
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)

__lowerCAmelCase   :     Optional[Any]      =						logging.getLogger()
__lowerCAmelCase   :     Dict      =						torch.cuda.is_available()
__lowerCAmelCase   :     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       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Any:
    __lowercase						:     Union[str, Any]		      =    '''\n'''.join(__lowerCAmelCase		)
    Path(__lowerCAmelCase		).open('''w'''		).writelines(__lowerCAmelCase		)


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


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


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  Tuple:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(__lowerCAmelCase		,   F'{split}.source'		)		,   __lowerCAmelCase		)
        _dump_articles(os.path.join(__lowerCAmelCase		,   F'{split}.target'		)		,   __lowerCAmelCase		)
    return tmp_dir


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      @classmethod
      def 						snake_case_     ( cls						:    Dict			):
          logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
          return cls




      @slow
      @require_torch_gpu
      def 						snake_case_     ( self						:    Dict			):
          __lowercase						:     Any		      =    list_models()
          __lowercase						:     Optional[int]		      =    '''sshleifer'''
          __lowercase						:     Dict		      =    [x.modelId for x in model_list if x.modelId.startswith(_snake_case			)]
          __lowercase						:     Dict		      =    ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
          __lowercase						:     Tuple		      =    []
          for m in model_ids:
              if m in allowed_to_be_broken:
                  continue
              try:
                  AutoConfig.from_pretrained(_snake_case			)
              except Exception:
                  failures.append(_snake_case			)
          assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'




      def 						snake_case_     ( self						:    Union[str, Any]			):
          __lowercase						:     Optional[Any]		      =    {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
          self._test_distiller_cli(_snake_case			)




      def 						snake_case_     ( self						:    List[Any]			):
          __lowercase						:     int		      =    {
              '''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						:     Optional[int]		      =    self._test_distiller_cli(_snake_case			,    check_contents=_snake_case			)

          __lowercase						:     Dict		      =    list(Path(model.output_dir			).glob('''*.ckpt'''			)			)
          self.assertEqual(1			,    len(_snake_case			)			)
          __lowercase						:     Union[str, Any]		      =    list(Path(model.output_dir			).glob('''**/*.bin'''			)			)
          self.assertEqual(len(_snake_case			)			,    2			)
          __lowercase						:     int		      =    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(_snake_case			,    _snake_case			,    str(model.output_dir / '''best_tfmr'''			)			)
          self.assertTrue(Path(_snake_case			).exists()			)

          __lowercase						:     Tuple		      =    self.get_auto_remove_tmp_dir()
          convert_pl_to_hf(ckpts[0]			,    transformer_ckpts[0].parent			,    _snake_case			)
          assert os.path.exists(os.path.join(_snake_case			,    '''pytorch_model.bin'''			)			)




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

          __lowercase						:     Optional[Any]		      =    model(_snake_case			,    attention_mask=_snake_case			,    decoder_input_ids=_snake_case			,    use_cache=_snake_case			).logits

          __lowercase						:     Union[str, Any]		      =    nn.functional.log_softmax(_snake_case			,    dim=-1			)
          __lowercase						:     List[str]		      =    label_smoothed_nll_loss(
              _snake_case			,    _snake_case			,    0.1			,    ignore_index=model.config.pad_token_id			)
          with self.assertRaises(_snake_case			):
              # TODO: understand why this breaks
              self.assertEqual(_snake_case			,    _snake_case			)




      def 						snake_case_     ( self						:    str			):
          __lowercase						:     Dict		      =    {
              '''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						:     Optional[Any]		      =    self._test_distiller_cli(_snake_case			,    check_contents=_snake_case			)
          assert model.model.config.model_type == "mbart"

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




      def 						snake_case_     ( self						:    Tuple			):
          __lowercase						:     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(_snake_case			)




      def 						snake_case_     ( self						:    Optional[int]			):
          __lowercase						:     Optional[int]		      =    {
              '''teacher''': T5_TINY,
              '''student''': T5_TINIER,
              '''model_name_or_path''': T5_TINIER,
              '''tokenizer_name''': T5_TINIER,
          }
          self._test_distiller_cli(_snake_case			)




      def 						snake_case_     ( self						:    int			,    _snake_case						:    Optional[Any]			,    _snake_case						:    int=True			):
          __lowercase						:     List[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(_snake_case			)
          __lowercase						:     dict		      =    CHEAP_ARGS.copy()
          __lowercase						:     Optional[Any]		      =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
          __lowercase						:     Union[str, Any]		      =    self.get_auto_remove_tmp_dir()

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

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

          __lowercase						:     List[str]		      =    load_json(model.metrics_save_path			)
          __lowercase						:     Dict		      =    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}']			,    _snake_case			)
          __lowercase						:     int		      =    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1			)
          self.assertEqual(len(metrics['''val''']			)			,    _snake_case			)
          self.assertEqual(len(metrics['''test''']			)			,    1			)
          return model






class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,    )
      def 						snake_case_     ( self						:    int			,    _snake_case						:    Optional[Any]			):
          __lowercase						:     dict		      =    CHEAP_ARGS.copy()
          __lowercase						:     str		      =    '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
          __lowercase						:     Optional[Any]		      =    0.1 if task == '''translation''' else 0

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

          __lowercase						:     int		      =    module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              __lowercase						:     Tuple		      =    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						:     Dict		      =    module.model.model
              __lowercase						:     Tuple		      =    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						:     List[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						:     List[Any]		      =    load_json(module.output_dir / '''text_batch.json'''			)
          assert isinstance(_snake_case			,    _snake_case			)
          assert len(_snake_case			) >= 4




      def 						snake_case_     ( self						:    str			):
          __lowercase						:     dict		      =    CHEAP_ARGS.copy()

          __lowercase						:     Dict		      =    '''summarization'''
          __lowercase						:     int		      =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

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

          # test models whose config includes the extra_model_args
          __lowercase						:     Any		      =    BART_TINY
          __lowercase						:     int		      =    self.get_auto_remove_tmp_dir()
          __lowercase						:     List[Any]		      =    args_d.copy()
          args_da.update(
              model_name_or_path=_snake_case			,    output_dir=_snake_case			,    )
          __lowercase						:     List[Any]		      =    ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
          for p in extra_model_params:
              __lowercase						:     int		      =    0.5
          __lowercase						:     Optional[Any]		      =    argparse.Namespace(**_snake_case			)
          __lowercase						:     Union[str, Any]		      =    main(_snake_case			)
          for p in extra_model_params:
              assert getattr(model.config			,    _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
          __lowercase						:     List[Any]		      =    T5_TINY
          __lowercase						:     Optional[Any]		      =    self.get_auto_remove_tmp_dir()
          __lowercase						:     Any		      =    args_d.copy()
          args_da.update(
              model_name_or_path=_snake_case			,    output_dir=_snake_case			,    )
          __lowercase						:     int		      =    '''encoder_layerdrop'''
          __lowercase						:     Tuple		      =    0.5
          __lowercase						:     Tuple		      =    argparse.Namespace(**_snake_case			)
          with pytest.raises(_snake_case			) as excinfo:
              __lowercase						:     Optional[Any]		      =    main(_snake_case			)
          assert str(excinfo.value			) == F'model config doesn\'t have a `{unsupported_param}` attribute'




      def 						snake_case_     ( self						:    int			):
          __lowercase						:     dict		      =    CHEAP_ARGS.copy()

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

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

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

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

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




__lowerCAmelCase   :     List[Any]      =						"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase   :     Optional[Any]      =						[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase   :     List[str]      =						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


