"""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:				int    							=  logging.getLogger()
_UpperCamelCase:				Dict    							=  torch.cuda.is_available()
_UpperCamelCase:				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": 4_2,
    "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-0_8,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 1_2,
    "max_target_length": 1_2,
    "val_max_target_length": 1_2,
    "test_max_target_length": 1_2,
    "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   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


      lowercase  :      Union[str, Any]					=   '''\n'''.join(snake_case_				)
      Path(snake_case_				).open('w'				).writelines(snake_case_				)


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


_UpperCamelCase:				Dict    							=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def   lowercase__     (			_UpperCAmelCase				)     ->			List[str]:






      '''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   a__		(						UpperCamelCase_     ):


   @classmethod
   def        lowercase				(							cls      :							Union[str, Any]			)    ->       int:
         logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
         return cls


   @slow
   @require_torch_gpu
   def        lowercase				(							self      :							Optional[int]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   list_models()
         lowercase  :      List[Any]					=   '''sshleifer'''
         lowercase  :      Tuple					=   [x.modelId for x in model_list if x.modelId.startswith(__UpperCamelCase			)]
         lowercase  :      str					=   ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
         lowercase  :      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        lowercase				(							self      :							Union[str, Any]			)    ->       Dict:
         lowercase  :      Optional[int]					=   {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
         self._test_distiller_cli(__UpperCamelCase			)


   def        lowercase				(							self      :							Optional[Any]			)    ->       Any:
         lowercase  :      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''',
         }
         lowercase  :      int					=   self._test_distiller_cli(__UpperCamelCase,     check_contents=__UpperCamelCase			)

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

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

         lowercase  :      int					=   model(__UpperCamelCase,     attention_mask=__UpperCamelCase,     decoder_input_ids=__UpperCamelCase,     use_cache=__UpperCamelCase			).logits

         lowercase  :      Optional[Any]					=   nn.functional.log_softmax(__UpperCamelCase,     dim=-1			)
         lowercase  :      Dict					=   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      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      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''',
         }
         lowercase  :      int					=   self._test_distiller_cli(__UpperCamelCase,     check_contents=__UpperCamelCase			)
         assert model.model.config.model_type == "mbart"

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


   def        lowercase				(							self      :							Optional[int]			)    ->       Tuple:
         lowercase  :      str					=   {
             '''student_encoder_layers''': 1,
             '''student_decoder_layers''': 1,
             '''alpha_hid''': 2.0,
             '''teacher''': T5_TINY,
             '''model_name_or_path''': T5_TINY,
             '''tokenizer_name''': T5_TINY,
         }
         self._test_distiller_cli(__UpperCamelCase			)


   def        lowercase				(							self      :							Any			)    ->       Optional[Any]:
         lowercase  :      int					=   {
             '''teacher''': T5_TINY,
             '''student''': T5_TINIER,
             '''model_name_or_path''': T5_TINIER,
             '''tokenizer_name''': T5_TINIER,
         }
         self._test_distiller_cli(__UpperCamelCase			)






   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							str=True			)    ->       List[str]:
         lowercase  :      Tuple					=   {
             '''label_smoothing''': 0.0,
             '''early_stopping_patience''': -1,
             '''train_batch_size''': 1,
             '''eval_batch_size''': 2,
             '''max_epochs''': 2,
             '''alpha_mlm''': 0.2,
             '''alpha_ce''': 0.8,
             '''do_predict''': True,
             '''model_name_or_path''': '''sshleifer/tinier_bart''',
             '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
             '''val_check_interval''': 0.5,
         }
         default_updates.update(__UpperCamelCase			)
         lowercase  :      dict					=   CHEAP_ARGS.copy()
         lowercase  :      str					=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
         lowercase  :      Any					=   self.get_auto_remove_tmp_dir()

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

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

         lowercase  :      Optional[int]					=   load_json(model.metrics_save_path			)
         lowercase  :      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			)
         lowercase  :      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__		(						UpperCamelCase_     ):


   @parameterized.expand(
       [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],     )
   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         lowercase  :      dict					=   CHEAP_ARGS.copy()
         lowercase  :      List[Any]					=   '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
         lowercase  :      List[Any]					=   0.1 if task == '''translation''' else 0

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

         lowercase  :      List[str]					=   module.model.get_input_embeddings()
         assert not input_embeds.weight.requires_grad
         if model == T5_TINY:
               lowercase  :      Optional[int]					=   module.model.lm_head
               assert not lm_head.weight.requires_grad
               assert (lm_head.weight == input_embeds.weight).all().item()
         elif model == FSMT_TINY:
               lowercase  :      str					=   module.model.model
               lowercase  :      Optional[int]					=   fsmt.decoder.embed_positions
               assert not embed_pos.weight.requires_grad
               assert not fsmt.decoder.embed_tokens.weight.requires_grad
               # check that embeds are not the same
               assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
         else:
               lowercase  :      Union[str, Any]					=   module.model.model
               lowercase  :      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

         lowercase  :      Optional[Any]					=   load_json(module.output_dir / 'text_batch.json'			)
         assert isinstance(__UpperCamelCase,     __UpperCamelCase			)
         assert len(__UpperCamelCase			) >= 4


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         lowercase  :      dict					=   CHEAP_ARGS.copy()

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






   def        lowercase				(							self      :							int			)    ->       Optional[int]:
         lowercase  :      dict					=   CHEAP_ARGS.copy()

         lowercase  :      Tuple					=   '''summarization'''
         lowercase  :      Optional[int]					=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

         lowercase  :      List[str]					=   BART_TINY
         lowercase  :      Tuple					=   self.get_auto_remove_tmp_dir()

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

         # emulate finetune.py
         lowercase  :      str					=   argparse.ArgumentParser()
         lowercase  :      List[Any]					=   pl.Trainer.add_argparse_args(__UpperCamelCase			)
         lowercase  :      Any					=   SummarizationModule.add_model_specific_args(__UpperCamelCase,     os.getcwd()			)
         lowercase  :      Optional[int]					=   {'''--help''': True}

         # --help test
         with pytest.raises(__UpperCamelCase			) as excinfo:
               with CaptureStdout() as cs:
                     lowercase  :      str					=   parser.parse_args(__UpperCamelCase			)
               assert False, "--help is expected to sys.exit"
         assert excinfo.type == SystemExit
         lowercase  :      List[Any]					=   lightning_base.arg_to_scheduler_metavar
         assert expected in cs.out, "--help is expected to list the supported schedulers"

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





"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:				int    							=  TypeVar('T', bound='TaskTemplate')


@dataclass(frozen=SCREAMING_SNAKE_CASE__     )
class   a__		(						abc.ABC     ):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42


   def        lowercase				(							self      :							T,     lowerCAmelCase      :							Features			)    ->       T:
         return copy.deepcopy(self			)


   @property
   def        lowercase				(							self      :							Union[str, Any]			)    ->       Features:
         return Features(**self.input_schema,     **self.label_schema			)


   @property
   @abc.abstractmethod
   def        lowercase				(							self      :							Tuple			)    ->       Dict[str, str]:
         raise NotImplementedError






   @classmethod
   def        lowercase				(							cls      :							Type[T],     lowerCAmelCase      :							dict			)    ->       T:
         lowercase  :      Union[str, Any]					=   {f.name for f in dataclasses.fields(cls			)}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}			)
