code: 
  
  import random
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  
  from ..state import AcceleratorState
  from .constants import CUDA_DISTRIBUTED_TYPES
  from .dataclasses import DistributedType, RNGType
  from .imports import is_npu_available, is_tpu_available, is_xpu_available
  
  
  if is_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase = False				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if device_specific:
          seed += AcceleratorState().process_index
      random.seed(_UpperCAmelCase				)
      np.random.seed(_UpperCAmelCase				)
      torch.manual_seed(_UpperCAmelCase				)
      if is_xpu_available():
          torch.xpu.manual_seed_all(_UpperCAmelCase				)
      elif is_npu_available():
          torch.npu.manual_seed_all(_UpperCAmelCase				)
      else:
          torch.cuda.manual_seed_all(_UpperCAmelCase				)
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
          xm.set_rng_state(_UpperCAmelCase				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = None,      _UpperCAmelCase = None				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if rng_type == RNGType.TORCH:
          lowerCAmelCase				:			List[str]    			=      torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
          lowerCAmelCase				:			int    			=      torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
          assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
          lowerCAmelCase				:			Union[str, Any]    			=      torch.tensor(xm.get_rng_state()				)
      elif rng_type == RNGType.NPU:
          assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
          lowerCAmelCase				:			List[Any]    			=      torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
          assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
          lowerCAmelCase				:			Optional[Any]    			=      torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
          assert generator is not None, "Need a generator to synchronize its seed."
          lowerCAmelCase				:			Tuple    			=      generator.get_state()
  
      # Broadcast the rng state from device 0 to other devices
      lowerCAmelCase				:			Optional[int]    			=      AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
          lowerCAmelCase				:			str    			=      rng_state.to(xm.xla_device()				)
          xm.collective_broadcast([rng_state]				)
          xm.mark_step()
          lowerCAmelCase				:			int    			=      rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
          lowerCAmelCase				:			Union[str, Any]    			=      rng_state.to(state.device				)
          torch.distributed.broadcast(_UpperCAmelCase,      0				)
          lowerCAmelCase				:			List[str]    			=      rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
          torch.distributed.broadcast(_UpperCAmelCase,      0				)
  
      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
          torch.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.CUDA:
          torch.cuda.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.NPU:
          torch.npu.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.XPU:
          torch.xpu.set_rng_state(_UpperCAmelCase				)
      elif rng_type == RNGType.XLA:
          xm.set_rng_state(rng_state.item()				)
      elif rng_type == RNGType.GENERATOR:
          generator.set_state(_UpperCAmelCase				)
  
  
  
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase = None				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      for rng_type in rng_types:
          synchronize_rng_state(RNGType(_UpperCAmelCase				),      generator=_UpperCAmelCase				)
  
  
  
  
  
  
style_context: 
  
  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)
  
  __A   :     Dict      =			logging.getLogger()
  __A   :     str      =			torch.cuda.is_available()
  __A   :     Union[str, Any]      =			{
      '''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 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> Dict:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      '\n'.join(_UpperCAmelCase				)
      Path(_UpperCAmelCase				).open('w'				).writelines(_UpperCAmelCase				)
  
  
  __A   :     Optional[int]      =			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  __A   :     Dict      =			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  __A   :     str      =			'''patrickvonplaten/t5-tiny-random'''
  __A   :     int      =			'''sshleifer/t5-tinier-random'''
  __A   :     Optional[int]      =			'''sshleifer/bart-tiny-random'''
  __A   :     List[str]      =			'''sshleifer/tiny-mbart'''
  __A   :     int      =			'''sshleifer/tiny-marian-en-de'''
  __A   :     Tuple      =			'''stas/tiny-wmt19-en-de'''
  
  
  __A   :     Any      =			logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Optional[int]:
  
  
  
      '''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    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      @classmethod
      def 	lowercase__ (	cls			:    Optional[int]    ):
          logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
          return cls
  
  
  
  
  
