




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  (      snake_case_						):

						'''simple docstring'''


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


lowercase_     :    Tuple      =			logging.getLogger(__name__)






class    __lowerCAmelCase			(	pl.Callback			):







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      Dict   ,   snake_case__   :      str				):




									"""simple docstring"""


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







			@rank_zero_only
			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      pl.Trainer   ,   snake_case__   :      pl.LightningModule   ,   snake_case__   :      str   ,   snake_case__   :      Dict=True				):




									"""simple docstring"""


									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=snake_case__				)
															generations_file.parent.mkdir(exist_ok=snake_case__				)
									with open(snake_case__   ,   "a+"				) as writer:
															for key in sorted(snake_case__				):
																					if key in ["log", "progress_bar", "preds"]:
																											continue
																					_UpperCAmelCase													=					metrics[key]
																					if isinstance(snake_case__   ,   torch.Tensor				):
																											_UpperCAmelCase													=					val.item()
																					_UpperCAmelCase													=					F"""{key}: {val:.6f}\n"""
																					writer.write(snake_case__				)

									if not save_generations:
															return

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







			@rank_zero_only
			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Any   ,   snake_case__   :      Union[str, Any]				):




									"""simple docstring"""


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

									_UpperCAmelCase													=					count_trainable_parameters(snake_case__				)
									# 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 UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      pl.Trainer   ,   snake_case__   :      pl.LightningModule				):




									"""simple docstring"""


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







			@rank_zero_only
			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      pl.Trainer   ,   snake_case__   :      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")




def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_=1     ,   snake_case_=False						):

						'''simple docstring'''


						if metric == "rouge2":
												_UpperCAmelCase													=					"{val_avg_rouge2:.4f}-{step_count}"
						elif metric == "bleu":
												_UpperCAmelCase													=					"{val_avg_bleu:.4f}-{step_count}"
						elif metric == "loss":
												_UpperCAmelCase													=					"{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."						)

						_UpperCAmelCase													=					ModelCheckpoint(
						    dirpath=snake_case_     ,   filename=snake_case_     ,   monitor=f"""val_{metric}"""     ,   mode="min" if "loss" in metric else "max"     ,   save_top_k=snake_case_     ,   )
						return checkpoint_callback





def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


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










import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase_     :    Union[str, Any]      =			datasets.utils.logging.get_logger(__name__)






@dataclass
class    __lowerCAmelCase			(	datasets.BuilderConfig			):
			snake_case_				:			Optional[datasets.Features]								=					None






class    __lowerCAmelCase			(	datasets.ArrowBasedBuilder			):
			snake_case_				:			str								=					ArrowConfig







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


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







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Optional[int]				):




									"""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(snake_case__   ,   (str, list, tuple)				):
															_UpperCAmelCase													=					data_files
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) 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(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																					for file in itertools.chain.from_iterable(snake_case__				):
																											with open(snake_case__   ,   "rb"				) as f:
																																	_UpperCAmelCase													=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(snake_case__				).schema				)
																											break
															splits.append(datasets.SplitGenerator(name=snake_case__   ,   gen_kwargs={"files": files}				)				)
									return splits







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      pa.Table				):




									"""simple docstring"""


									if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															_UpperCAmelCase													=					table_cast(snake_case__   ,   self.info.features.arrow_schema				)
									return pa_table







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
															with open(snake_case__   ,   "rb"				) as f:
																					try:
																											for batch_idx, record_batch in enumerate(pa.ipc.open_stream(snake_case__				)				):
																																	_UpperCAmelCase													=					pa.Table.from_batches([record_batch]				)
																																	# 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 F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__				)
																					except ValueError as e:
																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case__				)}: {e}"""				)
																											raise




