
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


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

UpperCAmelCase__		:     Tuple     			=      {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    '''deberta-v2'''


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any=1_2_8_1_0_0			,			lowerCAmelCase_     :							int=1_5_3_6			,			lowerCAmelCase_     :							Union[str, Any]=2_4			,			lowerCAmelCase_     :							List[Any]=2_4			,			lowerCAmelCase_     :							Tuple=6_1_4_4			,			lowerCAmelCase_     :							Optional[int]="gelu"			,			lowerCAmelCase_     :							str=0.1			,			lowerCAmelCase_     :							Union[str, Any]=0.1			,			lowerCAmelCase_     :							Optional[Any]=5_1_2			,			lowerCAmelCase_     :							Optional[Any]=0			,			lowerCAmelCase_     :							List[Any]=0.02			,			lowerCAmelCase_     :							Dict=1e-7			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Optional[Any]=-1			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							Union[str, Any]=True			,			lowerCAmelCase_     :							List[str]=None			,			lowerCAmelCase_     :							List[Any]=0			,			lowerCAmelCase_     :							List[str]="gelu"			,			**lowerCAmelCase_     :							Optional[Any]			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[str]					=       hidden_size
      _A:  List[Any]					=       num_hidden_layers
      _A:  Optional[int]					=       num_attention_heads
      _A:  Union[str, Any]					=       intermediate_size
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       hidden_dropout_prob
      _A:  Tuple					=       attention_probs_dropout_prob
      _A:  Tuple					=       max_position_embeddings
      _A:  str					=       type_vocab_size
      _A:  Optional[int]					=       initializer_range
      _A:  Optional[Any]					=       relative_attention
      _A:  int					=       max_relative_positions
      _A:  Union[str, Any]					=       pad_token_id
      _A:  Optional[Any]					=       position_biased_input

      # Backwards compatibility
      if type(lowerCAmelCase_     ) == str:
         _A:  Any					=       [x.strip() for x in pos_att_type.lower().split('''|'''     )]

      _A:  List[str]					=       pos_att_type
      _A:  Dict					=       vocab_size
      _A:  Optional[Any]					=       layer_norm_eps

      _A:  Dict					=       kwargs.get('''pooler_hidden_size'''			,			lowerCAmelCase_     )
      _A:  int					=       pooler_dropout
      _A:  List[Any]					=       pooler_hidden_act






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:  int					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:  str					=       {0: '''batch''', 1: '''sequence'''}
      if self._config.type_vocab_size > 0:
         return OrderedDict(
             [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]     )
      else:
         return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]     )


   @property
   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      return 1_2


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,			lowerCAmelCase_     :							int = -1			,			lowerCAmelCase_     :							int = -1			,			lowerCAmelCase_     :							int = -1			,			lowerCAmelCase_     :							bool = False			,			lowerCAmelCase_     :							Optional["TensorType"] = None			,			lowerCAmelCase_     :							int = 3			,			lowerCAmelCase_     :							int = 4_0			,			lowerCAmelCase_     :							int = 4_0			,			lowerCAmelCase_     :							"PreTrainedTokenizerBase" = None			,			):

      """simple docstring"""
      _A:  List[str]					=       super().generate_dummy_inputs(preprocessor=lowerCAmelCase_			,			framework=lowerCAmelCase_     )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs





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   lowerCamelCase__							(					a      )      ->   int:
   _A:  int					=       filter(lambda a      : p.requires_grad		,		model.parameters()      )
   _A:  Dict					=       sum([np.prod(p.size()      ) for p in model_parameters]      )
   return params


UpperCAmelCase__		:     str     			=      logging.getLogger(__name__)






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if metric == "rouge2":
      _A:  Tuple					=       '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      _A:  List[str]					=       '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      _A:  str					=       '''{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.'''      )

   _A:  List[str]					=       ModelCheckpoint(
       dirpath=a		,		filename=a		,		monitor=f"""val_{metric}"""		,		mode='''max'''		,		save_top_k=3		,		every_n_epochs=1		,		)
   return checkpoint_callback






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






class UpperCAmelCase   (						pl.Callback   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  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(lowerCAmelCase_     )


   @rank_zero_only
   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Any=True     ):

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

      if not save_generations:
         return

      if "preds" in metrics:
         _A:  Optional[int]					=       '''\n'''.join(metrics['''preds''']     )
         generations_file.open('''w+'''     ).write(lowerCAmelCase_     )


   @rank_zero_only
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      try:
         _A:  Union[str, Any]					=       pl_module.model.model.num_parameters()
      except AttributeError:
         _A:  Any					=       pl_module.model.num_parameters()

      _A:  Optional[int]					=       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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule     ):

      """simple docstring"""
      save_json(pl_module.metrics			,			pl_module.metrics_save_path     )
      return self._write_logs(lowerCAmelCase_			,			lowerCAmelCase_			,			'''test'''     )


   @rank_zero_only
   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							Optional[Any]     ):

      """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")



