code: 
  
  
  import argparse
  import logging
  import os
  import sys
  import tempfile
  from pathlib import Path
  
  import lightning_base
  import pytest
  import pytorch_lightning as pl
  import torch
  from convert_pl_checkpoint_to_hf import convert_pl_to_hf
  from distillation import distill_main
  from finetune import SummarizationModule, main
  from huggingface_hub import list_models
  from parameterized import parameterized
  from run_eval import generate_summaries_or_translations
  from torch import nn
  
  from transformers import AutoConfig, AutoModelForSeqaSeqLM
  from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
  from utils import label_smoothed_nll_loss, lmap, load_json
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  UpperCamelCase__				       =			logging.getLogger()
  UpperCamelCase__				       =			torch.cuda.is_available()
  UpperCamelCase__				       =			{
      'max_tokens_per_batch': None,
      'supervise_forward': True,
      'normalize_hidden': True,
      'label_smoothing': 0.2,
      'eval_max_gen_length': None,
      'eval_beams': 1,
      'val_metric': 'loss',
      'save_top_k': 1,
      'adafactor': True,
      'early_stopping_patience': 2,
      'logger_name': 'default',
      'length_penalty': 0.5,
      'cache_dir': '',
      'task': 'summarization',
      'num_workers': 2,
      'alpha_hid': 0,
      'freeze_embeds': True,
      'enc_only': False,
      'tgt_suffix': '',
      'resume_from_checkpoint': None,
      'sortish_sampler': True,
      'student_decoder_layers': 1,
      'val_check_interval': 1.0,
      'output_dir': '',
      'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      'no_teacher': False,
      'fp16_opt_level': 'O1',
      'gpus': 1 if CUDA_AVAILABLE else 0,
      'n_tpu_cores': 0,
      'max_grad_norm': 1.0,
      'do_train': True,
      'do_predict': True,
      'accumulate_grad_batches': 1,
      'server_ip': '',
      'server_port': '',
      'seed': 42,
      'model_name_or_path': 'sshleifer/bart-tiny-random',
      'config_name': '',
      'tokenizer_name': 'facebook/bart-large',
      'do_lower_case': False,
      'learning_rate': 0.3,
      'lr_scheduler': 'linear',
      'weight_decay': 0.0,
      'adam_epsilon': 1e-08,
      'warmup_steps': 0,
      'max_epochs': 1,
      'train_batch_size': 2,
      'eval_batch_size': 2,
      'max_source_length': 12,
      'max_target_length': 12,
      'val_max_target_length': 12,
      'test_max_target_length': 12,
      'fast_dev_run': False,
      'no_cache': False,
      'n_train': -1,
      'n_val': -1,
      'n_test': -1,
      'student_encoder_layers': 1,
      'freeze_encoder': False,
      'auto_scale_batch_size': False,
      'overwrite_output_dir': False,
      'student': None,
  }
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Union[str, Any]           =			'''\n'''.join(UpperCAmelCase_			)
         Path(UpperCAmelCase_			).open('''w'''			).writelines(UpperCAmelCase_			)
  
  
  UpperCamelCase__				       =			[' Sam ate lunch today.', 'Sams lunch ingredients.']
  UpperCamelCase__				       =			['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
  UpperCamelCase__				       =			'patrickvonplaten/t5-tiny-random'
  UpperCamelCase__				       =			'sshleifer/t5-tinier-random'
  UpperCamelCase__				       =			'sshleifer/bart-tiny-random'
  UpperCamelCase__				       =			'sshleifer/tiny-mbart'
  UpperCamelCase__				       =			'sshleifer/tiny-marian-en-de'
  UpperCamelCase__				       =			'stas/tiny-wmt19-en-de'
  
  
  UpperCamelCase__				       =			logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         for split in ["train", "val", "test"]:
                _dump_articles(os.path.join(UpperCAmelCase_    ,     F'{split}.source'			)    ,     UpperCAmelCase_			)
                _dump_articles(os.path.join(UpperCAmelCase_    ,     F'{split}.target'			)    ,     UpperCAmelCase_			)
         return tmp_dir
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       @classmethod
       def   lowerCAmelCase_   ( cls       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
              return cls
  
