
import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

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


SCREAMING_SNAKE_CASE__				=							datasets.utils.logging.get_logger(__name__)
@dataclass
class       A__  (		datasets.BuilderConfig							):
							lowerCAmelCase__						:		Optional[datasets.Features]            =				None
							lowerCAmelCase__						:		str            =				"utf-8"
							lowerCAmelCase__						:		Optional[str]            =				None
							lowerCAmelCase__						:		int            =				10 << 20  # 10MB
							lowerCAmelCase__						:		bool            =				False
							lowerCAmelCase__						:		str            =				"line"






class       A__  (		datasets.ArrowBasedBuilder							):
							lowerCAmelCase__						:		Optional[int]            =				TextConfig





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



											"""simple docstring"""


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





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  str							)		->						List[str]:



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





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pa.Table							)		->						pa.Table:



											"""simple docstring"""


											if self.config.features is not None:
															__lowercase								=   self.config.features.arrow_schema
															if all(not require_storage_cast(_UpperCAmelCase							) for feature in self.config.features.values()							):
																			# cheaper cast
																			__lowercase								=   pa_table.cast(_UpperCAmelCase							)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			__lowercase								=   table_cast(_UpperCAmelCase	,					_UpperCAmelCase							)
															return pa_table
											else:
															return pa_table.cast(pa.schema({'text': pa.string()}							)							)





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]							)		->						List[Any]:



											"""simple docstring"""


											__lowercase								=   list(self.config.features							) if self.config.features is not None else ['text']
											for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase							)							):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(_UpperCAmelCase	,					encoding=self.config.encoding	,					errors=self.config.errors							) as f:
																			if self.config.sample_by == "line":
																							__lowercase								=   0
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not batch:
																															break
																											batch += f.readline()  # finish current line
																											# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																											__lowercase								=   StringIO(_UpperCAmelCase							).readlines()
																											if not self.config.keep_linebreaks:
																															__lowercase								=   [line.rstrip('\n'							) for line in batch]
																											__lowercase								=   pa.Table.from_arrays([pa.array(_UpperCAmelCase							)]	,					names=_UpperCAmelCase							)
																											# 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(_UpperCAmelCase							)
																											batch_idx += 1
																			elif self.config.sample_by == "paragraph":
																							__lowercase								=   0
																							__lowercase								=   ''
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not new_batch:
																															break
																											batch += new_batch
																											batch += f.readline()  # finish current line
																											__lowercase								=   batch.split('\n\n'							)
																											__lowercase								=   pa.Table.from_arrays(
																											    [pa.array([example for example in batch[:-1] if example]							)]	,					names=_UpperCAmelCase							)
																											# 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(_UpperCAmelCase							)
																											batch_idx += 1
																											__lowercase								=   batch[-1]
																							if batch:
																											__lowercase								=   pa.Table.from_arrays([pa.array([batch]							)]	,					names=_UpperCAmelCase							)
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																			elif self.config.sample_by == "document":
																							__lowercase								=   f.read()
																							__lowercase								=   pa.Table.from_arrays([pa.array([text]							)]	,					names=_UpperCAmelCase							)
																							yield file_idx, self._cast_table(_UpperCAmelCase							)







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  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {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      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


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

											if not save_generations:
															return

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





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


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

											__lowercase								=   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      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""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  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{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								=   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 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





