






"""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 import save_json







def 					lowerCamelCase__	(							_lowerCamelCase	:		int			)				->    Any:
  lowerCamelCase_   					=						filter(lambda _lowerCamelCase			: p.requires_grad		,    model.parameters()			)
  lowerCamelCase_   					=						sum([np.prod(p.size()			) for p in model_parameters]			)
  return params


_SCREAMING_SNAKE_CASE :    str    =						logging.getLogger(__name__)






class 					a						( pl.Callback		):






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		int:
      lowerCamelCase_   					=						{F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
      pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE )






    @rank_zero_only
    def        UpperCamelCase	(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							pl.Trainer	,						__SCREAMING_SNAKE_CASE					:							pl.LightningModule	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							List[Any]=True )					->		None:
      logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
      lowerCamelCase_   					=						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
      lowerCamelCase_   					=						Path(pl_module.hparams.output_dir )
      if type_path == "test":
        lowerCamelCase_   					=						od / 'test_results.txt'
        lowerCamelCase_   					=						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.
        lowerCamelCase_   					=						od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
        lowerCamelCase_   					=						od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
        results_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
        generations_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
      with open(__SCREAMING_SNAKE_CASE	,						'a+' ) as writer:
        for key in sorted(__SCREAMING_SNAKE_CASE ):
          if key in ["log", "progress_bar", "preds"]:
            continue
          lowerCamelCase_   					=						metrics[key]
          if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor ):
            lowerCamelCase_   					=						val.item()
          lowerCamelCase_   					=						F'''{key}: {val:.6f}\n'''
          writer.write(__SCREAMING_SNAKE_CASE )

      if not save_generations:
        return

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






    @rank_zero_only
    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any] )					->		int:
      try:
        lowerCamelCase_   					=						pl_module.model.model.num_parameters()
      except AttributeError:
        lowerCamelCase_   					=						pl_module.model.num_parameters()

      lowerCamelCase_   					=						count_trainable_parameters(__SCREAMING_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					:							int	,						__SCREAMING_SNAKE_CASE					:							pl.Trainer	,						__SCREAMING_SNAKE_CASE					:							pl.LightningModule )					->		str:
      save_json(pl_module.metrics	,						pl_module.metrics_save_path )
      return self._write_logs(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						'test' )






    @rank_zero_only
    def        UpperCamelCase	(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							pl.Trainer	,						__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		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")







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Any=1		,    _lowerCamelCase	:		Any=False			)				->    Any:
  if metric == "rouge2":
    lowerCamelCase_   					=						'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    lowerCamelCase_   					=						'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "loss":
    lowerCamelCase_   					=						'{val_avg_loss:.4f}-{step_count}'
  else:
    raise NotImplementedError(
        F'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
        ' this function.'			)

  lowerCamelCase_   					=						ModelCheckpoint(
      dirpath=_lowerCamelCase		,    filename=_lowerCamelCase		,    monitor=F'''val_{metric}'''		,    mode='min' if 'loss' in metric else 'max'		,    save_top_k=_lowerCamelCase		,    )
  return checkpoint_callback







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












"""simple docstring"""





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class 					a						( enum.Enum		):
    SCREAMING_SNAKE_CASE						:					str						=			"""timm"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			"""transformers"""







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Iterable[str]]		,    _lowerCamelCase	:		Optional[Iterable[int]]		,    _lowerCamelCase	:		Optional[Iterable[str]]			)				->    List[Any]:
  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'			)

  if out_features is not None:
    if not isinstance(_lowerCamelCase		,    (list,)			):
      raise ValueError(F'''out_features must be a list {type(_lowerCamelCase			)}'''			)
    if any(feat not in stage_names for feat in out_features			):
      raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''			)

  if out_indices is not None:
    if not isinstance(_lowerCamelCase		,    (list, tuple)			):
      raise ValueError(F'''out_indices must be a list or tuple, got {type(_lowerCamelCase			)}'''			)
    if any(idx >= len(_lowerCamelCase			) for idx in out_indices			):
      raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'			)

  if out_features is not None and out_indices is not None:
    if len(_lowerCamelCase			) != len(_lowerCamelCase			):
      raise ValueError('out_features and out_indices should have the same length if both are set'			)
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('out_features and out_indices should correspond to the same stages if both are set'			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Union[str, Any]:
  if out_indices is None and out_features is None:
    lowerCamelCase_   					=						[len(_lowerCamelCase			) - 1]
    lowerCamelCase_   					=						[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    lowerCamelCase_   					=						[stage_names.index(_lowerCamelCase			) for layer in out_features]
  elif out_features is None and out_indices is not None:
    lowerCamelCase_   					=						[stage_names[idx] for idx in out_indices]
  return out_features, out_indices







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Tuple[List[str], List[int]]:
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						_align_output_features_output_indices(
      out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  return output_features, output_indices






class 					a						:
    SCREAMING_SNAKE_CASE						:					Optional[BackboneType]						=			None






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		None:
      if getattr(self	,						'_backbone'	,						__SCREAMING_SNAKE_CASE ) is None:
        raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      lowerCamelCase_   					=						[stage['module'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						[stage['num_chs'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						self._backbone.feature_info.out_indices
      lowerCamelCase_   					=						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )
      lowerCamelCase_					,     lowerCamelCase_   					=						out_features, out_indices






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Any )					->		None:
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'stage_names' )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_features'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_indices'	,						__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						stage_names
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=__SCREAMING_SNAKE_CASE )
      # Number of channels for each stage. This is set in the transformer backbone model init
      lowerCamelCase_   					=						None






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		None:
      lowerCamelCase_   					=						config

      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'use_timm_backbone'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(__SCREAMING_SNAKE_CASE )
      elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(__SCREAMING_SNAKE_CASE )
      else:
        raise ValueError(F'''backbone_type {self.backbone_type} not supported.''' )






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[Any]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							List[Any] )					->		Dict:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]






    def        UpperCamelCase	(	self					:							Optional[int]	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Tuple:
      lowerCamelCase_   					=						dict(inspect.signature(self.forward ).parameters )
      lowerCamelCase_   					=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						)					->		int:
      raise NotImplementedError('This method should be implemented by the derived class.' )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[str]:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output






class 					a						:






    @property
    def        UpperCamelCase	(	self					:							int )					->		Optional[Any]:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Tuple:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		str:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    def        UpperCamelCase	(	self					:							int )					->		str:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output