  
      @slow
      @require_torch_gpu
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Union[str, Any]    			=      list_models()
          lowerCAmelCase				:			Optional[int]    			=      'sshleifer'
          lowerCAmelCase				:			List[str]    			=      [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_    )]
          lowerCAmelCase				:			Optional[int]    			=      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
          lowerCAmelCase				:			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 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			List[Any]    			=      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
          self._test_distiller_cli(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			List[str]    			=      {
              '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',
          }
          lowerCAmelCase				:			Dict    			=      self._test_distiller_cli(UpperCAmelCase_    ,				check_contents=UpperCAmelCase_    )
  
          lowerCAmelCase				:			int    			=      list(Path(model.output_dir    ).glob('*.ckpt'    )    )
          self.assertEqual(1    ,				len(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Any    			=      list(Path(model.output_dir    ).glob('**/*.bin'    )    )
          self.assertEqual(len(UpperCAmelCase_    )    ,				2    )
          lowerCAmelCase				:			Dict    			=      lmap(str.strip    ,				Path(model.hparams.data_dir    ).joinpath('test.source'    ).open().readlines()    )
          lowerCAmelCase				:			Optional[Any]    			=      tempfile.mktemp()  # XXX: not being cleaned up
          generate_summaries_or_translations(UpperCAmelCase_    ,				UpperCAmelCase_    ,				str(model.output_dir / 'best_tfmr'    )    )
          self.assertTrue(Path(UpperCAmelCase_    ).exists()    )
  
          lowerCAmelCase				:			Optional[int]    			=      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 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			Optional[int]    			=      AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_    )
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
          lowerCAmelCase				:			List[Any]    			=      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,				dtype=torch.long    ,				device=model.device    )
          lowerCAmelCase				:			List[str]    			=      target_ids[:, :-1].contiguous()  # Why this line?
          lowerCAmelCase				:			List[Any]    			=      target_ids[:, 1:].clone()  # why clone?
          lowerCAmelCase				:			Any    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				decoder_input_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    ).loss
  
          lowerCAmelCase				:			Union[str, Any]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				decoder_input_ids=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    ).logits
  
          lowerCAmelCase				:			Optional[int]    			=      nn.functional.log_softmax(UpperCAmelCase_    ,				dim=-1    )
          lowerCAmelCase			,  lowerCAmelCase				:			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 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Any    			=      {
              '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',
          }
          lowerCAmelCase				:			Dict    			=      self._test_distiller_cli(UpperCAmelCase_    ,				check_contents=UpperCAmelCase_    )
          assert model.model.config.model_type == "mbart"
  
          lowerCAmelCase				:			Any    			=      list(Path(model.output_dir    ).glob('*.ckpt'    )    )
          self.assertEqual(1    ,				len(UpperCAmelCase_    )    )
          lowerCAmelCase				:			int    			=      list(Path(model.output_dir    ).glob('**/*.bin'    )    )
          lowerCAmelCase				:			Dict    			=      list(Path(model.output_dir    ).glob('best_tfmr/*'    )    )
          assert len(UpperCAmelCase_    ) > 2
          self.assertEqual(len(UpperCAmelCase_    )    ,				2    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			Union[str, Any]    			=      {
              '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 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Union[str, Any]    			=      {
              'teacher': T5_TINY,
              'student': T5_TINIER,
              'model_name_or_path': T5_TINIER,
              'tokenizer_name': T5_TINIER,
          }
          self._test_distiller_cli(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Dict=True    ):
          lowerCAmelCase				:			Optional[Any]    			=      {
              '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_    )
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
          lowerCAmelCase				:			Optional[Any]    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
          lowerCAmelCase				:			List[str]    			=      self.get_auto_remove_tmp_dir()
  
          args_d.update(data_dir=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				**UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      distill_main(argparse.Namespace(**UpperCAmelCase_    )    )
          if not check_contents:
              return model
          lowerCAmelCase				:			Tuple    			=      os.listdir(UpperCAmelCase_    )
          lowerCAmelCase				:			Union[str, Any]    			=      {os.path.basename(UpperCAmelCase_    ) for p in contents}
          lowerCAmelCase				:			List[str]    			=      [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_    )
  
          lowerCAmelCase				:			Optional[int]    			=      load_json(model.metrics_save_path    )
          lowerCAmelCase				:			Optional[int]    			=      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_    )
          lowerCAmelCase				:			Optional[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    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,				)
      def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    int    ):
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
          lowerCAmelCase				:			Any    			=      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
          lowerCAmelCase				:			Union[str, Any]    			=      0.1 if task == 'translation' else 0
  
