
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, 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
from datasets.utils.py_utils import Literal


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

_A		     =    ['''names''', '''prefix''']
_A		     =    ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
_A		     =    ['''encoding_errors''', '''on_bad_lines''']
_A		     =    ['''date_format''']

@dataclass
class 						lowercase_				(				datasets.BuilderConfig ):
 A__  : str      					=			","
 A__  : Optional[str]      					=			None
 A__  : Optional[Union[int, List[int], str]]      					=			"infer"
 A__  : Optional[List[str]]      					=			None
 A__  : Optional[List[str]]      					=			None
 A__  : Optional[Union[int, str, List[int], List[str]]]      					=			None
 A__  : Optional[Union[List[int], List[str]]]      					=			None
 A__  : Optional[str]      					=			None
 A__  : bool      					=			True
 A__  : Optional[Literal["c", "python", "pyarrow"]]      					=			None
 A__  : Dict[Union[int, str], Callable[[Any], Any]]      					=			None
 A__  : Optional[list]      					=			None
 A__  : Optional[list]      					=			None
 A__  : bool      					=			False
 A__  : Optional[Union[int, List[int]]]      					=			None
 A__  : Optional[int]      					=			None
 A__  : Optional[Union[str, List[str]]]      					=			None
 A__  : bool      					=			True
 A__  : bool      					=			True
 A__  : bool      					=			False
 A__  : bool      					=			True
 A__  : Optional[str]      					=			None
 A__  : str      					=			"."
 A__  : Optional[str]      					=			None
 A__  : str      					=			'"'
 A__  : int      					=			0
 A__  : Optional[str]      					=			None
 A__  : Optional[str]      					=			None
 A__  : Optional[str]      					=			None
 A__  : Optional[str]      					=			None
 A__  : bool      					=			True
 A__  : bool      					=			True
 A__  : int      					=			0
 A__  : bool      					=			True
 A__  : bool      					=			False
 A__  : Optional[str]      					=			None
 A__  : int      					=			1_0000
 A__  : Optional[datasets.Features]      					=			None
 A__  : Optional[str]      					=			"strict"
 A__  : Literal["error", "warn", "skip"]      					=			"error"
 A__  : Optional[str]      					=			None

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        if self.delimiter is not None:
               UpperCamelCase_											=				self.delimiter
        if self.column_names is not None:
               UpperCamelCase_											=				self.column_names

 @property
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				{
            "sep": self.sep,
            "header": self.header,
            "names": self.names,
            "index_col": self.index_col,
            "usecols": self.usecols,
            "prefix": self.prefix,
            "mangle_dupe_cols": self.mangle_dupe_cols,
            "engine": self.engine,
            "converters": self.converters,
            "true_values": self.true_values,
            "false_values": self.false_values,
            "skipinitialspace": self.skipinitialspace,
            "skiprows": self.skiprows,
            "nrows": self.nrows,
            "na_values": self.na_values,
            "keep_default_na": self.keep_default_na,
            "na_filter": self.na_filter,
            "verbose": self.verbose,
            "skip_blank_lines": self.skip_blank_lines,
            "thousands": self.thousands,
            "decimal": self.decimal,
            "lineterminator": self.lineterminator,
            "quotechar": self.quotechar,
            "quoting": self.quoting,
            "escapechar": self.escapechar,
            "comment": self.comment,
            "encoding": self.encoding,
            "dialect": self.dialect,
            "error_bad_lines": self.error_bad_lines,
            "warn_bad_lines": self.warn_bad_lines,
            "skipfooter": self.skipfooter,
            "doublequote": self.doublequote,
            "memory_map": self.memory_map,
            "float_precision": self.float_precision,
            "chunksize": self.chunksize,
            "encoding_errors": self.encoding_errors,
            "on_bad_lines": self.on_bad_lines,
            "date_format": self.date_format,
        }

        # some kwargs must not be passed if they don't have a default value
        # some others are deprecated and we can also not pass them if they are the default value
        for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
               if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()   ,		lowerCamelCase__   ):
                      del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
        if not (datasets.config.PANDAS_VERSION.major >= 2):
               for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                      del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
        if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
               for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                      del pd_read_csv_kwargs[pd_read_csv_parameter]

        return pd_read_csv_kwargs



class 						lowercase_				(				datasets.ArrowBasedBuilder ):
 A__  : Tuple      					=			CsvConfig

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return datasets.DatasetInfo(features=self.config.features   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        if not self.config.data_files:
               raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''   )
        UpperCamelCase_											=				dl_manager.download_and_extract(self.config.data_files   )
        if isinstance(lowerCamelCase__   ,		(str, list, tuple)   ):
               UpperCamelCase_											=				data_files
               if isinstance(lowerCamelCase__   ,		lowerCamelCase__   ):
                      UpperCamelCase_											=				[files]
               UpperCamelCase_											=				[dl_manager.iter_files(lowerCamelCase__   ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,		gen_kwargs={"""files""": files}   )]
        UpperCamelCase_											=				[]
        for split_name, files in data_files.items():
               if isinstance(lowerCamelCase__   ,		lowerCamelCase__   ):
                      UpperCamelCase_											=				[files]
               UpperCamelCase_											=				[dl_manager.iter_files(lowerCamelCase__   ) for file in files]
               splits.append(datasets.SplitGenerator(name=lowerCamelCase__   ,		gen_kwargs={"""files""": files}   )   )
        return splits

