



'''simple docstring'''







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)

SCREAMING_SNAKE_CASE__     : str				=   logging.getLogger()
SCREAMING_SNAKE_CASE__     : List[str]				=   torch.cuda.is_available()
SCREAMING_SNAKE_CASE__     : int				=   {
    """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__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      '\n'.join(SCREAMING_SNAKE_CASE     )
		Path(SCREAMING_SNAKE_CASE     ).open('w'     ).writelines(SCREAMING_SNAKE_CASE     )


SCREAMING_SNAKE_CASE__     : Optional[int]				=   [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
SCREAMING_SNAKE_CASE__     : int				=   ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """patrickvonplaten/t5-tiny-random"""
SCREAMING_SNAKE_CASE__     : List[Any]				=   """sshleifer/t5-tinier-random"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__     : Any				=   """sshleifer/tiny-mbart"""
SCREAMING_SNAKE_CASE__     : Dict				=   """sshleifer/tiny-marian-en-de"""
SCREAMING_SNAKE_CASE__     : List[Any]				=   """stas/tiny-wmt19-en-de"""


SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(SCREAMING_SNAKE_CASE ,		F'{split}.source'     ) ,		SCREAMING_SNAKE_CASE     )
				_dump_articles(os.path.join(SCREAMING_SNAKE_CASE ,		F'{split}.target'     ) ,		SCREAMING_SNAKE_CASE     )
		return tmp_dir




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@classmethod
			def 				_lowercase	(   cls  :							Union[str, Any]	):




					"""simple docstring"""







					logging.disable(logging.CRITICAL	)  # remove noisy download output from tracebacks
					return cls






			@slow
			@require_torch_gpu
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      list_models()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'sshleifer'
					SCREAMING_SNAKE_CASE_						:Any										=      [x.modelId for x in model_list if x.modelId.startswith(SCREAMING_SNAKE_CASE	)]
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      []
					for m in model_ids:
							if m in allowed_to_be_broken:
									continue
							try:
									AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
							except Exception:
									failures.append(SCREAMING_SNAKE_CASE	)
					assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
					self._test_distiller_cli(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[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',
					}
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self._test_distiller_cli(SCREAMING_SNAKE_CASE      ,       check_contents=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      list(Path(model.output_dir	).glob('*.ckpt'	)	)
					self.assertEqual(1      ,       len(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      list(Path(model.output_dir	).glob('**/*.bin'	)	)
					self.assertEqual(len(SCREAMING_SNAKE_CASE	)      ,       2	)
					SCREAMING_SNAKE_CASE_						:Dict										=      lmap(str.strip      ,       Path(model.hparams.data_dir	).joinpath('test.source'	).open().readlines()	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      tempfile.mktemp()  # XXX: not being cleaned up
					generate_summaries_or_translations(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       str(model.output_dir / 'best_tfmr'	)	)
					self.assertTrue(Path(SCREAMING_SNAKE_CASE	).exists()	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.get_auto_remove_tmp_dir()
					convert_pl_to_hf(ckpts[0]      ,       transformer_ckpts[0].parent      ,       SCREAMING_SNAKE_CASE	)
					assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE      ,       'pytorch_model.bin'	)	)






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]      ,       dtype=torch.long      ,       device=model.device	)
					SCREAMING_SNAKE_CASE_						:Dict										=      target_ids[:, :-1].contiguous()  # Why this line?
					SCREAMING_SNAKE_CASE_						:List[Any]										=      target_ids[:, 1:].clone()  # why clone?
					SCREAMING_SNAKE_CASE_						:int										=      model(
					    SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       decoder_input_ids=SCREAMING_SNAKE_CASE      ,       labels=SCREAMING_SNAKE_CASE      ,       use_cache=SCREAMING_SNAKE_CASE	).loss

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      model(SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       decoder_input_ids=SCREAMING_SNAKE_CASE      ,       use_cache=SCREAMING_SNAKE_CASE	).logits

					SCREAMING_SNAKE_CASE_						:Any										=      nn.functional.log_softmax(SCREAMING_SNAKE_CASE      ,       dim=-1	)
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      label_smoothed_nll_loss(
					    SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       0.1      ,       ignore_index=model.config.pad_token_id	)
					with self.assertRaises(SCREAMING_SNAKE_CASE	):
							# TODO: understand why this breaks
							self.assertEqual(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {
					    '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',
					}
					SCREAMING_SNAKE_CASE_						:int										=      self._test_distiller_cli(SCREAMING_SNAKE_CASE      ,       check_contents=SCREAMING_SNAKE_CASE	)
					assert model.model.config.model_type == "mbart"

