






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Tuple      )  -> int:





    """simple docstring"""

    __A             =		filter(lambda __lowercase      : p.requires_grad     ,			model.parameters()      )
    __A             =		sum([np.prod(p.size()      ) for p in model_parameters]      )
    return params


__a :    List[str]								=				logging.getLogger(__name__)


class  __lowercase (		pl.Callback					):







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     List[Any]	,		UpperCamelCase_    :     Any	,		UpperCamelCase_    :     Optional[Any]			):


         """simple docstring"""



         __A             =		{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
         pl_module.logger.log_metrics(UpperCamelCase_			)




     @rank_zero_only
     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     pl.Trainer	,		UpperCamelCase_    :     pl.LightningModule	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     List[Any]=True			):


         """simple docstring"""



         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"			)
         __A             =		trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}			)
         # Log results
         __A             =		Path(pl_module.hparams.output_dir			)
         if type_path == "test":
             __A             =		od / """test_results.txt"""
             __A             =		od / """test_generations.txt"""
         else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             __A             =		od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             __A             =		od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
             results_file.parent.mkdir(exist_ok=UpperCamelCase_			)
             generations_file.parent.mkdir(exist_ok=UpperCamelCase_			)
         with open(UpperCamelCase_	,		"""a+"""			) as writer:
             for key in sorted(UpperCamelCase_			):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 __A             =		metrics[key]
                 if isinstance(UpperCamelCase_	,		torch.Tensor			):
                     __A             =		val.item()
                 __A             =		F"{key}: {val:.6f}\n"
                 writer.write(UpperCamelCase_			)

         if not save_generations:
             return

         if "preds" in metrics:
             __A             =		"""\n""".join(metrics["""preds"""]			)
             generations_file.open("""w+"""			).write(UpperCamelCase_			)




     @rank_zero_only
     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     Optional[int]	,		UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         try:
             __A             =		pl_module.model.model.num_parameters()
         except AttributeError:
             __A             =		pl_module.model.num_parameters()

         __A             =		count_trainable_parameters(UpperCamelCase_			)
         # mp stands for million parameters
         trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}			)




     @rank_zero_only
     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     pl.Trainer	,		UpperCamelCase_    :     pl.LightningModule			):


         """simple docstring"""



         save_json(pl_module.metrics	,		pl_module.metrics_save_path			)
         return self._write_logs(UpperCamelCase_	,		UpperCamelCase_	,		"""test"""			)





     @rank_zero_only
     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     pl.Trainer	,		UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         save_json(pl_module.metrics	,		pl_module.metrics_save_path			)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[Any]     ,			__lowercase				:     List[str]     ,			__lowercase				:     Optional[int]=1     ,			__lowercase				:     Optional[Any]=False      )  -> str:





    """simple docstring"""

    if metric == "rouge2":
        __A             =		"""{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        __A             =		"""{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "loss":
        __A             =		"""{val_avg_loss:.4f}-{step_count}"""
    else:
        raise NotImplementedError(
            f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
            """ this function."""      )

    __A             =		ModelCheckpoint(
        dirpath=__lowercase     ,			filename=__lowercase     ,			monitor=f"val_{metric}"     ,			mode="""min""" if """loss""" in metric else """max"""     ,			save_top_k=__lowercase     ,			)
    return checkpoint_callback






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Tuple     ,			__lowercase				:     Optional[Any]      )  -> Tuple:





    """simple docstring"""

    return EarlyStopping(
        monitor=f"val_{metric}"     ,			mode="""min""" if """loss""" in metric else """max"""     ,			patience=__lowercase     ,			verbose=__lowercase     ,			)












import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__a :    List[str]								=				get_logger(__name__)


if TYPE_CHECKING:
  from .builder import DatasetBuilder



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Dict     ,			__lowercase				:     Optional[Union[str, bool]] = None      )  -> List[str]:





    """simple docstring"""

    __A             =		importlib.import_module(__lowercase      )

    # TODO(QL): always update the module to add subsequent new authentication
    if hasattr(__lowercase     ,			"""_patched_for_streaming"""      ) and module._patched_for_streaming:
        return

    def wrap_auth(__lowercase				:     int      ):
        @wraps(__lowercase      )
        def wrapper(*__lowercase				:     Tuple     ,			**__lowercase				:     List[str]      ):
            return function(*__lowercase     ,			use_auth_token=__lowercase     ,			**__lowercase      )

        __A             =		"""wrap_auth"""
        return wrapper

    # open files in a streaming fashion
    patch_submodule(__lowercase     ,			"""open"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""os.listdir"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""os.walk"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""glob.glob"""     ,			wrap_auth(__lowercase      )      ).start()
    # allow to navigate in remote zip files
    patch_submodule(__lowercase     ,			"""os.path.join"""     ,			__lowercase      ).start()
    patch_submodule(__lowercase     ,			"""os.path.dirname"""     ,			__lowercase      ).start()
    patch_submodule(__lowercase     ,			"""os.path.basename"""     ,			__lowercase      ).start()
    patch_submodule(__lowercase     ,			"""os.path.relpath"""     ,			__lowercase      ).start()
    patch_submodule(__lowercase     ,			"""os.path.split"""     ,			__lowercase      ).start()
    patch_submodule(__lowercase     ,			"""os.path.splitext"""     ,			__lowercase      ).start()
    # allow checks on paths
    patch_submodule(__lowercase     ,			"""os.path.exists"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""os.path.isdir"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""os.path.isfile"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""os.path.getsize"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""pathlib.Path"""     ,			__lowercase      ).start()
    # file readers
    patch_submodule(__lowercase     ,			"""gzip.open"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""numpy.load"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""pandas.read_csv"""     ,			wrap_auth(__lowercase      )     ,			attrs=["""__version__"""]      ).start()
    patch_submodule(__lowercase     ,			"""pandas.read_excel"""     ,			wrap_auth(__lowercase      )     ,			attrs=["""__version__"""]      ).start()
    patch_submodule(__lowercase     ,			"""scipy.io.loadmat"""     ,			wrap_auth(__lowercase      )     ,			attrs=["""__version__"""]      ).start()
    patch_submodule(__lowercase     ,			"""xml.etree.ElementTree.parse"""     ,			wrap_auth(__lowercase      )      ).start()
    patch_submodule(__lowercase     ,			"""xml.dom.minidom.parse"""     ,			wrap_auth(__lowercase      )      ).start()
    __A             =		True






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     "DatasetBuilder"      )  -> Union[str, Any]:





    """simple docstring"""

    extend_module_for_streaming(builder.__module__     ,			use_auth_token=builder.use_auth_token      )
    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
    if not builder.__module__.startswith("""datasets."""      ):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__      )      ):
            if imports[0] == "internal":
                __A             =		imports[1]
                __A             =		""".""".join(builder.__module__.split("""."""      )[:-1] + [internal_import_name]      )
                extend_module_for_streaming(__lowercase     ,			use_auth_token=builder.use_auth_token      )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
    from .builder import DatasetBuilder

    __A             =		[
        cls.__module__
        for cls in type(__lowercase      ).__mro__[1:]  # make sure it's not the same module we've already patched
        if issubclass(cls     ,			__lowercase      ) and cls.__module__ != DatasetBuilder.__module__
    ]  # check it's not a standard builder from datasets.builder
    for module in parent_builder_modules:
        extend_module_for_streaming(__lowercase     ,			use_auth_token=builder.use_auth_token      )




