
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 						UpperCAmelCase      (     a_						)		->     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


SCREAMING_SNAKE_CASE							:str										=    logging.getLogger(__name__)


def 						UpperCAmelCase      (     a_		,       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}"
				elif metric == "loss":
								__A             =			"{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."						)

				__A             =			ModelCheckpoint(
				    dirpath=a_		,       filename=a_		,       monitor=F'''val_{metric}'''		,       mode="max"		,       save_top_k=1		,       every_n_epochs=1		,       )

				return checkpoint_callback


def 						UpperCAmelCase      (     a_		,       a_						)		->     int:







				"""simple docstring"""



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





class 							UpperCAmelCase				(  pl.Callback  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			List[Any]							,A :			Union[str, Any]							,A :			List[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(A			)






	@rank_zero_only
	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			pl.Trainer							,A :			pl.LightningModule							,A :			str							,A :			str=True			):
					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=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
													__A             =			metrics[key]
													if isinstance(A							,torch.Tensor			):
																	__A             =			val.item()
													__A             =			f'''{key}: {val:.6f}\n'''
													writer.write(A			)

					if not save_generations:
									return

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






	@rank_zero_only
	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Optional[int]							,A :			Optional[Any]			):
					try:
									__A             =			pl_module.model.model.num_parameters()
					except AttributeError:
									__A             =			pl_module.model.num_parameters()

					__A             =			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     UpperCamelCase_  (	self :			Tuple							,A :			pl.Trainer							,A :			pl.LightningModule			):
					save_json(pl_module.metrics							,pl_module.metrics_save_path			)
					return self._write_logs(A							,A							,"test"			)







	@rank_zero_only
	def     UpperCamelCase_  (	self :			Tuple							,A :			pl.Trainer							,A :			Dict			):
					save_json(pl_module.metrics							,pl_module.metrics_save_path			)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