					SCREAMING_SNAKE_CASE_						:List[Any]										=      list(Path(model.output_dir	).glob('*.ckpt'	)	)
					self.assertEqual(1      ,       len(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      list(Path(model.output_dir	).glob('**/*.bin'	)	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      list(Path(model.output_dir	).glob('best_tfmr/*'	)	)
					assert len(SCREAMING_SNAKE_CASE	) > 2
					self.assertEqual(len(SCREAMING_SNAKE_CASE	)      ,       2	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						: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(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      {
					    'teacher': T5_TINY,
					    'student': T5_TINIER,
					    'model_name_or_path': T5_TINIER,
					    'tokenizer_name': T5_TINIER,
					}
					self._test_distiller_cli(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=True	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[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(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:dict										=      CHEAP_ARGS.copy()
					SCREAMING_SNAKE_CASE_						:Any										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_auto_remove_tmp_dir()

					args_d.update(data_dir=SCREAMING_SNAKE_CASE      ,       output_dir=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      distill_main(argparse.Namespace(**SCREAMING_SNAKE_CASE	)	)
					if not check_contents:
							return model
					SCREAMING_SNAKE_CASE_						:Any										=      os.listdir(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      {os.path.basename(SCREAMING_SNAKE_CASE	) for p in contents}
					SCREAMING_SNAKE_CASE_						:str										=      [p for p in contents if p.endswith('ckpt'	)]
					assert len(SCREAMING_SNAKE_CASE	) > 0

					self.assertIn('test_generations.txt'      ,       SCREAMING_SNAKE_CASE	)
					self.assertIn('test_results.txt'      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      load_json(model.metrics_save_path	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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}']      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1	)
					self.assertEqual(len(metrics['val']	)      ,       SCREAMING_SNAKE_CASE	)
					self.assertEqual(len(metrics['test']	)      ,       1	)
					return model




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@parameterized.expand(
			    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]      ,       )
			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:dict										=      CHEAP_ARGS.copy()
					SCREAMING_SNAKE_CASE_						:Any										=      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      0.1 if task == 'translation' else 0

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.get_auto_remove_tmp_dir()
					args_d.update(
					    data_dir=SCREAMING_SNAKE_CASE      ,       model_name_or_path=SCREAMING_SNAKE_CASE      ,       tokenizer_name=SCREAMING_SNAKE_CASE      ,       train_batch_size=2      ,       eval_batch_size=2      ,       output_dir=SCREAMING_SNAKE_CASE      ,       do_predict=SCREAMING_SNAKE_CASE      ,       task=SCREAMING_SNAKE_CASE      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO'      ,       freeze_encoder=SCREAMING_SNAKE_CASE      ,       freeze_embeds=SCREAMING_SNAKE_CASE      ,       )
					assert "n_train" in args_d
					SCREAMING_SNAKE_CASE_						:List[Any]										=      argparse.Namespace(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      main(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      module.model.get_input_embeddings()
					assert not input_embeds.weight.requires_grad
					if model == T5_TINY:
							SCREAMING_SNAKE_CASE_						:List[str]										=      module.model.lm_head
							assert not lm_head.weight.requires_grad
							assert (lm_head.weight == input_embeds.weight).all().item()
					elif model == FSMT_TINY:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      module.model.model
							SCREAMING_SNAKE_CASE_						:Union[str, 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:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      module.model.model
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      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

					SCREAMING_SNAKE_CASE_						:List[str]										=      load_json(module.output_dir / 'text_batch.json'	)
					assert isinstance(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					assert len(SCREAMING_SNAKE_CASE	) >= 4






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:dict										=      CHEAP_ARGS.copy()

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'summarization'
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)

					args_d.update(
					    data_dir=SCREAMING_SNAKE_CASE      ,       tokenizer_name=SCREAMING_SNAKE_CASE      ,       train_batch_size=2      ,       eval_batch_size=2      ,       do_predict=SCREAMING_SNAKE_CASE      ,       task=SCREAMING_SNAKE_CASE      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO'      ,       freeze_encoder=SCREAMING_SNAKE_CASE      ,       freeze_embeds=SCREAMING_SNAKE_CASE      ,       )