  
  
  
       @slow
       @require_torch_gpu
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			list_models()
              _lowercase					:				Any           =			'''sshleifer'''
              _lowercase					:				Dict           =			[x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase		)]
              _lowercase					:				str           =			['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
              _lowercase					:				int           =			[]
              for m in model_ids:
                     if m in allowed_to_be_broken:
                            continue
                     try:
                            AutoConfig.from_pretrained(UpperCamelCase		)
                     except Exception:
                            failures.append(UpperCamelCase		)
              assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
              self._test_distiller_cli(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			{
                  '''student_encoder_layers''': 2,
                  '''student_decoder_layers''': 1,
                  '''max_epochs''': 4,
                  '''val_check_interval''': 0.25,
                  '''alpha_hid''': 2.0,
                  '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
              }
              _lowercase					:				Tuple           =			self._test_distiller_cli(UpperCamelCase	,      check_contents=UpperCamelCase		)
  
              _lowercase					:				str           =			list(Path(model.output_dir		).glob('''*.ckpt'''		)		)
              self.assertEqual(1	,      len(UpperCamelCase		)		)
              _lowercase					:				Optional[int]           =			list(Path(model.output_dir		).glob('''**/*.bin'''		)		)
              self.assertEqual(len(UpperCamelCase		)	,      2		)
              _lowercase					:				Tuple           =			lmap(str.strip	,      Path(model.hparams.data_dir		).joinpath('''test.source'''		).open().readlines()		)
              _lowercase					:				int           =			tempfile.mktemp()  # XXX: not being cleaned up
              generate_summaries_or_translations(UpperCamelCase	,      UpperCamelCase	,      str(model.output_dir / '''best_tfmr'''		)		)
              self.assertTrue(Path(UpperCamelCase		).exists()		)
  
              _lowercase					:				Tuple           =			self.get_auto_remove_tmp_dir()
              convert_pl_to_hf(ckpts[0]	,      transformer_ckpts[0].parent	,      UpperCamelCase		)
              assert os.path.exists(os.path.join(UpperCamelCase	,      '''pytorch_model.bin'''		)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase		)
              _lowercase       ,	_lowercase					:				List[Any]           =			model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
              _lowercase					:				Union[str, Any]           =			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]	,      dtype=torch.long	,      device=model.device		)
              _lowercase					:				str           =			target_ids[:, :-1].contiguous()  # Why this line?
              _lowercase					:				str           =			target_ids[:, 1:].clone()  # why clone?
              _lowercase					:				Dict           =			model(
                  UpperCamelCase	,      attention_mask=UpperCamelCase	,      decoder_input_ids=UpperCamelCase	,      labels=UpperCamelCase	,      use_cache=UpperCamelCase		).loss
  
              _lowercase					:				Union[str, Any]           =			model(UpperCamelCase	,      attention_mask=UpperCamelCase	,      decoder_input_ids=UpperCamelCase	,      use_cache=UpperCamelCase		).logits
  
              _lowercase					:				Dict           =			nn.functional.log_softmax(UpperCamelCase	,      dim=-1		)
              _lowercase       ,	_lowercase					:				Union[str, Any]           =			label_smoothed_nll_loss(
                  UpperCamelCase	,      UpperCamelCase	,      0.1	,      ignore_index=model.config.pad_token_id		)
              with self.assertRaises(UpperCamelCase		):
                     # TODO: understand why this breaks
                     self.assertEqual(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			{
                  '''student_encoder_layers''': 2,
                  '''student_decoder_layers''': 1,
                  '''num_train_epochs''': 4,
                  '''val_check_interval''': 0.25,
                  '''alpha_hid''': 2.0,
                  '''task''': '''translation''',
                  '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
                  '''tokenizer_name''': MBART_TINY,
                  '''teacher''': MBART_TINY,
                  '''src_lang''': '''en_XX''',
                  '''tgt_lang''': '''ro_RO''',
              }
              _lowercase					:				List[str]           =			self._test_distiller_cli(UpperCamelCase	,      check_contents=UpperCamelCase		)
              assert model.model.config.model_type == "mbart"
  
