





'''simple docstring'''




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


_A:					int							=				logging.get_logger(__name__)

_A:					List[str]							=				{
    """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,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A=None     ,			__A=None     ,			*__A     ,			**__A					):
     super().__init__(*__A     ,			**__A					)

     if config is None:
      assert isinstance(self.model     ,			__A					), (
          "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
          f' {self.model.__class__}'
      )
      __UpperCAmelCase					=   self.model.config
     else:
      __UpperCAmelCase					=   config

     __UpperCAmelCase					=   data_args
     __UpperCAmelCase					=   self.config.tgt_vocab_size if isinstance(self.config     ,			__A					) else self.config.vocab_size

     if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
      assert self.config.pad_token_id is not None, (
          "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
          " calculation or doing label smoothing."
      )

     if self.config.pad_token_id is None and self.config.eos_token_id is not None:
      logger.warning(
          f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
          ' padding..'					)

     if self.args.label_smoothing == 0:
      __UpperCAmelCase					=   torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id					)
     else:
      # dynamically import label_smoothed_nll_loss
      from utils import label_smoothed_nll_loss

      __UpperCAmelCase					=   label_smoothed_nll_loss



    def 					__lowerCamelCase						(  self     ,			__A					):
     if self.optimizer is None:
      __UpperCAmelCase					=   ['bias', 'LayerNorm.weight']
      __UpperCAmelCase					=   [
          {
              'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay					)],
              'weight_decay': self.args.weight_decay,
          },
          {
              'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay					)],
              'weight_decay': 0.0,
          },
      ]
      __UpperCAmelCase					=   Adafactor if self.args.adafactor else AdamW
      if self.args.adafactor:
       __UpperCAmelCase					=   Adafactor
       __UpperCAmelCase					=   {'scale_parameter': False, 'relative_step': False}
      else:
       __UpperCAmelCase					=   AdamW
       __UpperCAmelCase					=   {
           'betas': (self.args.adam_betaa, self.args.adam_betaa),
           'eps': self.args.adam_epsilon,
       }
      __UpperCAmelCase					=   self.args.learning_rate
      if self.sharded_ddp:
       __UpperCAmelCase					=   OSS(
           params=__A     ,			optim=__A     ,			**__A     ,			)
      else:
       __UpperCAmelCase					=   optimizer_cls(__A     ,			**__A					)

     if self.lr_scheduler is None:
      __UpperCAmelCase					=   self._get_lr_scheduler(__A					)
     else:  # ignoring --lr_scheduler
      logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
      __UpperCAmelCase					=   schedule_func(self.optimizer					)
     elif self.args.lr_scheduler == "constant_w_warmup":
      __UpperCAmelCase					=   schedule_func(self.optimizer     ,			num_warmup_steps=self.args.warmup_steps					)
     else:
      __UpperCAmelCase					=   schedule_func(
          self.optimizer     ,			num_warmup_steps=self.args.warmup_steps     ,			num_training_steps=__A					)
     return scheduler



    def 					__lowerCamelCase						(  self					):
     if isinstance(self.train_dataset     ,			torch.utils.data.IterableDataset					):
      return None
     elif is_torch_tpu_available():
      return get_tpu_sampler(self.train_dataset					)
     else:
      if self.args.sortish_sampler:
       self.train_dataset.make_sortish_sampler(
           self.args.per_device_train_batch_size     ,			distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)     ,			)

      return (
          RandomSampler(self.train_dataset					)
          if self.args.local_rank == -1
          else DistributedSampler(self.train_dataset					)
      )



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     if self.args.label_smoothing == 0:
      if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
       # force training to ignore pad token
       __UpperCAmelCase					=   model(**__A     ,			use_cache=__A					)[0]
       __UpperCAmelCase					=   self.loss_fn(logits.view(-1     ,			logits.shape[-1]					)     ,			labels.view(-1					)					)
      else:
       # compute usual loss via models
       __UpperCAmelCase					,    __UpperCAmelCase					=   model(**__A     ,			labels=__A     ,			use_cache=__A					)[:2]
     else:
      # compute label smoothed loss
      __UpperCAmelCase					=   model(**__A     ,			use_cache=__A					)[0]
      __UpperCAmelCase					=   torch.nn.functional.log_softmax(__A     ,			dim=-1					)
      __UpperCAmelCase					,    __UpperCAmelCase					=   self.loss_fn(__A     ,			__A     ,			self.args.label_smoothing     ,			ignore_index=self.config.pad_token_id					)
     return loss, logits



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   inputs.pop('labels'					)
     __UpperCAmelCase					,    __UpperCAmelCase					=   self._compute_loss(__A     ,			__A     ,			__A					)
     return loss



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A = None     ,			):
     __UpperCAmelCase					=   self._prepare_inputs(__A					)

     __UpperCAmelCase					=   {
         'max_length': self.data_args.val_max_target_length
         if self.data_args is not None
         else self.config.max_length,
         'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
     }

     if self.args.predict_with_generate and not self.args.prediction_loss_only:
      __UpperCAmelCase					=   self.model.generate(
          inputs['input_ids']     ,			attention_mask=inputs['attention_mask']     ,			**__A     ,			)
      # in case the batch is shorter than max length, the output should be padded
      if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
       __UpperCAmelCase					=   self._pad_tensors_to_max_len(__A     ,			gen_kwargs['max_length']					)

     __UpperCAmelCase					=   inputs.pop('labels'					)
     with torch.no_grad():
      # compute loss on predict data
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._compute_loss(__A     ,			__A     ,			__A					)