					# test models whose config includes the extra_model_args
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      BART_TINY
					SCREAMING_SNAKE_CASE_						:int										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      args_d.copy()
					args_da.update(
					    model_name_or_path=SCREAMING_SNAKE_CASE      ,       output_dir=SCREAMING_SNAKE_CASE      ,       )
					SCREAMING_SNAKE_CASE_						:int										=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
					for p in extra_model_params:
							SCREAMING_SNAKE_CASE_						:Any										=      0.5
					SCREAMING_SNAKE_CASE_						:Any										=      argparse.Namespace(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      main(SCREAMING_SNAKE_CASE	)
					for p in extra_model_params:
							assert getattr(model.config      ,       SCREAMING_SNAKE_CASE	) == 0.5, f'failed to override the model config for param {p}'

					# test models whose config doesn't include the extra_model_args
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      T5_TINY
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Tuple										=      args_d.copy()
					args_da.update(
					    model_name_or_path=SCREAMING_SNAKE_CASE      ,       output_dir=SCREAMING_SNAKE_CASE      ,       )
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'encoder_layerdrop'
					SCREAMING_SNAKE_CASE_						:int										=      0.5
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      argparse.Namespace(**SCREAMING_SNAKE_CASE	)
					with pytest.raises(SCREAMING_SNAKE_CASE	) as excinfo:
							SCREAMING_SNAKE_CASE_						:List[str]										=      main(SCREAMING_SNAKE_CASE	)
					assert str(excinfo.value	) == f'model config doesn\'t have a `{unsupported_param}` attribute'






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:dict										=      CHEAP_ARGS.copy()

					SCREAMING_SNAKE_CASE_						:str										=      'summarization'
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)

					SCREAMING_SNAKE_CASE_						:int										=      BART_TINY
					SCREAMING_SNAKE_CASE_						:Any										=      self.get_auto_remove_tmp_dir()

					args_d.update(
					    data_dir=SCREAMING_SNAKE_CASE      ,       model_name_or_path=SCREAMING_SNAKE_CASE      ,       output_dir=SCREAMING_SNAKE_CASE      ,       tokenizer_name=SCREAMING_SNAKE_CASE      ,       train_batch_size=2      ,       eval_batch_size=2      ,       do_predict=SCREAMING_SNAKE_CASE      ,       task=SCREAMING_SNAKE_CASE      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO'      ,       freeze_encoder=SCREAMING_SNAKE_CASE      ,       freeze_embeds=SCREAMING_SNAKE_CASE      ,       )

					# emulate finetune.py
					SCREAMING_SNAKE_CASE_						:Dict										=      argparse.ArgumentParser()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE      ,       os.getcwd()	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      {'--help': True}

					# --help test
					with pytest.raises(SCREAMING_SNAKE_CASE	) as excinfo:
							with CaptureStdout() as cs:
									SCREAMING_SNAKE_CASE_						:int										=      parser.parse_args(SCREAMING_SNAKE_CASE	)
							assert False, "--help is expected to sys.exit"
					assert excinfo.type == SystemExit
					SCREAMING_SNAKE_CASE_						:Tuple										=      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
					SCREAMING_SNAKE_CASE_						:int										=      'non_existing_scheduler'
					SCREAMING_SNAKE_CASE_						:Tuple										=      {f'--lr_scheduler={unsupported_param}'}
					with pytest.raises(SCREAMING_SNAKE_CASE	) as excinfo:
							with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE_						:str										=      parser.parse_args(SCREAMING_SNAKE_CASE	)
							assert False, "invalid argument is expected to sys.exit"
					assert excinfo.type == SystemExit
					SCREAMING_SNAKE_CASE_						: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
					SCREAMING_SNAKE_CASE_						:List[str]										=      'cosine'
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      args_d.copy()
					SCREAMING_SNAKE_CASE_						:List[str]										=      supported_param
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      argparse.Namespace(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      main(SCREAMING_SNAKE_CASE	)
					assert (
					    getattr(model.hparams      ,       'lr_scheduler'	) == supported_param
					), f'lr_scheduler={supported_param} shouldn\'t fail'





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


SCREAMING_SNAKE_CASE__     : Dict				=   logging.get_logger(__name__)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def __init__(   self  :							List[Any]      ,       *SCREAMING_SNAKE_CASE  :							int      ,       **SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					warnings.warn(
					    'The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use MaskFormerImageProcessor instead.'      ,       SCREAMING_SNAKE_CASE      ,       )
					super().__init__(*SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