          lowerCAmelCase				:			Optional[int]    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
          lowerCAmelCase				:			str    			=      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
          lowerCAmelCase				:			List[str]    			=      argparse.Namespace(**UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      main(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Tuple    			=      module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              lowerCAmelCase				:			Union[str, 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:
              lowerCAmelCase				:			Union[str, Any]    			=      module.model.model
              lowerCAmelCase				:			Any    			=      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:
              lowerCAmelCase				:			Tuple    			=      module.model.model
              lowerCAmelCase				:			Dict    			=      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
  
          lowerCAmelCase				:			Tuple    			=      load_json(module.output_dir / 'text_batch.json'    )
          assert isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    )
          assert len(UpperCAmelCase_    ) >= 4
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
  
          lowerCAmelCase				:			List[Any]    			=      'summarization'
          lowerCAmelCase				:			int    			=      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
          lowerCAmelCase				:			Optional[int]    			=      BART_TINY
          lowerCAmelCase				:			int    			=      self.get_auto_remove_tmp_dir()
          lowerCAmelCase				:			int    			=      args_d.copy()
          args_da.update(
              model_name_or_path=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			List[Any]    			=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
          for p in extra_model_params:
              lowerCAmelCase				:			str    			=      0.5
          lowerCAmelCase				:			Any    			=      argparse.Namespace(**UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      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
          lowerCAmelCase				:			Any    			=      T5_TINY
          lowerCAmelCase				:			Any    			=      self.get_auto_remove_tmp_dir()
          lowerCAmelCase				:			Tuple    			=      args_d.copy()
          args_da.update(
              model_name_or_path=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			Any    			=      'encoder_layerdrop'
          lowerCAmelCase				:			Dict    			=      0.5
          lowerCAmelCase				:			str    			=      argparse.Namespace(**UpperCAmelCase_    )
          with pytest.raises(UpperCAmelCase_    ) as excinfo:
              lowerCAmelCase				:			Optional[Any]    			=      main(UpperCAmelCase_    )
          assert str(excinfo.value    ) == f"model config doesn't have a `{unsupported_param}` attribute"
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
  
          lowerCAmelCase				:			Dict    			=      'summarization'
          lowerCAmelCase				:			Union[str, Any]    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
  
          lowerCAmelCase				:			List[str]    			=      BART_TINY
          lowerCAmelCase				:			Dict    			=      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
          lowerCAmelCase				:			Dict    			=      argparse.ArgumentParser()
          lowerCAmelCase				:			List[str]    			=      pl.Trainer.add_argparse_args(UpperCAmelCase_    )
          lowerCAmelCase				:			Tuple    			=      SummarizationModule.add_model_specific_args(UpperCAmelCase_    ,				os.getcwd()    )
          lowerCAmelCase				:			str    			=      {'--help': True}
  
          # --help test
          with pytest.raises(UpperCAmelCase_    ) as excinfo:
              with CaptureStdout() as cs:
                  lowerCAmelCase				:			List[str]    			=      parser.parse_args(UpperCAmelCase_    )
              assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          lowerCAmelCase				:			Optional[int]    			=      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
          lowerCAmelCase				:			int    			=      'non_existing_scheduler'
          lowerCAmelCase				:			Optional[Any]    			=      {f"--lr_scheduler={unsupported_param}"}
          with pytest.raises(UpperCAmelCase_    ) as excinfo:
              with CaptureStderr() as cs:
                  lowerCAmelCase				:			int    			=      parser.parse_args(UpperCAmelCase_    )
              assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          lowerCAmelCase				:			Dict    			=      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
          lowerCAmelCase				:			Optional[int]    			=      'cosine'
          lowerCAmelCase				:			Tuple    			=      args_d.copy()
          lowerCAmelCase				:			str    			=      supported_param
          lowerCAmelCase				:			List[str]    			=      argparse.Namespace(**UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      main(UpperCAmelCase_    )
          assert (
              getattr(model.hparams    ,				'lr_scheduler'    ) == supported_param
          ), f"lr_scheduler={supported_param} shouldn't fail"
  
  
  
  
  
  