'''simple docstring'''




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
 import sqlitea

 import sqlalchemy


_A										= datasets.utils.logging.get_logger(__name__)


@dataclass
class    UpperCAmelCase__	(							datasets.BuilderConfig      ):






   """simple docstring"""
   A				:							Union[str, "sqlalchemy.sql.Selectable"]			     =     None
   A				:							Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			     =     None
   A				:							Optional[Union[str, List[str]]]			     =     None
   A				:							bool			     =     True
   A				:							Optional[Union[List, Tuple, Dict]]			     =     None
   A				:							Optional[Union[List, Dict]]			     =     None
   A				:							Optional[List[str]]			     =     None
   A				:							Optional[int]			     =     1_00_00
   A				:							Optional[datasets.Features]			     =     None
   def 		_lowerCamelCase			(self					)     ->  Dict:
        if self.sql is None:
             raise ValueError('sql must be specified'					)
        if self.con is None:
             raise ValueError('con must be specified'					)
   def 		_lowerCamelCase			(self							,					_a							,					_a = None							,					)     ->  str:
        lowercase_       :							Optional[int]  					=       config_kwargs.copy()
        # We need to stringify the Selectable object to make its hash deterministic

        # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
        lowercase_       :							List[Any]  					=       config_kwargs['sql']
        if not isinstance(_a							,					_a					):
             if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                  import sqlalchemy

                  if isinstance(_a							,					sqlalchemy.sql.Selectable					):
                       lowercase_       :							int  					=       sqlalchemy.create_engine(config_kwargs['con'].split('://'					)[0] + '://'					)
                       lowercase_       :							str  					=       str(sql.compile(dialect=engine.dialect					)					)
                       lowercase_       :							str  					=       sql_str
                  else:
                       raise TypeError(
                           f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(_a					)}: {sql}'''					)
             else:
                  raise TypeError(
                      f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(_a					)}: {sql}'''					)
        lowercase_       :							List[Any]  					=       config_kwargs['con']
        if not isinstance(_a							,					_a					):
             lowercase_       :							Any  					=       id(_a					)
             logger.info(
                 f'''SQL connection \'con\' of type {type(_a					)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''					)

        return super().create_config_id(_a							,					custom_features=_a					)





   @property
   def 		_lowerCamelCase			(self					)     ->  str:
        lowercase_       :							str  					=       {
            'index_col': self.index_col,
            'columns': self.columns,
            'params': self.params,
            'coerce_float': self.coerce_float,
            'parse_dates': self.parse_dates,
        }
        return pd_read_sql_kwargs



class    UpperCAmelCase__	(							datasets.ArrowBasedBuilder      ):






   """simple docstring"""
   A				:							Tuple			     =     SqlConfig
   def 		_lowerCamelCase			(self					)     ->  str:
        return datasets.DatasetInfo(features=self.config.features					)
   def 		_lowerCamelCase			(self							,					_a					)     ->  List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,					gen_kwargs={}					)]
   def 		_lowerCamelCase			(self							,					_a					)     ->  pa.Table:
        if self.config.features is not None:
             lowercase_       :							Dict  					=       self.config.features.arrow_schema
             if all(not require_storage_cast(_a					) for feature in self.config.features.values()					):
                  # cheaper cast
                  lowercase_       :							Optional[int]  					=       pa.Table.from_arrays([pa_table[field.name] for field in schema]							,					schema=_a					)
             else:
                  # more expensive cast; allows str <-> int/float or str to Audio for example
                  lowercase_       :							int  					=       table_cast(_a							,					_a					)
        return pa_table





   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							List[str]  					=       self.config.chunksize
        lowercase_       :							Dict  					=       pd.read_sql(
            self.config.sql							,					self.config.con							,					chunksize=_a							,					**self.config.pd_read_sql_kwargs					)
        lowercase_       :							int  					=       [sql_reader] if chunksize is None else sql_reader
        for chunk_idx, df in enumerate(_a					):
             lowercase_       :							Union[str, Any]  					=       pa.Table.from_pandas(_a					)
             yield chunk_idx, self._cast_table(_a					)





'''simple docstring'''




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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       filter(lambda SCREAMING_SNAKE_CASE_				: p.requires_grad	,							model.parameters()				)
     lowercase_       :							Tuple  					=       sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	(							pl.Callback      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  str:
        lowercase_       :							List[str]  					=       {f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
        pl_module.logger.log_metrics(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a=True					)     ->  None:
        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
        lowercase_       :							Union[str, Any]  					=       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
        lowercase_       :							Optional[int]  					=       Path(pl_module.hparams.output_dir					)
        if type_path == "test":
             lowercase_       :							Union[str, Any]  					=       od / 'test_results.txt'
             lowercase_       :							Tuple  					=       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.
             lowercase_       :							List[str]  					=       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
             lowercase_       :							int  					=       od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
             results_file.parent.mkdir(exist_ok=_a					)
             generations_file.parent.mkdir(exist_ok=_a					)
        with open(_a							,					'a+'					) as writer:
             for key in sorted(_a					):
                  if key in ["log", "progress_bar", "preds"]:
                       continue
                  lowercase_       :							Any  					=       metrics[key]
                  if isinstance(_a							,					torch.Tensor					):
                       lowercase_       :							Any  					=       val.item()
                  lowercase_       :							List[str]  					=       f'''{key}: {val:.6f}\n'''
                  writer.write(_a					)

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_       :							Optional[Any]  					=       '\n'.join(metrics['preds']					)
             generations_file.open('w+'					).write(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Tuple:
        try:
             lowercase_       :							List[Any]  					=       pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_       :							Dict  					=       pl_module.model.num_parameters()

        lowercase_       :							Optional[Any]  					=       count_trainable_parameters(_a					)
        # 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							,					_a							,					_a					)     ->  int:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        return self._write_logs(_a							,					_a							,					'test'					)





   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  List[str]:
        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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=1	,							SCREAMING_SNAKE_CASE_=False				):
     if metric == "rouge2":
          lowercase_       :							Optional[int]  					=       '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_       :							int  					=       '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "loss":
          lowercase_       :							List[Any]  					=       '{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.'				)

     lowercase_       :							Tuple  					=       ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE_	,							filename=SCREAMING_SNAKE_CASE_	,							monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							save_top_k=SCREAMING_SNAKE_CASE_	,							)
     return checkpoint_callback






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return EarlyStopping(
         monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							patience=SCREAMING_SNAKE_CASE_	,							verbose=SCREAMING_SNAKE_CASE_	,							)




