
"""simple docstring"""


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = False							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							super().__init__(
							    UpperCAmelCase							,						split=UpperCAmelCase							,						features=UpperCAmelCase							,						cache_dir=UpperCAmelCase							,						keep_in_memory=UpperCAmelCase							,						streaming=UpperCAmelCase							,						num_proc=UpperCAmelCase							,						**UpperCAmelCase							,						)
							lowerCamelCase_					=  field
							lowerCamelCase_					=  path_or_paths if isinstance(UpperCAmelCase							,						UpperCAmelCase   ) else {self.split: path_or_paths}
							lowerCamelCase_					=  Json(
							    cache_dir=UpperCAmelCase							,						data_files=UpperCAmelCase							,						features=UpperCAmelCase							,						field=UpperCAmelCase							,						**UpperCAmelCase							,						)





				def        UpperCAmelCase__						(				self   ):
							# Build iterable dataset
							if self.streaming:
										lowerCamelCase_					=  self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
										lowerCamelCase_					=  None
										lowerCamelCase_					=  None
										lowerCamelCase_					=  None
										lowerCamelCase_					=  None

										self.builder.download_and_prepare(
										    download_config=UpperCAmelCase							,						download_mode=UpperCAmelCase							,						verification_mode=UpperCAmelCase							,						base_path=UpperCAmelCase							,						num_proc=self.num_proc							,						)
										lowerCamelCase_					=  self.builder.as_dataset(
										    split=self.split							,						verification_mode=UpperCAmelCase							,						in_memory=self.keep_in_memory   )
							return dataset




class  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							if num_proc is not None and num_proc <= 0:
										raise ValueError(f"num_proc {num_proc} must be an integer > 0."   )

							lowerCamelCase_					=  dataset
							lowerCamelCase_					=  path_or_buf
							lowerCamelCase_					=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							lowerCamelCase_					=  num_proc
							lowerCamelCase_					=  '''utf-8'''
							lowerCamelCase_					=  to_json_kwargs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.to_json_kwargs.pop('''path_or_buf'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''orient'''							,						'''records'''   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''lines'''							,						True if orient == '''records''' else False   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''index'''							,						False if orient in ['''split''', '''table'''] else True   )
							lowerCamelCase_					=  self.to_json_kwargs.pop('''compression'''							,						UpperCAmelCase   )

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
										raise NotImplementedError(f"`datasets` currently does not support {compression} compression"   )

							if isinstance(self.path_or_buf							,						(str, bytes, os.PathLike)   ):
										with fsspec.open(self.path_or_buf							,						'''wb'''							,						compression=UpperCAmelCase   ) as buffer:
													lowerCamelCase_					=  self._write(file_obj=UpperCAmelCase							,						orient=UpperCAmelCase							,						lines=UpperCAmelCase							,						index=UpperCAmelCase							,						**self.to_json_kwargs   )
							else:
										if compression:
													raise NotImplementedError(
													    f"The compression parameter is not supported when writing to a buffer, but compression={compression}"
													    ''' was passed. Please provide a local path instead.'''   )
										lowerCamelCase_					=  self._write(
										    file_obj=self.path_or_buf							,						orient=UpperCAmelCase							,						lines=UpperCAmelCase							,						index=UpperCAmelCase							,						**self.to_json_kwargs   )
							return written




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  args

							lowerCamelCase_					=  query_table(
							    table=self.dataset.data							,						key=slice(UpperCAmelCase							,						offset + self.batch_size   )							,						indices=self.dataset._indices							,						)
							lowerCamelCase_					=  batch.to_pandas().to_json(
							    path_or_buf=UpperCAmelCase							,						orient=UpperCAmelCase							,						lines=UpperCAmelCase							,						index=UpperCAmelCase							,						**UpperCAmelCase   )
							if not json_str.endswith('''\n'''   ):
										json_str += "\n"
							return json_str.encode(self.encoding   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  0

							if self.num_proc is None or self.num_proc == 1:
										for offset in logging.tqdm(
										    range(0							,						len(self.dataset   )							,						self.batch_size   )							,						unit='''ba'''							,						disable=not logging.is_progress_bar_enabled()							,						desc='''Creating json from Arrow format'''							,						):
													lowerCamelCase_					=  self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
													written += file_obj.write(UpperCAmelCase   )
							else:
										lowerCamelCase_		, lowerCamelCase_					=  len(self.dataset   ), self.batch_size
										with multiprocessing.Pool(self.num_proc   ) as pool:
													for json_str in logging.tqdm(
													    pool.imap(
													        self._batch_json							,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,						UpperCAmelCase							,						UpperCAmelCase   )]							,						)							,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,						unit='''ba'''							,						disable=not logging.is_progress_bar_enabled()							,						desc='''Creating json from Arrow format'''							,						):
																written += file_obj.write(UpperCAmelCase   )

							return written




'''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       (		UpperCamelCase__					: Tuple							):


	"""simple docstring"""
	__UpperCAmelCase      					=      filter(lambda UpperCamelCase__							: p.requires_grad     ,       model.parameters()							)
	__UpperCAmelCase      					=      sum([np.prod(p.size()							) for p in model_parameters]							)
	return params


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





def    lowerCAmelCase       (		UpperCamelCase__					: Any     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	if metric == "rouge2":
		__UpperCAmelCase      					=      '''{val_avg_rouge2:.4f}-{step_count}'''
	elif metric == "bleu":
		__UpperCAmelCase      					=      '''{val_avg_bleu:.4f}-{step_count}'''
	elif metric == "em":
		__UpperCAmelCase      					=      '''{val_avg_em:.4f}-{step_count}'''
	elif metric == "loss":
		__UpperCAmelCase      					=      '''{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      					=      ModelCheckpoint(
	    dirpath=UpperCamelCase__     ,       filename=UpperCamelCase__     ,       monitor=f"""val_{metric}"""     ,       mode='''max'''     ,       save_top_k=1     ,       every_n_epochs=1     ,       )

	return checkpoint_callback





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Optional[Any]							):


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






class     A ( pl.Callback ):





							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				int  ,		__a					:				Any						)				->						Optional[Any]:
								__UpperCAmelCase      					=      {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    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule  ,		__a					:				str  ,		__a					:				Any=True						)				->						None:
								logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
								__UpperCAmelCase      					=      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      					=      Path(pl_module.hparams.output_dir						)
								if type_path == "test":
									__UpperCAmelCase      					=      od / '''test_results.txt'''
									__UpperCAmelCase      					=      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      					=      od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
									__UpperCAmelCase      					=      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
										__UpperCAmelCase      					=      metrics[key]
										if isinstance(__a  ,		torch.Tensor						):
											__UpperCAmelCase      					=      val.item()
										__UpperCAmelCase      					=      f"""{key}: {val:.6f}\n"""
										writer.write(__a						)

								if not save_generations:
									return

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





							@rank_zero_only
							def    snake_case__    (   self					:				List[Any]  ,		__a					:				Optional[int]  ,		__a					:				Union[str, Any]						)				->						List[Any]:
								try:
									__UpperCAmelCase      					=      pl_module.model.model.num_parameters()
								except AttributeError:
									__UpperCAmelCase      					=      pl_module.model.num_parameters()

								__UpperCAmelCase      					=      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    snake_case__    (   self					:				List[str]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule						)				->						Optional[int]:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								return self._write_logs(__a  ,		__a  ,		'''test'''						)






							@rank_zero_only
							def    snake_case__    (   self					:				Any  ,		__a					:				pl.Trainer  ,		__a					:				Union[str, Any]						)				->						Tuple:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
