




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 			__magic_name__				( A		:					List[str]   ):






			'''simple docstring'''







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


__lowerCAmelCase		:				int       			=		logging.getLogger(__name__)




def 			__magic_name__				( A		:					List[Any],  A		:					Dict   ):






			'''simple docstring'''







			if metric == "rouge2":
						a    =			"{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						a    =			"{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						a    =			"{val_avg_em:.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."   )

			a    =			ModelCheckpoint(
			    dirpath=A,  filename=A,  monitor=F"""val_{metric}""",  mode="max",  save_top_k=3,  every_n_epochs=1,  )
			return checkpoint_callback




def 			__magic_name__				( A		:					str,  A		:					int   ):






			'''simple docstring'''







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






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



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

							if not save_generations:
										return

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





				@rank_zero_only
				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Tuple							)					->     Any:
							try:
										a    =			pl_module.model.model.num_parameters()
							except AttributeError:
										a    =			pl_module.model.num_parameters()

							a    =			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 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     pl.Trainer					,      __lowerCamelCase				:     pl.LightningModule							)					->     Optional[Any]:
							save_json(pl_module.metrics					,      pl_module.metrics_save_path							)
							return self._write_logs(__lowerCamelCase					,      __lowerCamelCase					,      "test"							)





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












# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch




class     snake_case__							(TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Any							)					->     Optional[Any]:
							super().__init__(features=__lowerCamelCase							)
							a    =			torch_tensor_kwargs
							import torch  # noqa import torch at initialization





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							import torch

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and column:
										if all(
										    isinstance(__lowerCamelCase					,      torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
										    for x in column							):
													return torch.stack(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							import torch

							if isinstance(__lowerCamelCase					,      (str, bytes, type(__lowerCamelCase							))							):
										return value
							elif isinstance(__lowerCamelCase					,      (np.character, np.ndarray)							) and np.issubdtype(value.dtype					,      np.character							):
										return value.tolist()

							a    =			{}

							if isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.integer							):
										a    =			{"dtype": torch.intaa}
							elif isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.floating							):
										a    =			{"dtype": torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(__lowerCamelCase					,      PIL.Image.Image							):
													a    =			np.asarray(__lowerCamelCase							)
							return torch.tensor(__lowerCamelCase					,      **{**default_dtype, **self.torch_tensor_kwargs}							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(__lowerCamelCase					,      "__array__"							) and not isinstance(__lowerCamelCase					,      torch.Tensor							):
										a    =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(__lowerCamelCase					,      np.ndarray							):
										if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							elif isinstance(__lowerCamelCase					,      (list, tuple)							):
										return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							return self._tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     dict							)					->     str:
							return map_nested(self._recursive_tensorize					,      __lowerCamelCase					,      map_list=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_row(__lowerCamelCase							)
							return self.recursive_tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     pa.Table							)					->     "torch.Tensor":
							a    =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_column(__lowerCamelCase					,      pa_table.column_names[0]							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							a    =			self._consolidate(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_batch(__lowerCamelCase							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							for column_name in batch:
										a    =			self._consolidate(batch[column_name]							)
							return batch






