





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__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   filter(lambda UpperCamelCase__     : p.requires_grad       , model.parameters()     )
		SCREAMING_SNAKE_CASE__								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
		return params


_lowerCamelCase 					=      logging.getLogger(__name__)






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

		SCREAMING_SNAKE_CASE__								=   ModelCheckpoint(
		    dirpath=UpperCamelCase__       , filename=UpperCamelCase__       , monitor=f'''val_{metric}'''       , mode="""max"""       , save_top_k=3       , every_n_epochs=1       , )
		return checkpoint_callback






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				List[str]     ):
		return EarlyStopping(
		    monitor=f'''val_{metric}'''       , mode="""min""" if """loss""" in metric else """max"""       , patience=UpperCamelCase__       , verbose=UpperCamelCase__       , )




class 	UpperCamelCase_							(  pl.Callback     ):


							def   _snake_case	(				self     :List[Any]				,					__A     :Any				,					__A     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {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   _snake_case	(				self     :Union[str, Any]				,					__A     :pl.Trainer				,					__A     :pl.LightningModule				,					__A     :str				,					__A     :str=True		) -> None:



									"""simple docstring"""



									logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''		)
									SCREAMING_SNAKE_CASE__								=   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
									SCREAMING_SNAKE_CASE__								=   Path(pl_module.hparams.output_dir		)
									if type_path == "test":
											SCREAMING_SNAKE_CASE__								=   od / """test_results.txt"""
											SCREAMING_SNAKE_CASE__								=   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.
											SCREAMING_SNAKE_CASE__								=   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
											SCREAMING_SNAKE_CASE__								=   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
													SCREAMING_SNAKE_CASE__								=   metrics[key]
													if isinstance(__A				,					torch.Tensor		):
															SCREAMING_SNAKE_CASE__								=   val.item()
													SCREAMING_SNAKE_CASE__								=   f'''{key}: {val:.6f}\n'''
													writer.write(__A		)

									if not save_generations:
											return

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


							@rank_zero_only
							def   _snake_case	(				self     :List[str]				,					__A     :str				,					__A     :Union[str, Any]		) -> List[str]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :List[Any]				,					__A     :pl.Trainer				,					__A     :pl.LightningModule		) -> Dict:



									"""simple docstring"""



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


							@rank_zero_only
							def   _snake_case	(				self     :int				,					__A     :pl.Trainer				,					__A     :Dict		) -> int:



									"""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")






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42




class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__     ):


							@register_to_config
							def __init__(				self     :Union[str, Any]				,					__A     :int = 3				,					__A     :int = 3				,					__A     :Tuple[str] = ("DownEncoderBlock2D",)				,					__A     :Tuple[str] = ("UpDecoderBlock2D",)				,					__A     :Tuple[int] = (64,)				,					__A     :int = 1				,					__A     :str = "silu"				,					__A     :int = 3				,					__A     :int = 32				,					__A     :int = 256				,					__A     :int = 32				,					__A     :Optional[int] = None				,					__A     :float = 0.1_8_2_1_5				,					__A     :str = "group"				,					) -> Any:



									"""simple docstring"""



									super().__init__()

									# pass init params to Encoder
									SCREAMING_SNAKE_CASE__								=   Encoder(
									    in_channels=__A				,					out_channels=__A				,					down_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					double_z=__A				,					)

									SCREAMING_SNAKE_CASE__								=   vq_embed_dim if vq_embed_dim is not None else latent_channels

									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   VectorQuantizer(__A				,					__A				,					beta=0.2_5				,					remap=__A				,					sane_index_shape=__A		)
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)

									# pass init params to Decoder
									SCREAMING_SNAKE_CASE__								=   Decoder(
									    in_channels=__A				,					out_channels=__A				,					up_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					norm_type=__A				,					)


							@apply_forward_hook
							def   _snake_case	(				self     :Union[str, Any]				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> VQEncoderOutput:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.encoder(__A		)
									SCREAMING_SNAKE_CASE__								=   self.quant_conv(__A		)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=__A		)


							@apply_forward_hook
							def   _snake_case	(				self     :Tuple				,					__A     :torch.FloatTensor				,					__A     :bool = False				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									if not force_not_quantize:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.quantize(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   h
									SCREAMING_SNAKE_CASE__								=   self.post_quant_conv(__A		)
									SCREAMING_SNAKE_CASE__								=   self.decoder(__A				,					quant if self.config.norm_type == """spatial""" else None		)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)


							def   _snake_case	(				self     :int				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sample
									SCREAMING_SNAKE_CASE__								=   self.encode(__A		).latents
									SCREAMING_SNAKE_CASE__								=   self.decode(__A		).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)