


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       (     UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						filter(lambda UpperCAmelCase      : p.requires_grad						,	model.parameters()      )
      __lowerCamelCase     :      Dict       						=						sum([np.prod(p.size()      ) for p in model_parameters]      )
      return params


__UpperCamelCase    :						str     =							logging.getLogger(__name__)





def      _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      if metric == "rouge2":
            __lowerCamelCase     :      int       						=						'''{val_avg_rouge2:.4f}-{step_count}'''
      elif metric == "bleu":
            __lowerCamelCase     :      List[Any]       						=						'''{val_avg_bleu:.4f}-{step_count}'''
      elif metric == "em":
            __lowerCamelCase     :      Union[str, Any]       						=						'''{val_avg_em:.4f}-{step_count}'''
      elif metric == "loss":
            __lowerCamelCase     :      Union[str, Any]       						=						'''{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."""      )

      __lowerCamelCase     :      Union[str, Any]       						=						ModelCheckpoint(
          dirpath=snake_case_						,	filename=snake_case_						,	monitor=f"""val_{metric}"""						,	mode="""max"""						,	save_top_k=1						,	every_n_epochs=1						,	)

      return checkpoint_callback





def      _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




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




class 							_UpperCamelCase (						pl.Callback						):

   '''simple docstring'''







   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      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[Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Optional[Any]=True							):






         '''simple docstring'''



         logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
         __lowerCamelCase     :      Dict       						=						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     :      Union[str, Any]       						=						Path(pl_module.hparams.output_dir							)
         if type_path == "test":
               __lowerCamelCase     :      str       						=						od / '''test_results.txt'''
               __lowerCamelCase     :      int       						=						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     :      int       						=						od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
               __lowerCamelCase     :      Dict       						=						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
                     __lowerCamelCase     :      Tuple       						=						metrics[key]
                     if isinstance(_lowerCamelCase       ,  torch.Tensor							):
                           __lowerCamelCase     :      List[str]       						=						val.item()
                     __lowerCamelCase     :      int       						=						F"""{key}: {val:.6f}\n"""
                     writer.write(_lowerCamelCase							)

         if not save_generations:
               return

         if "preds" in metrics:
               __lowerCamelCase     :      Optional[Any]       						=						'''\n'''.join(metrics["""preds"""]							)
               generations_file.open("""w+"""							).write(_lowerCamelCase							)

   @rank_zero_only
   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         try:
               __lowerCamelCase     :      Dict       						=						pl_module.model.model.num_parameters()
         except AttributeError:
               __lowerCamelCase     :      Optional[int]       						=						pl_module.model.num_parameters()

         __lowerCamelCase     :      Tuple       						=						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[Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Optional[int]							):






         '''simple docstring'''



         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[Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : List[str]							):






         '''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")







import copy
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
from ..auto import CONFIG_MAPPING


__UpperCamelCase    :						Optional[int]     =							logging.get_logger(__name__)

__UpperCamelCase    :						Any     =							{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[int]    = "conditional_detr"
   a_				:							Union[str, Any]    = ["past_key_values"]
   a_				:							str    = {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }

   def __init__(  self  : Dict       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : int=None       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Tuple=3_0_0       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Union[str, Any]=2_0_4_8       ,  _lowerCamelCase  : List[str]=8       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Any=2_0_4_8       ,  _lowerCamelCase  : List[Any]=8       ,  _lowerCamelCase  : Optional[int]=0.0       ,  _lowerCamelCase  : Any=0.0       ,  _lowerCamelCase  : Tuple=True       ,  _lowerCamelCase  : Union[str, Any]="relu"       ,  _lowerCamelCase  : str=2_5_6       ,  _lowerCamelCase  : int=0.1       ,  _lowerCamelCase  : List[str]=0.0       ,  _lowerCamelCase  : Tuple=0.0       ,  _lowerCamelCase  : List[str]=0.02       ,  _lowerCamelCase  : Union[str, Any]=1.0       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : List[str]="sine"       ,  _lowerCamelCase  : Optional[Any]="resnet50"       ,  _lowerCamelCase  : List[str]=True       ,  _lowerCamelCase  : Dict=False       ,  _lowerCamelCase  : Optional[int]=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : List[Any]=2       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Union[str, Any]=0.25       ,  **_lowerCamelCase  : Tuple       ,  ):






