


"""simple docstring"""





import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__UpperCAmelCase    				=      torch.cuda.is_available()
__UpperCAmelCase    				=      [" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCAmelCase    				=      ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCAmelCase    				=      {
    "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     lowercase__	(  lowerCamelCase     :  Path				,	lowerCamelCase     :  list				)					->     Optional[Any]:
  lowerCAmelCase__    :    Optional[int]								=					"\n".join(lowerCamelCase				)
  Path(lowerCamelCase				).open("w"				).writelines(lowerCamelCase				)





def     lowercase__	(  lowerCamelCase     :  str				)					->     Dict:
  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  lowercase_  ( a_						):







      @classmethod
      def 				_lowerCAmelCase       (      cls :							Tuple    ):
        return cls







      @require_torch_multi_gpu
      def 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    Any								=					{
            "no_teacher": True,
            "freeze_encoder": True,
            "gpus": 2,
            "overwrite_output_dir": True,
            "sortish_sampler": True,
        }
        self._test_distiller_cli_fork(_lowercase		,  check_contents=_lowercase    )







      def 				_lowerCAmelCase       (      self :							Union[str, Any]		,  _lowercase :							Optional[Any]		,  _lowercase :							Union[str, Any]=True    ):
        lowerCAmelCase__    :    List[str]								=					{
            "label_smoothing": 0.0,
            "early_stopping_patience": -1,
            "train_batch_size": 1,
            "eval_batch_size": 2,
            "max_epochs": 2,
            "alpha_mlm": 0.2,
            "alpha_ce": 0.8,
            "do_predict": True,
            "model_name_or_path": "sshleifer/tinier_bart",
            "teacher": CHEAP_ARGS["model_name_or_path"],
            "val_check_interval": 0.5,
        }
        default_updates.update(_lowercase    )
        lowerCAmelCase__    :    dict								=					CHEAP_ARGS.copy()
        lowerCAmelCase__    :    Tuple								=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
        lowerCAmelCase__    :    Tuple								=					self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=_lowercase		,  output_dir=_lowercase		,  **_lowercase    )

        def convert(_lowercase :							List[Any]		,  _lowercase :							Tuple    ):
          if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
            return ""
          if v is False or v is None:
            return ""
          if v is True:  # or len(str(v))==0:
            return f"--{k}"
          return f"--{k}={v}"

        lowerCAmelCase__    :    Any								=					[x for x in (convert(_lowercase		,  _lowercase    ) for k, v in args_d.items()) if len(_lowercase    )]
        lowerCAmelCase__    :    Optional[Any]								=					[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
        execute_subprocess_async(_lowercase		,  env=self.get_env()    )

        lowerCAmelCase__    :    Any								=					os.listdir(_lowercase    )
        lowerCAmelCase__    :    Union[str, Any]								=					{os.path.basename(_lowercase    ) for p in contents}
        lowerCAmelCase__    :    List[Any]								=					[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    )

        # get the following from the module, (we don't have access to `model` here)
        lowerCAmelCase__    :    int								=					os.path.join(_lowercase		,  "metrics.json"    )
        lowerCAmelCase__    :    Optional[Any]								=					"rouge2"

        lowerCAmelCase__    :    Dict								=					load_json(_lowercase    )
        # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
        print(_lowercase    )
        lowerCAmelCase__    :    List[str]								=					metrics["val"][-1]
        self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]		,  0.01    )
        self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]		,  _lowercase    )
        self.assertEqual(len(metrics["test"]    )		,  1    )
        lowerCAmelCase__    :    Union[str, Any]								=					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1    )
        self.assertEqual(len(metrics["val"]    )		,  _lowercase    )




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase    				=      {"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SEWDForCTC",
       "SEWDForSequenceClassification",
       "SEWDModel",
       "SEWDPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
