

'''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  a__       (    _SCREAMING_SNAKE_CASE   :       int				)     ->					Optional[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      int						=  filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad	,				model.parameters()				)
	UpperCAmelCase_     :      Union[str, Any]						=  sum([np.prod(p.size()				) for p in model_parameters]				)
	return params


_lowerCamelCase      					=     logging.getLogger(__name__)


def  a__       (    _SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       str				)     ->					str:


	"""simple docstring"""





	if metric == "rouge2":
		UpperCAmelCase_     :      int						=  "{val_avg_rouge2:.4f}-{step_count}"
	elif metric == "bleu":
		UpperCAmelCase_     :      Dict						=  "{val_avg_bleu:.4f}-{step_count}"
	elif metric == "em":
		UpperCAmelCase_     :      List[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."				)

	UpperCAmelCase_     :      Tuple						=  ModelCheckpoint(
	    dirpath=_SCREAMING_SNAKE_CASE	,				filename=_SCREAMING_SNAKE_CASE	,				monitor=F'''val_{metric}'''	,				mode="max"	,				save_top_k=3	,				every_n_epochs=1	,				)
	return checkpoint_callback


def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					List[str]:


	"""simple docstring"""





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





class 					_snake_case		(pl.Callback):






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Union[str, Any]						=  {f'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
				pl_module.logger.log_metrics(_snake_case     )






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

				if not save_generations:
					return

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






			@rank_zero_only
			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				try:
					UpperCAmelCase_     :      List[Any]						=  pl_module.model.model.num_parameters()
				except AttributeError:
					UpperCAmelCase_     :      Any						=  pl_module.model.num_parameters()

				UpperCAmelCase_     :      Union[str, Any]						=  count_trainable_parameters(_snake_case     )
				# 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			,_snake_case			,_snake_case     ):
				save_json(pl_module.metrics			,pl_module.metrics_save_path     )
				return self._write_logs(_snake_case			,_snake_case			,"test"     )






			@rank_zero_only
			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						Any        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''poolformer'''

					def __init__(self       :		Dict ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		str=16 ,   _UpperCAmelCase       :		Any=16 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Union[str, Any]=4.0 ,   _UpperCAmelCase       :		str=[2, 2, 6, 2] ,   _UpperCAmelCase       :		int=[64, 128, 320, 512] ,   _UpperCAmelCase       :		Union[str, Any]=[7, 3, 3, 3] ,   _UpperCAmelCase       :		List[Any]=[4, 2, 2, 2] ,   _UpperCAmelCase       :		Union[str, Any]=[2, 1, 1, 1] ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict=0.0 ,   _UpperCAmelCase       :		Union[str, Any]="gelu" ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		Tuple=1E-5 ,   _UpperCAmelCase       :		Tuple=0.02 ,   **_UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						num_channels
							lowercase__	     =						patch_size
							lowercase__	     =						stride
							lowercase__	     =						padding
							lowercase__	     =						pool_size
							lowercase__	     =						hidden_sizes
							lowercase__	     =						mlp_ratio
							lowercase__	     =						depths
							lowercase__	     =						patch_sizes
							lowercase__	     =						strides
							lowercase__	     =						num_encoder_blocks
							lowercase__	     =						drop_path_rate
							lowercase__	     =						hidden_act
							lowercase__	     =						use_layer_scale
							lowercase__	     =						layer_scale_init_value
							lowercase__	     =						initializer_range
							super().__init__(**_UpperCAmelCase						)






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					version.parse('''1.11'''				)

					@property
					def     lowerCamelCase__	(self       :		List[str]						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]						)


					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  float:




							"""simple docstring"""


							return 2E-3

