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.num_choices
      _UpperCAmelCase    			=   MraForMultipleChoice(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   input_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   token_type_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   input_mask.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase ,					)
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   config_and_inputs
      _UpperCAmelCase    			=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	str  				=				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _snake_case  :	Dict  				=				False
    _snake_case  :	Any  				=				False
    _snake_case  :	List[str]  				=				False
    _snake_case  :	Tuple  				=				False

    _snake_case  :	int  				=				()


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   MraModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      self.config_tester.run_common_tests()


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        _UpperCAmelCase    			=   type
        self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase    			=   MraModel.from_pretrained(__lowerCAmelCase       )
        self.assertIsNotNone(__lowerCAmelCase       )


    @unittest.skip(reason="""MRA does not output attentions"""       )
    def   lowerCAmelCase_	(       self						:	str       ):
      return



@require_torch
class 							a					(		unittest.TestCase ):


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraModel.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   torch.Size((1, 256, 768)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )


    @slow
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 256, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""       )
      _UpperCAmelCase    			=   torch.arange(4096       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 4096, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )
