'''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)

UpperCamelCase       :     Tuple			=    logging.getLogger()
UpperCamelCase       :     List[str]			=    torch.cuda.is_available()
UpperCamelCase       :     Dict			=    {
    """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__  (				snake_case       :				Path				,	snake_case       :				list  )   ->				Dict:







     """simple docstring"""
     a					:							Optional[int]							= '\n'.join(snake_case  )
     Path(snake_case  ).open('w'  ).writelines(snake_case  )


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


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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any  )   ->				Optional[int]:







     """simple docstring"""
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(snake_case				,	F"""{split}.source"""  )				,	snake_case  )
          _dump_articles(os.path.join(snake_case				,	F"""{split}.target"""  )				,	snake_case  )
     return tmp_dir



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @classmethod
    def    SCREAMING_SNAKE_CASE_     (					cls     :						Tuple):


         """simple docstring"""







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





    @slow
    @require_torch_gpu
    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							List[str]							= list_models()
         a					:							str							= 'sshleifer'
         a					:							str							= [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_)]
         a					:							List[Any]							= ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
         a					:							List[Any]							= []
         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    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							str							= {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
         self._test_distiller_cli(UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         a					:							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',
         }
         a					:							Dict							= self._test_distiller_cli(UpperCAmelCase_							,							check_contents=UpperCAmelCase_)

         a					:							Dict							= list(Path(model.output_dir).glob('*.ckpt'))
         self.assertEqual(1							,							len(UpperCAmelCase_))
         a					:							List[Any]							= list(Path(model.output_dir).glob('**/*.bin'))
         self.assertEqual(len(UpperCAmelCase_)							,							2)
         a					:							List[Any]							= lmap(str.strip							,							Path(model.hparams.data_dir).joinpath('test.source').open().readlines())
         a					:							Optional[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())

         a					:							Dict							= 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    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







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

         a					:							Union[str, Any]							= model(UpperCAmelCase_							,							attention_mask=UpperCAmelCase_							,							decoder_input_ids=UpperCAmelCase_							,							use_cache=UpperCAmelCase_).logits

         a					:							List[Any]							= nn.functional.log_softmax(UpperCAmelCase_							,							dim=-1)
         a							,			a					:							Optional[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    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							Tuple							= {
             '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',
         }
         a					:							Any							= self._test_distiller_cli(UpperCAmelCase_							,							check_contents=UpperCAmelCase_)
         assert model.model.config.model_type == "mbart"

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





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         a					:							Tuple							= {
             '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    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							int							= {
             'teacher': T5_TINY,
             'student': T5_TINIER,
             'model_name_or_path': T5_TINIER,
             'tokenizer_name': T5_TINIER,
         }
         self._test_distiller_cli(UpperCAmelCase_)







    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						Union[str, Any]							,							UpperCAmelCase_     :						Optional[Any]=True):


         """simple docstring"""







         a					:							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_)
         a					:							dict							= CHEAP_ARGS.copy()
         a					:							Any							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         a					:							Tuple							= self.get_auto_remove_tmp_dir()

         args_d.update(data_dir=UpperCAmelCase_							,							output_dir=UpperCAmelCase_							,							**UpperCAmelCase_)
         a					:							Optional[Any]							= distill_main(argparse.Namespace(**UpperCAmelCase_))
         if not check_contents:
              return model
         a					:							int							= os.listdir(UpperCAmelCase_)
         a					:							str							= {os.path.basename(UpperCAmelCase_) for p in contents}
         a					:							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_)

         a					:							Optional[Any]							= load_json(model.metrics_save_path)
         a					:							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}"""]							,							UpperCAmelCase_)
         a					:							str							= 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    SCREAMING_SNAKE_CASE_     (					self     :						str							,							UpperCAmelCase_     :						Any):


         """simple docstring"""







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

         a					:							Optional[Any]							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         a					:							Tuple							= 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
         a					:							int							= argparse.Namespace(**UpperCAmelCase_)
         a					:							Optional[Any]							= main(UpperCAmelCase_)

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

         a					:							Dict							= load_json(module.output_dir / 'text_batch.json')
         assert isinstance(UpperCAmelCase_							,							UpperCAmelCase_)
         assert len(UpperCAmelCase_) >= 4





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							dict							= CHEAP_ARGS.copy()

         a					:							List[Any]							= 'summarization'
         a					:							Optional[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
         a					:							List[str]							= BART_TINY
         a					:							Any							= self.get_auto_remove_tmp_dir()
         a					:							Union[str, Any]							= args_d.copy()
         args_da.update(
             model_name_or_path=UpperCAmelCase_							,							output_dir=UpperCAmelCase_							,							)
         a					:							Optional[Any]							= ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
         for p in extra_model_params:
              a					:							List[str]							= 0.5
         a					:							str							= argparse.Namespace(**UpperCAmelCase_)
         a					:							Optional[Any]							= 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
         a					:							Optional[Any]							= T5_TINY
         a					:							Tuple							= self.get_auto_remove_tmp_dir()
         a					:							str							= args_d.copy()
         args_da.update(
             model_name_or_path=UpperCAmelCase_							,							output_dir=UpperCAmelCase_							,							)
         a					:							Union[str, Any]							= 'encoder_layerdrop'
         a					:							Dict							= 0.5
         a					:							Dict							= argparse.Namespace(**UpperCAmelCase_)
         with pytest.raises(UpperCAmelCase_) as excinfo:
              a					:							Dict							= main(UpperCAmelCase_)
         assert str(excinfo.value) == f"""model config doesn't have a `{unsupported_param}` attribute"""







    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         a					:							dict							= CHEAP_ARGS.copy()

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

         a					:							Tuple							= BART_TINY
         a					:							List[str]							= 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
         a					:							Optional[int]							= argparse.ArgumentParser()
         a					:							List[Any]							= pl.Trainer.add_argparse_args(UpperCAmelCase_)
         a					:							Union[str, Any]							= SummarizationModule.add_model_specific_args(UpperCAmelCase_							,							os.getcwd())
         a					:							List[Any]							= {'--help': True}

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


'''simple docstring'''





import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      UpperCamelCase       :     List[Any]			=    []

      UpperCamelCase       :     Tuple			=    os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      UpperCamelCase       :     List[str]			=    glob.glob(pattern)
      # TODO: deal with TF/Flax too
      UpperCamelCase       :     List[Any]			=    [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            UpperCamelCase       :     List[str]			=    get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        UpperCamelCase       :     int			=    tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        UpperCamelCase       :     Union[str, Any]			=    tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    UpperCamelCase       :     List[Any]			=    None
                                    if k in ["vocab_size"]:
                                          UpperCamelCase       :     List[Any]			=    100
                                    elif k in ["max_position_embeddings"]:
                                          UpperCamelCase       :     Tuple			=    128
                                    elif k in ["hidden_size", "d_model"]:
                                          UpperCamelCase       :     Any			=    40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          UpperCamelCase       :     Tuple			=    5
                                    if target is not None and v > target:
                                          failures.append(
                                              f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                              f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                              f''' than {target}.'''
                                          )

      if len(failures) > 0:
            raise Exception(f'''There were {len(failures)} failures:\n''' + """\n""".join(failures))