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        if self.config.features is not None:
               UpperCamelCase_											=				self.config.features.arrow_schema
               if all(not require_storage_cast(lowerCamelCase__   ) for feature in self.config.features.values()   ):
                      # cheaper cast
                      UpperCamelCase_											=				pa.Table.from_arrays([pa_table[field.name] for field in schema]   ,		schema=lowerCamelCase__   )
               else:
                      # more expensive cast; allows str <-> int/float or str to Audio for example
                      UpperCamelCase_											=				table_cast(lowerCamelCase__   ,		lowerCamelCase__   )
        return pa_table

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.config.features.arrow_schema if self.config.features else None
        # dtype allows reading an int column as str
        UpperCamelCase_											=				(
            {
                name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCamelCase__   ) else object
                for name, dtype, feature in zip(schema.names   ,		schema.types   ,		self.config.features.values()   )
            }
            if schema is not None
            else None
        )
        for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__   )   ):
               UpperCamelCase_											=				pd.read_csv(lowerCamelCase__   ,		iterator=lowerCamelCase__   ,		dtype=lowerCamelCase__   ,		**self.config.pd_read_csv_kwargs   )
               try:
                      for batch_idx, df in enumerate(lowerCamelCase__   ):
                             UpperCamelCase_											=				pa.Table.from_pandas(lowerCamelCase__   )
                             # Uncomment for debugging (will print the Arrow table size and elements)
                             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                             yield (file_idx, batch_idx), self._cast_table(lowerCamelCase__   )
               except ValueError as e:
                      logger.error(f'''Failed to read file \'{file}\' with error {type(lowerCamelCase__   )}: {e}'''   )
                      raise







'''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_rag import save_json






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					filter(lambda __lowerCAmelCase      : p.requires_grad					,       model.parameters()      )
		_UpperCAmelCase						:				Optional[Any]	    =					sum([np.prod(p.size()      ) for p in model_parameters]      )
		return params


lowerCamelCase__					=   logging.getLogger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		if metric == "rouge2":
				_UpperCAmelCase						:				List[str]	    =					"{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase						:				int	    =					"{val_avg_bleu:.4f}-{step_count}"
		elif metric == "em":
				_UpperCAmelCase						:				str	    =					"{val_avg_em:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase						:				List[Any]	    =					"{val_avg_loss:.4f}-{step_count}"
		else:
				raise NotImplementedError(
				    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
				    " function."      )

		_UpperCAmelCase						:				Any	    =					ModelCheckpoint(
		    dirpath=__lowerCAmelCase					,       filename=__lowerCAmelCase					,       monitor=F"""val_{metric}"""					,       mode="max"					,       save_top_k=1					,       every_n_epochs=1					,       )

		return checkpoint_callback






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""					,       mode="min" if "loss" in metric else "max"					,       patience=__lowerCAmelCase					,       verbose=__lowerCAmelCase					,       )






class 		lowerCAmelCase__	(	pl.Callback		):




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->int:



					'''simple docstring'''







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




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[str, Any]=True       )      ->None:



					'''simple docstring'''







					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""       )
					_UpperCAmelCase						:				str	    =					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
					_UpperCAmelCase						:				Any	    =					Path(pl_module.hparams.output_dir       )
					if type_path == "test":
							_UpperCAmelCase						:				List[Any]	    =					od / "test_results.txt"
							_UpperCAmelCase						:				Union[str, Any]	    =					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.
							_UpperCAmelCase						:				Optional[Any]	    =					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase						:				Dict	    =					od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
							results_file.parent.mkdir(exist_ok=lowerCamelCase__       )
							generations_file.parent.mkdir(exist_ok=lowerCamelCase__       )
					with open(lowerCamelCase__		,     "a+"       ) as writer:
							for key in sorted(lowerCamelCase__       ):
									if key in ["log", "progress_bar", "preds"]:
											continue
									_UpperCAmelCase						:				Dict	    =					metrics[key]
									if isinstance(lowerCamelCase__		,     torch.Tensor       ):
											_UpperCAmelCase						:				Tuple	    =					val.item()
									_UpperCAmelCase						:				str	    =					F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__       )

					if not save_generations:
							return

					if "preds" in metrics:
							_UpperCAmelCase						:				Optional[int]	    =					"\n".join(metrics["preds"]       )
							generations_file.open("w+"       ).write(lowerCamelCase__       )




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







					try:
							_UpperCAmelCase						:				Tuple	    =					pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase						:				int	    =					pl_module.model.num_parameters()

					_UpperCAmelCase						:				int	    =					count_trainable_parameters(lowerCamelCase__       )
					# 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     :   Dict		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule       )      ->int:



					'''simple docstring'''







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






			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   Union[str, Any]       )      ->Union[str, 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")