              _lowercase					:				Any           =			list(Path(model.output_dir		).glob('''*.ckpt'''		)		)
              self.assertEqual(1	,      len(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			list(Path(model.output_dir		).glob('''**/*.bin'''		)		)
              _lowercase					:				List[str]           =			list(Path(model.output_dir		).glob('''best_tfmr/*'''		)		)
              assert len(UpperCamelCase		) > 2
              self.assertEqual(len(UpperCamelCase		)	,      2		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			{
                  '''student_encoder_layers''': 1,
                  '''student_decoder_layers''': 1,
                  '''alpha_hid''': 2.0,
                  '''teacher''': T5_TINY,
                  '''model_name_or_path''': T5_TINY,
                  '''tokenizer_name''': T5_TINY,
              }
              self._test_distiller_cli(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			{
                  '''teacher''': T5_TINY,
                  '''student''': T5_TINIER,
                  '''model_name_or_path''': T5_TINIER,
                  '''tokenizer_name''': T5_TINIER,
              }
              self._test_distiller_cli(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any	,      UpperCamelCase       :       Tuple	,      UpperCamelCase       :       Optional[Any]=True		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			{
                  '''label_smoothing''': 0.0,
                  '''early_stopping_patience''': -1,
                  '''train_batch_size''': 1,
                  '''eval_batch_size''': 2,
                  '''max_epochs''': 2,
                  '''alpha_mlm''': 0.2,
                  '''alpha_ce''': 0.8,
                  '''do_predict''': True,
                  '''model_name_or_path''': '''sshleifer/tinier_bart''',
                  '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
                  '''val_check_interval''': 0.5,
              }
              default_updates.update(UpperCamelCase		)
              _lowercase					:				dict           =			CHEAP_ARGS.copy()
              _lowercase					:				Optional[Any]           =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
              _lowercase					:				Dict           =			self.get_auto_remove_tmp_dir()
  
              args_d.update(data_dir=UpperCamelCase	,      output_dir=UpperCamelCase	,      **UpperCamelCase		)
              _lowercase					:				Any           =			distill_main(argparse.Namespace(**UpperCamelCase		)		)
              if not check_contents:
                     return model
              _lowercase					:				List[Any]           =			os.listdir(UpperCamelCase		)
              _lowercase					:				List[str]           =			{os.path.basename(UpperCamelCase		) for p in contents}
              _lowercase					:				Optional[int]           =			[p for p in contents if p.endswith('''ckpt'''		)]
              assert len(UpperCamelCase		) > 0
  
              self.assertIn('''test_generations.txt'''	,      UpperCamelCase		)
              self.assertIn('''test_results.txt'''	,      UpperCamelCase		)
  
              _lowercase					:				Union[str, Any]           =			load_json(model.metrics_save_path		)
              _lowercase					:				Dict           =			metrics['''val'''][-1]
              self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']	,      0.01		)
              self.assertGreaterEqual(1.0	,      last_step_stats['''val_avg_gen_time''']		)
              self.assertIsInstance(last_step_stats[F'val_avg_{model.val_metric}']	,      UpperCamelCase		)
              _lowercase					:				List[Any]           =			int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1		)
              self.assertEqual(len(metrics['''val''']		)	,      UpperCamelCase		)
              self.assertEqual(len(metrics['''test''']		)	,      1		)
              return model
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       @parameterized.expand(
           [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]	,      )
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				dict           =			CHEAP_ARGS.copy()
              _lowercase					:				Optional[Any]           =			'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
              _lowercase					:				List[Any]           =			0.1 if task == '''translation''' else 0
  
              _lowercase					:				Optional[Any]           =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
              _lowercase					:				int           =			self.get_auto_remove_tmp_dir()
              args_d.update(
                  data_dir=UpperCamelCase	,      model_name_or_path=UpperCamelCase	,      tokenizer_name=UpperCamelCase	,      train_batch_size=2	,      eval_batch_size=2	,      output_dir=UpperCamelCase	,      do_predict=UpperCamelCase	,      task=UpperCamelCase	,      src_lang='''en_XX'''	,      tgt_lang='''ro_RO'''	,      freeze_encoder=UpperCamelCase	,      freeze_embeds=UpperCamelCase	,      )
              assert "n_train" in args_d
              _lowercase					:				Any           =			argparse.Namespace(**UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			main(UpperCamelCase		)
  
