






'''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 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       filter(lambda __magic_name__						: p.requires_grad     ,model.parameters()						)
			snake_case_				:					List[str]   =       sum([np.prod(p.size()						) for p in model_parameters]						)
			return params


__lowerCamelCase   :					Optional[Any]   							=					logging.getLogger(__name__)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			if metric == "rouge2":
						snake_case_				:					List[str]   =       "{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						snake_case_				:					str   =       "{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						snake_case_				:					str   =       "{val_avg_em:.4f}-{step_count}"
			elif metric == "loss":
						snake_case_				:					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."						)

			snake_case_				:					Optional[Any]   =       ModelCheckpoint(
			    dirpath=__magic_name__     ,filename=__magic_name__     ,monitor=F'''val_{metric}'''     ,mode="max"     ,save_top_k=1     ,every_n_epochs=1     ,)

			return checkpoint_callback

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



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







class    A_	(pl.Callback							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Any				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       {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 							_A	(		self			:int       ,							lowerCAmelCase__			:pl.Trainer       ,							lowerCAmelCase__			:pl.LightningModule       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Dict=True				)				->  None:


										'''simple docstring'''




										logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''				)
										snake_case_				:					List[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
										snake_case_				:					Tuple   =       Path(pl_module.hparams.output_dir				)
										if type_path == "test":
													snake_case_				:					str   =       od / "test_results.txt"
													snake_case_				:					List[str]   =       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.
													snake_case_				:					Dict   =       od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
													snake_case_				:					Optional[int]   =       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
																snake_case_				:					Dict   =       metrics[key]
																if isinstance(lowerCAmelCase__       ,							torch.Tensor				):
																			snake_case_				:					str   =       val.item()
																snake_case_				:					Optional[int]   =       F'''{key}: {val:.6f}\n'''
																writer.write(lowerCAmelCase__				)

										if not save_generations:
													return

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

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


										'''simple docstring'''




										try:
													snake_case_				:					Dict   =       pl_module.model.model.num_parameters()
										except AttributeError:
													snake_case_				:					Dict   =       pl_module.model.num_parameters()

										snake_case_				:					Optional[Any]   =       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 							_A	(		self			:Any       ,							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 							_A	(		self			:Dict       ,							lowerCAmelCase__			:pl.Trainer       ,							lowerCAmelCase__			:Any				)				->  Tuple:


										'''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")












'''simple docstring'''




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


__lowerCamelCase   :					Optional[Any]   							=					datasets.utils.logging.get_logger(__name__)

__lowerCamelCase   :					List[str]   							=					['''names''', '''prefix''']
__lowerCamelCase   :					int   							=					['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__lowerCamelCase   :					str   							=					['''encoding_errors''', '''on_bad_lines''']
__lowerCamelCase   :					Optional[Any]   							=					['''date_format''']







@dataclass
class    A_	(datasets.BuilderConfig							):
							"""simple docstring"""




							a__   		=	","
							a__   		=	None
							a__   		=	"infer"
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	True
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	False
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	True
							a__   		=	True
							a__   		=	False
							a__   		=	True
							a__   		=	None
							a__   		=	"."
							a__   		=	None
							a__   		=	'"'
							a__   		=	0
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	True
							a__   		=	True
							a__   		=	0
							a__   		=	True
							a__   		=	False
							a__   		=	None
							a__   		=	10000
							a__   		=	None
							a__   		=	"strict"
							a__   		=	"error"
							a__   		=	None

							def 							_A	(		self			:List[str]				)				->  Any:


										'''simple docstring'''




										if self.delimiter is not None:
													snake_case_				:					Tuple   =       self.delimiter
										if self.column_names is not None:
													snake_case_				:					List[Any]   =       self.column_names






							@property
							def 							_A	(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       {
										    "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    A_	(datasets.ArrowBasedBuilder							):
							"""simple docstring"""




							a__   		=	CsvConfig

							def 							_A	(		self			:Optional[Any]				)				->  Optional[Any]:


										'''simple docstring'''




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

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Dict				)				->  List[Any]:


										'''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}'''				)
										snake_case_				:					Optional[Any]   =       dl_manager.download_and_extract(self.config.data_files				)
										if isinstance(lowerCAmelCase__       ,							(str, list, tuple)				):
													snake_case_				:					int   =       data_files
													if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
																snake_case_				:					List[str]   =       [files]
													snake_case_				:					Tuple   =       [dl_manager.iter_files(lowerCAmelCase__				) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,							gen_kwargs={"files": files}				)]
										snake_case_				:					str   =       []
										for split_name, files in data_files.items():
													if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
																snake_case_				:					str   =       [files]
													snake_case_				:					Any   =       [dl_manager.iter_files(lowerCAmelCase__				) for file in files]
													splits.append(datasets.SplitGenerator(name=lowerCAmelCase__       ,							gen_kwargs={"files": files}				)				)
										return splits

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:pa.Table				)				->  pa.Table:


										'''simple docstring'''




										if self.config.features is not None:
													snake_case_				:					int   =       self.config.features.arrow_schema
													if all(not require_storage_cast(lowerCAmelCase__				) for feature in self.config.features.values()				):
																# cheaper cast
																snake_case_				:					Optional[Any]   =       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
																snake_case_				:					Dict   =       table_cast(lowerCAmelCase__       ,							lowerCAmelCase__				)
										return pa_table






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


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.config.features.arrow_schema if self.config.features else None
										# dtype allows reading an int column as str
										snake_case_				:					str   =       (
										    {
										        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__				)				):
													snake_case_				:					Tuple   =       pd.read_csv(lowerCAmelCase__       ,							iterator=lowerCAmelCase__       ,							dtype=lowerCAmelCase__       ,							**self.config.pd_read_csv_kwargs				)
													try:
																for batch_idx, df in enumerate(lowerCAmelCase__				):
																			snake_case_				:					Optional[int]   =       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