     __UpperCAmelCase					=   loss.mean().detach()
     if self.args.prediction_loss_only:
      return (loss, None, None)

     __UpperCAmelCase					=   generated_tokens if self.args.predict_with_generate else logits

     if labels.shape[-1] < gen_kwargs["max_length"]:
      __UpperCAmelCase					=   self._pad_tensors_to_max_len(__A     ,			gen_kwargs['max_length']					)

     return (loss, logits, labels)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # If PAD token is not defined at least EOS token has to be defined
     __UpperCAmelCase					=   self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

     if pad_token_id is None:
      raise ValueError(
          'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
          f' padded to `max_length`={max_length}'					)

     __UpperCAmelCase					=   pad_token_id * torch.ones(
         (tensor.shape[0], max_length)     ,			dtype=tensor.dtype     ,			device=tensor.device					)
     __UpperCAmelCase					=   tensor
     return padded_tensor












'''simple docstring'''




import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
    )






class UpperCAmelCase							:



    def __init__(  self     ,			__A     ,			__A=13     ,			__A=7     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=99     ,			__A=64     ,			__A=32     ,			__A=5     ,			__A=4     ,			__A=37     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=16     ,			__A=2     ,			__A=0.0_2     ,			__A=3     ,			__A=4     ,			__A=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					=   embedding_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					):
     __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					):
     return MegatronBertConfig(
         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     ,			embedding_size=self.embedding_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=__A     ,			initializer_range=self.initializer_range     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertModel(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A					)
     __UpperCAmelCase					=   model(__A     ,			token_type_ids=__A					)
     __UpperCAmelCase					=   model(__A					)

     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)					)
     self.parent.assertEqual(result.pooler_output.shape     ,			(self.batch_size, self.hidden_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForMaskedLM(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForCausalLM(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForNextSentencePrediction(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForPreTraining(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			next_sentence_label=__A     ,			)
     self.parent.assertEqual(result.prediction_logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)
     self.parent.assertEqual(result.seq_relationship_logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForQuestionAnswering(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			start_positions=__A     ,			end_positions=__A     ,			)
     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     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   MegatronBertForSequenceClassification(__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   MegatronBertForTokenClassification(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_choices
     __UpperCAmelCase					=   MegatronBertForMultipleChoice(config=__A					)
     model.to(__A					)
     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(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)					)




    def 					__lowerCamelCase						(  self					):
     __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 UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_					,			unittest.TestCase							):
    _A :			str						   =       (
        (
            MegatronBertModel,
            MegatronBertForMaskedLM,
            MegatronBertForCausalLM,
            MegatronBertForMultipleChoice,
            MegatronBertForNextSentencePrediction,
            MegatronBertForPreTraining,
            MegatronBertForQuestionAnswering,
            MegatronBertForSequenceClassification,
            MegatronBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _A :			Dict						   =       (
        {
            """feature-extraction""": MegatronBertModel,
            """fill-mask""": MegatronBertForMaskedLM,
            """question-answering""": MegatronBertForQuestionAnswering,
            """text-classification""": MegatronBertForSequenceClassification,
            """text-generation""": MegatronBertForCausalLM,
            """token-classification""": MegatronBertForTokenClassification,
            """zero-shot""": MegatronBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _A :			List[str]						   =       True
    # test_resize_embeddings = False
    _A :			Tuple						   =       False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A=False					):
     __UpperCAmelCase					=   super()._prepare_for_class(__A     ,			__A     ,			return_labels=__A					)

     if return_labels:
      if model_class in get_values(__A					):
       __UpperCAmelCase					=   torch.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)     ,			dtype=torch.long     ,			device=__A					)
       __UpperCAmelCase					=   torch.zeros(
           self.model_tester.batch_size     ,			dtype=torch.long     ,			device=__A					)
     return inputs_dict



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   MegatronBertModelTester(self					)
     __UpperCAmelCase					=   ConfigTester(self     ,			config_class=__A     ,			hidden_size=37					)



    def 					__lowerCamelCase						(  self					):
     self.config_tester.run_common_tests()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_model(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_pretraining(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_question_answering(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__A					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_token_classification(*__A					)







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Dict:
 return torch.tensor(
     _lowerCAmelCase       ,    dtype=torch.long       ,    device=_lowerCAmelCase       ,    )


_A:					Any							=				1E-4






@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase							(		unittest.TestCase							):



    @slow
    @unittest.skip('Model is not available.'					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   'nvidia/megatron-bert-uncased-345m'
     if "MYDIR" in os.environ:
      __UpperCAmelCase					=   os.path.join(os.environ['MYDIR']     ,			__A					)
     __UpperCAmelCase					=   MegatronBertModel.from_pretrained(__A					)
     model.to(__A					)
     model.half()
     __UpperCAmelCase					=   _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(__A					)[0]
     __UpperCAmelCase					=   torch.Size((1, 9, 1_024)					)
     self.assertEqual(output.shape     ,			__A					)

     __UpperCAmelCase					=   [-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
     for ii in range(3					):
      for jj in range(3					):
       __UpperCAmelCase					=   output[0, ii, jj]
       __UpperCAmelCase					=   expected[3 * ii + jj]
       __UpperCAmelCase					=   'ii={} jj={} a={} b={}'.format(__A     ,			__A     ,			__A     ,			__A					)
       self.assertTrue(math.isclose(__A     ,			__A     ,			rel_tol=__A     ,			abs_tol=__A					)     ,			msg=__A					)