              _lowercase					:				Union[str, Any]           =			module.model.get_input_embeddings()
              assert not input_embeds.weight.requires_grad
              if model == T5_TINY:
                     _lowercase					:				List[Any]           =			module.model.lm_head
                     assert not lm_head.weight.requires_grad
                     assert (lm_head.weight == input_embeds.weight).all().item()
              elif model == FSMT_TINY:
                     _lowercase					:				int           =			module.model.model
                     _lowercase					:				int           =			fsmt.decoder.embed_positions
                     assert not embed_pos.weight.requires_grad
                     assert not fsmt.decoder.embed_tokens.weight.requires_grad
                     # check that embeds are not the same
                     assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
              else:
                     _lowercase					:				Union[str, Any]           =			module.model.model
                     _lowercase					:				Any           =			bart.decoder.embed_positions
                     assert not embed_pos.weight.requires_grad
                     assert not bart.shared.weight.requires_grad
                     # check that embeds are the same
                     assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
                     assert bart.decoder.embed_tokens == bart.shared
  
              _lowercase					:				int           =			load_json(module.output_dir / '''text_batch.json'''		)
              assert isinstance(UpperCamelCase	,      UpperCamelCase		)
              assert len(UpperCamelCase		) >= 4
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				dict           =			CHEAP_ARGS.copy()
  
              _lowercase					:				int           =			'''summarization'''
              _lowercase					:				Dict           =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
  
              args_d.update(
                  data_dir=UpperCamelCase	,      tokenizer_name=UpperCamelCase	,      train_batch_size=2	,      eval_batch_size=2	,      do_predict=UpperCamelCase	,      task=UpperCamelCase	,      src_lang='''en_XX'''	,      tgt_lang='''ro_RO'''	,      freeze_encoder=UpperCamelCase	,      freeze_embeds=UpperCamelCase	,      )
  
              # test models whose config includes the extra_model_args
              _lowercase					:				str           =			BART_TINY
              _lowercase					:				Union[str, Any]           =			self.get_auto_remove_tmp_dir()
              _lowercase					:				Union[str, Any]           =			args_d.copy()
              args_da.update(
                  model_name_or_path=UpperCamelCase	,      output_dir=UpperCamelCase	,      )
              _lowercase					:				Any           =			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
              for p in extra_model_params:
                     _lowercase					:				str           =			0.5
              _lowercase					:				Dict           =			argparse.Namespace(**UpperCamelCase		)
              _lowercase					:				Tuple           =			main(UpperCamelCase		)
              for p in extra_model_params:
                     assert getattr(model.config	,      UpperCamelCase		) == 0.5, F'failed to override the model config for param {p}'
  
              # test models whose config doesn't include the extra_model_args
              _lowercase					:				int           =			T5_TINY
              _lowercase					:				List[str]           =			self.get_auto_remove_tmp_dir()
              _lowercase					:				Optional[int]           =			args_d.copy()
              args_da.update(
                  model_name_or_path=UpperCamelCase	,      output_dir=UpperCamelCase	,      )
              _lowercase					:				List[Any]           =			'''encoder_layerdrop'''
              _lowercase					:				Optional[Any]           =			0.5
              _lowercase					:				Dict           =			argparse.Namespace(**UpperCamelCase		)
              with pytest.raises(UpperCamelCase		) as excinfo:
                     _lowercase					:				Union[str, Any]           =			main(UpperCamelCase		)
              assert str(excinfo.value		) == F'model config doesn\'t have a `{unsupported_param}` attribute'
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				dict           =			CHEAP_ARGS.copy()
  
              _lowercase					:				Any           =			'''summarization'''
              _lowercase					:				List[Any]           =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
  