         '''simple docstring'''



         if backbone_config is not None and use_timm_backbone:
               raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""							)

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
                     __lowerCamelCase     :      Any       						=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
               elif isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
                     __lowerCamelCase     :      str       						=						backbone_config.get("""model_type"""							)
                     __lowerCamelCase     :      Optional[Any]       						=						CONFIG_MAPPING[backbone_model_type]
                     __lowerCamelCase     :      Optional[Any]       						=						config_class.from_dict(_lowerCamelCase							)

         __lowerCamelCase     :      Optional[int]       						=						use_timm_backbone
         __lowerCamelCase     :      Union[str, Any]       						=						backbone_config
         __lowerCamelCase     :      List[Any]       						=						num_channels
         __lowerCamelCase     :      str       						=						num_queries
         __lowerCamelCase     :      Dict       						=						d_model
         __lowerCamelCase     :      List[Any]       						=						encoder_ffn_dim
         __lowerCamelCase     :      Optional[Any]       						=						encoder_layers
         __lowerCamelCase     :      Any       						=						encoder_attention_heads
         __lowerCamelCase     :      Dict       						=						decoder_ffn_dim
         __lowerCamelCase     :      List[str]       						=						decoder_layers
         __lowerCamelCase     :      Tuple       						=						decoder_attention_heads
         __lowerCamelCase     :      List[Any]       						=						dropout
         __lowerCamelCase     :      Any       						=						attention_dropout
         __lowerCamelCase     :      Optional[Any]       						=						activation_dropout
         __lowerCamelCase     :      List[str]       						=						activation_function
         __lowerCamelCase     :      Optional[int]       						=						init_std
         __lowerCamelCase     :      Union[str, Any]       						=						init_xavier_std
         __lowerCamelCase     :      Union[str, Any]       						=						encoder_layerdrop
         __lowerCamelCase     :      int       						=						decoder_layerdrop
         __lowerCamelCase     :      Dict       						=						encoder_layers
         __lowerCamelCase     :      Tuple       						=						auxiliary_loss
         __lowerCamelCase     :      Any       						=						position_embedding_type
         __lowerCamelCase     :      Tuple       						=						backbone
         __lowerCamelCase     :      int       						=						use_pretrained_backbone
         __lowerCamelCase     :      Tuple       						=						dilation
         # Hungarian matcher
         __lowerCamelCase     :      Dict       						=						class_cost
         __lowerCamelCase     :      Optional[Any]       						=						bbox_cost
         __lowerCamelCase     :      Any       						=						giou_cost
         # Loss coefficients
         __lowerCamelCase     :      Union[str, Any]       						=						mask_loss_coefficient
         __lowerCamelCase     :      List[Any]       						=						dice_loss_coefficient
         __lowerCamelCase     :      Any       						=						cls_loss_coefficient
         __lowerCamelCase     :      str       						=						bbox_loss_coefficient
         __lowerCamelCase     :      Dict       						=						giou_loss_coefficient
         __lowerCamelCase     :      List[str]       						=						focal_alpha
         super().__init__(is_encoder_decoder=_lowerCamelCase       ,  **_lowerCamelCase							)

   @property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return self.encoder_attention_heads

   @property
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         return self.d_model


   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						copy.deepcopy(self.__dict__							)
         if self.backbone_config is not None:
               __lowerCamelCase     :      Dict       						=						self.backbone_config.to_dict()
         __lowerCamelCase     :      int       						=						self.__class__.model_type
         return output



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Dict    = version.parse("1.11"						)

   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



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

   @property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return 1E-5


   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         return 1_2



