'''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				)    ->   Dict:
							lowercase_      :    Optional[int]       		= filter(lambda UpperCAmelCase__				: p.requires_grad   ,   model.parameters()				)
							lowercase_      :    Union[str, Any]       		= sum([np.prod(p.size()				) for p in model_parameters]				)
							return params


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




def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      str				)    ->   Dict:
							if metric == "rouge2":
														lowercase_      :    Tuple       		= """{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														lowercase_      :    Any       		= """{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "em":
														lowercase_      :    Dict       		= """{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."""				)

							lowercase_      :    List[Any]       		= ModelCheckpoint(
							    dirpath=UpperCAmelCase__   ,   filename=UpperCAmelCase__   ,   monitor=F'''val_{metric}'''   ,   mode="""max"""   ,   save_top_k=3   ,   every_n_epochs=1   ,   )
							return checkpoint_callback




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Dict:
							return EarlyStopping(
							    monitor=F'''val_{metric}'''   ,   mode="""min""" if """loss""" in metric else """max"""   ,   patience=UpperCAmelCase__   ,   verbose=UpperCAmelCase__   ,   )





class 					__magic_name__		(					pl.Callback):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Dict					):
										lowercase_      :    List[Any]       		= {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
										pl_module.logger.log_metrics(lowercase_					)







			@rank_zero_only
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						pl.Trainer       ,    lowercase_       :						pl.LightningModule       ,    lowercase_       :						str       ,    lowercase_       :						Tuple=True					):
										logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
										lowercase_      :    Dict       		= 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_      :    Optional[Any]       		= Path(pl_module.hparams.output_dir					)
										if type_path == "test":
																	lowercase_      :    List[str]       		= od / """test_results.txt"""
																	lowercase_      :    Union[str, Any]       		= 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_      :    Tuple       		= od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
																	lowercase_      :    Union[str, Any]       		= od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
																	results_file.parent.mkdir(exist_ok=lowercase_					)
																	generations_file.parent.mkdir(exist_ok=lowercase_					)
										with open(lowercase_       ,    """a+"""					) as writer:
																	for key in sorted(lowercase_					):
																								if key in ["log", "progress_bar", "preds"]:
																															continue
																								lowercase_      :    Any       		= metrics[key]
																								if isinstance(lowercase_       ,    torch.Tensor					):
																															lowercase_      :    Optional[Any]       		= val.item()
																								lowercase_      :    Optional[int]       		= f'''{key}: {val:.6f}\n'''
																								writer.write(lowercase_					)

										if not save_generations:
																	return

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







			@rank_zero_only
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Tuple					):
										try:
																	lowercase_      :    Optional[Any]       		= pl_module.model.model.num_parameters()
										except AttributeError:
																	lowercase_      :    Optional[Any]       		= pl_module.model.num_parameters()

										lowercase_      :    Dict       		= count_trainable_parameters(lowercase_					)
										# 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 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						pl.Trainer       ,    lowercase_       :						pl.LightningModule					):
										save_json(pl_module.metrics       ,    pl_module.metrics_save_path					)
										return self._write_logs(lowercase_       ,    lowercase_       ,    """test"""					)







			@rank_zero_only
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						pl.Trainer       ,    lowercase_       :						Optional[int]					):
										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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=_UpperCAmelCase)
class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = field(default='''image-classification''',  metadata={'''include_in_asdict_even_if_is_default''': True})
			UpperCamelCase__       = Features({'''image''': Image()})
			UpperCamelCase__       = Features({'''labels''': ClassLabel})
			UpperCamelCase__       = "image"
			UpperCamelCase__       = "labels"







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						str					):
										if self.label_column not in features:
																	raise ValueError(f'''Column {self.label_column} is not present in features.'''					)
										if not isinstance(features[self.label_column]       ,    lowercase_					):
																	raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''					)
										lowercase_      :    List[str]       		= copy.deepcopy(self					)
										lowercase_      :    List[str]       		= self.label_schema.copy()
										lowercase_      :    List[Any]       		= features[self.label_column]
										lowercase_      :    Optional[Any]       		= label_schema
										return task_template







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}





