






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

a		:	Tuple      					=			logging.getLogger()
a		:	Tuple      					=			torch.cuda.is_available()
a		:	str      					=			{
    "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-0_8,
    "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     lowerCamelCase__				(   __lowerCamelCase      :					Path  ,		__lowerCamelCase      :					list		):
  __UpperCAmelCase							:     Optional[Any]									=  """\n""".join(__lowerCamelCase		)
  Path(__lowerCamelCase		).open("""w"""		).writelines(__lowerCamelCase		)


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


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






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]		):
  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    a   (			lowercase__       ):



      """simple docstring"""


      @classmethod
      def      UpperCAmelCase				(				cls :      str )							->				List[Any]:
        logging.disable(logging.CRITICAL )  # remove noisy download output from tracebacks
        return cls
      @slow
      @require_torch_gpu
      def      UpperCAmelCase				(				self :      List[str] )							->				int:
        __UpperCAmelCase							:     int									=  list_models()
        __UpperCAmelCase							:     Tuple									=  """sshleifer"""
        __UpperCAmelCase							:     List[str]									=  [x.modelId for x in model_list if x.modelId.startswith(__lowercase )]
        __UpperCAmelCase							:     str									=  ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
        __UpperCAmelCase							:     Optional[int]									=  []
        for m in model_ids:
          if m in allowed_to_be_broken:
            continue
          try:
            AutoConfig.from_pretrained(__lowercase )
          except Exception:
            failures.append(__lowercase )
        assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Any:
        __UpperCAmelCase							:     List[str]									=  {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
        self._test_distiller_cli(__lowercase )
      def      UpperCAmelCase				(				self :      str )							->				Optional[Any]:
        __UpperCAmelCase							:     Dict									=  {
            """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""",
        }
        __UpperCAmelCase							:     str									=  self._test_distiller_cli(__lowercase		,				check_contents=__lowercase )

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

        __UpperCAmelCase							:     Tuple									=  self.get_auto_remove_tmp_dir()
        convert_pl_to_hf(ckpts[0]		,				transformer_ckpts[0].parent		,				__lowercase )
        assert os.path.exists(os.path.join(__lowercase		,				"""pytorch_model.bin""" ) )
      def      UpperCAmelCase				(				self :      Any )							->				List[Any]:
        __UpperCAmelCase							:     Union[str, Any]									=  AutoModelForSeqaSeqLM.from_pretrained(__lowercase )
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
        __UpperCAmelCase							:     Any									=  torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]		,				dtype=torch.long		,				device=model.device )
        __UpperCAmelCase							:     Dict									=  target_ids[:, :-1].contiguous()  # Why this line?
        __UpperCAmelCase							:     str									=  target_ids[:, 1:].clone()  # why clone?
        __UpperCAmelCase							:     Any									=  model(
            __lowercase		,				attention_mask=__lowercase		,				decoder_input_ids=__lowercase		,				labels=__lowercase		,				use_cache=__lowercase ).loss

        __UpperCAmelCase							:     Tuple									=  model(__lowercase		,				attention_mask=__lowercase		,				decoder_input_ids=__lowercase		,				use_cache=__lowercase ).logits

        __UpperCAmelCase							:     Optional[int]									=  nn.functional.log_softmax(__lowercase		,				dim=-1 )
        __UpperCAmelCase				,			__UpperCAmelCase							:     Any									=  label_smoothed_nll_loss(
            __lowercase		,				__lowercase		,				0.1		,				ignore_index=model.config.pad_token_id )
        with self.assertRaises(__lowercase ):
          # TODO: understand why this breaks
          self.assertEqual(__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Dict:
        __UpperCAmelCase							:     List[Any]									=  {
            """student_encoder_layers""": 2,
            """student_decoder_layers""": 1,
            """num_train_epochs""": 4,
            """val_check_interval""": 0.25,
            """alpha_hid""": 2.0,
            """task""": """translation""",
            """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
            """tokenizer_name""": MBART_TINY,
            """teacher""": MBART_TINY,
            """src_lang""": """en_XX""",
            """tgt_lang""": """ro_RO""",
        }
        __UpperCAmelCase							:     List[str]									=  self._test_distiller_cli(__lowercase		,				check_contents=__lowercase )
        assert model.model.config.model_type == "mbart"