              _lowercase					:				Optional[Any]           =			BART_TINY
              _lowercase					:				Tuple           =			self.get_auto_remove_tmp_dir()
  
              args_d.update(
                  data_dir=UpperCamelCase	,      model_name_or_path=UpperCamelCase	,      output_dir=UpperCamelCase	,      tokenizer_name=UpperCamelCase	,      train_batch_size=2	,      eval_batch_size=2	,      do_predict=UpperCamelCase	,      task=UpperCamelCase	,      src_lang='''en_XX'''	,      tgt_lang='''ro_RO'''	,      freeze_encoder=UpperCamelCase	,      freeze_embeds=UpperCamelCase	,      )
  
              # emulate finetune.py
              _lowercase					:				Dict           =			argparse.ArgumentParser()
              _lowercase					:				List[Any]           =			pl.Trainer.add_argparse_args(UpperCamelCase		)
              _lowercase					:				List[Any]           =			SummarizationModule.add_model_specific_args(UpperCamelCase	,      os.getcwd()		)
              _lowercase					:				str           =			{'''--help''': True}
  
              # --help test
              with pytest.raises(UpperCamelCase		) as excinfo:
                     with CaptureStdout() as cs:
                            _lowercase					:				str           =			parser.parse_args(UpperCamelCase		)
                     assert False, "--help is expected to sys.exit"
              assert excinfo.type == SystemExit
              _lowercase					:				Any           =			lightning_base.arg_to_scheduler_metavar
              assert expected in cs.out, "--help is expected to list the supported schedulers"
  
              # --lr_scheduler=non_existing_scheduler test
              _lowercase					:				Optional[int]           =			'''non_existing_scheduler'''
              _lowercase					:				List[Any]           =			{F'--lr_scheduler={unsupported_param}'}
              with pytest.raises(UpperCamelCase		) as excinfo:
                     with CaptureStderr() as cs:
                            _lowercase					:				Any           =			parser.parse_args(UpperCamelCase		)
                     assert False, "invalid argument is expected to sys.exit"
              assert excinfo.type == SystemExit
              _lowercase					:				Tuple           =			F'invalid choice: \'{unsupported_param}\''
              assert expected in cs.err, F'should have bailed on invalid choice of scheduler {unsupported_param}'
  
              # --lr_scheduler=existing_scheduler test
              _lowercase					:				str           =			'''cosine'''
              _lowercase					:				Tuple           =			args_d.copy()
              _lowercase					:				Optional[Any]           =			supported_param
              _lowercase					:				int           =			argparse.Namespace(**UpperCamelCase		)
              _lowercase					:				str           =			main(UpperCamelCase		)
              assert (
                  getattr(model.hparams	,      '''lr_scheduler'''		) == supported_param
              ), F'lr_scheduler={supported_param} shouldn\'t fail'
style_context: 
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  class 						UpperCAmelCase__   (      A_      ,						A_      ,						A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       @register_to_config
       def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       float	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       bool = False	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			nn.Embedding(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				Any           =			False
  
              _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)
  
              _lowercase					:				Any           =			TaConfig(
                  d_model=UpperCamelCase	,      num_heads=UpperCamelCase	,      d_kv=UpperCamelCase	,      d_ff=UpperCamelCase	,      feed_forward_proj=UpperCamelCase	,      dropout_rate=UpperCamelCase	,      is_decoder=UpperCamelCase	,      is_encoder_decoder=UpperCamelCase	,      )
              _lowercase					:				Any           =			nn.ModuleList()
              for lyr_num in range(UpperCamelCase		):
                     _lowercase					:				Tuple           =			TaBlock(UpperCamelCase		)
                     self.encoders.append(UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)
  
              # terminal relative positional encodings
              _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
              _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)
  
              _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
              _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
              x += self.position_encoding(UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)
  
              # inverted the attention mask
              _lowercase					:				Optional[int]           =			encoder_inputs.size()
              _lowercase					:				List[Any]           =			self.get_extended_attention_mask(UpperCamelCase	,      UpperCamelCase		)
  
              for lyr in self.encoders:
                     _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
              _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)
  
              return self.dropout_post(UpperCamelCase		), encoder_inputs_mask