

from typing import Dict, Optional, Tuple

import torch
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> torch.Tensor:



    '''simple docstring'''






    lowerCAmelCase				:			Dict    			=      boundaries[1] - boundaries[0]
    lowerCAmelCase				:			int    			=      boundaries + step / 2
    lowerCAmelCase				:			Optional[Any]    			=      torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1				)],      dim=0				)
    return bin_centers
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      )					-> Tuple[torch.Tensor, torch.Tensor]:



    '''simple docstring'''






    lowerCAmelCase				:			Optional[Any]    			=      _calculate_bin_centers(_UpperCAmelCase				)
    return (
        torch.sum(aligned_distance_error_probs * bin_centers,      dim=-1				),
        bin_centers[-1],
    )
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase = 31,      _UpperCAmelCase = 64,      **_UpperCAmelCase,      )					-> Dict[str, torch.Tensor]:



    '''simple docstring'''






    lowerCAmelCase				:			Tuple    			=      torch.linspace(0,      _UpperCAmelCase,      steps=(no_bins - 1),      device=logits.device				)

    lowerCAmelCase				:			Any    			=      torch.nn.functional.softmax(_UpperCAmelCase,      dim=-1				)
    lowerCAmelCase			,  lowerCAmelCase				:			str    			=      _calculate_expected_aligned_error(
        alignment_confidence_breaks=_UpperCAmelCase,      aligned_distance_error_probs=_UpperCAmelCase,      )

    return {
        "aligned_confidence_probs": aligned_confidence_probs,
        "predicted_aligned_error": predicted_aligned_error,
        "max_predicted_aligned_error": max_predicted_aligned_error,
    }






def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase = None,      _UpperCAmelCase = 31,      _UpperCAmelCase = 64,      _UpperCAmelCase = 1e-8,      **_UpperCAmelCase,      )					-> torch.Tensor:



    '''simple docstring'''






    if residue_weights is None:
        lowerCAmelCase				:			Any    			=      logits.new_ones(logits.shape[-2]				)

    lowerCAmelCase				:			str    			=      torch.linspace(0,      _UpperCAmelCase,      steps=(no_bins - 1),      device=logits.device				)

    lowerCAmelCase				:			Optional[int]    			=      _calculate_bin_centers(_UpperCAmelCase				)
    torch.sum(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[Any]    			=      logits.shape[-2]
    lowerCAmelCase				:			Any    			=      max(_UpperCAmelCase,      19				)

    lowerCAmelCase				:			Any    			=      1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

    lowerCAmelCase				:			List[Any]    			=      torch.nn.functional.softmax(_UpperCAmelCase,      dim=-1				)

    lowerCAmelCase				:			List[str]    			=      1.0 / (1 + (bin_centers**2) / (da**2))
    lowerCAmelCase				:			Any    			=      torch.sum(probs * tm_per_bin,      dim=-1				)

    lowerCAmelCase				:			Optional[Any]    			=      residue_weights / (eps + residue_weights.sum())
    lowerCAmelCase				:			Optional[Any]    			=      torch.sum(predicted_tm_term * normed_residue_mask,      dim=-1				)

    lowerCAmelCase				:			List[str]    			=      per_alignment * residue_weights

    lowerCAmelCase				:			Any    			=      (weighted == torch.max(_UpperCAmelCase				)).nonzero()[0]
    return per_alignment[tuple(_UpperCAmelCase				)]








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"