        __UpperCAmelCase							:     int									=  list(Path(model.output_dir ).glob("""*.ckpt""" ) )
        self.assertEqual(1		,				len(__lowercase ) )
        __UpperCAmelCase							:     Union[str, Any]									=  list(Path(model.output_dir ).glob("""**/*.bin""" ) )
        __UpperCAmelCase							:     Tuple									=  list(Path(model.output_dir ).glob("""best_tfmr/*""" ) )
        assert len(__lowercase ) > 2
        self.assertEqual(len(__lowercase )		,				2 )
      def      UpperCAmelCase				(				self :      Any )							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  {
            """student_encoder_layers""": 1,
            """student_decoder_layers""": 1,
            """alpha_hid""": 2.0,
            """teacher""": T5_TINY,
            """model_name_or_path""": T5_TINY,
            """tokenizer_name""": T5_TINY,
        }
        self._test_distiller_cli(__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				int:
        __UpperCAmelCase							:     Union[str, Any]									=  {
            """teacher""": T5_TINY,
            """student""": T5_TINIER,
            """model_name_or_path""": T5_TINIER,
            """tokenizer_name""": T5_TINIER,
        }
        self._test_distiller_cli(__lowercase )




      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Optional[Any]		,				__lowercase :      Union[str, Any]=True )							->				Tuple:
        __UpperCAmelCase							:     Union[str, 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(__lowercase )
        __UpperCAmelCase							:     dict									=  CHEAP_ARGS.copy()
        __UpperCAmelCase							:     Dict									=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
        __UpperCAmelCase							:     Dict									=  self.get_auto_remove_tmp_dir()

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

        self.assertIn("""test_generations.txt"""		,				__lowercase )
        self.assertIn("""test_results.txt"""		,				__lowercase )

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


class    a   (			lowercase__       ):



      """simple docstring"""


      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]		,				)
      def      UpperCAmelCase				(				self :      str		,				__lowercase :      List[str] )							->				Any:
        __UpperCAmelCase							:     dict									=  CHEAP_ARGS.copy()
        __UpperCAmelCase							:     str									=  """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
        __UpperCAmelCase							:     Optional[int]									=  0.1 if task == """translation""" else 0

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

        __UpperCAmelCase							:     Dict									=  module.model.get_input_embeddings()
        assert not input_embeds.weight.requires_grad
        if model == T5_TINY:
          __UpperCAmelCase							:     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:
          __UpperCAmelCase							:     List[Any]									=  module.model.model
          __UpperCAmelCase							:     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:
          __UpperCAmelCase							:     Union[str, Any]									=  module.model.model
          __UpperCAmelCase							:     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

        __UpperCAmelCase							:     List[str]									=  load_json(module.output_dir / """text_batch.json""" )
        assert isinstance(__lowercase		,				__lowercase )
        assert len(__lowercase ) >= 4
      def      UpperCAmelCase				(				self :      Dict )							->				Optional[Any]:
        __UpperCAmelCase							:     dict									=  CHEAP_ARGS.copy()

        __UpperCAmelCase							:     int									=  """summarization"""
        __UpperCAmelCase							:     Tuple									=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

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

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

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




      def      UpperCAmelCase				(				self :      List[str] )							->				Dict:
        __UpperCAmelCase							:     dict									=  CHEAP_ARGS.copy()

        __UpperCAmelCase							:     int									=  """summarization"""
        __UpperCAmelCase							:     List[str]									=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

        __UpperCAmelCase							:     Any									=  BART_TINY
        __UpperCAmelCase							:     List[Any]									=  self.get_auto_remove_tmp_dir()

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

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

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










def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  if len(__lowerCamelCase		) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]		),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]		),
    ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


