





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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Any      =		filter(lambda _lowercase			: p.requires_grad   ,    model.parameters()			)
		lowercase__					:      Any      =		sum([np.prod(p.size()			) for p in model_parameters]			)
		return params


__UpperCamelCase:	Dict = logging.getLogger(__name__)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if metric == "rouge2":
				lowercase__					:      str      =		'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				lowercase__					:      List[str]      =		'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				lowercase__					:      Optional[Any]      =		'{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__					:      Optional[int]      =		ModelCheckpoint(
		    dirpath=_lowercase   ,    filename=_lowercase   ,    monitor=f"""val_{metric}"""   ,    mode='max'   ,    save_top_k=3   ,    every_n_epochs=1   ,    )
		return checkpoint_callback


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Optional[Any]			)   ->							Union[str, Any]:




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





class       __lowerCAmelCase					(       pl.Callback      ):


	'''simple docstring'''





	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Any,					lowerCamelCase_:						int			):
			lowercase__					:      Any      =		{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    snake_case__(						self:						List[str],					lowerCamelCase_:						pl.Trainer,					lowerCamelCase_:						pl.LightningModule,					lowerCamelCase_:						str,					lowerCamelCase_:						Dict=True			):
			logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
			lowercase__					:      Optional[Any]      =		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__					:      Any      =		Path(pl_module.hparams.output_dir			)
			if type_path == "test":
					lowercase__					:      Tuple      =		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__					:      Dict      =		od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
					lowercase__					:      int      =		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
							lowercase__					:      Any      =		metrics[key]
							if isinstance(lowerCamelCase_,					torch.Tensor			):
									lowercase__					:      str      =		val.item()
							lowercase__					:      List[str]      =		F"""{key}: {val:.6f}\n"""
							writer.write(lowerCamelCase_			)

			if not save_generations:
					return

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

	@rank_zero_only
	def    snake_case__(						self:						str,					lowerCamelCase_:						List[str],					lowerCamelCase_:						Any			):
			try:
					lowercase__					:      List[Any]      =		pl_module.model.model.num_parameters()
			except AttributeError:
					lowercase__					:      List[str]      =		pl_module.model.num_parameters()

			lowercase__					:      str      =		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    snake_case__(						self:						List[str],					lowerCamelCase_:						pl.Trainer,					lowerCamelCase_:						pl.LightningModule			):
			save_json(pl_module.metrics,					pl_module.metrics_save_path			)
			return self._write_logs(lowerCamelCase_,					lowerCamelCase_,					'test'			)





	@rank_zero_only
	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						pl.Trainer,					lowerCamelCase_:						List[str]			):
			save_json(pl_module.metrics,					pl_module.metrics_save_path			)
			# Uncommenting this will save val generations
			# return self._write_logs(trainer, pl_module, "valid")









from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


