






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_ (      _A  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     filter(lambda _A  : p.requires_grad      ,   model.parameters()  )
				snake_case__									=     sum([np.prod(p.size()  ) for p in model_parameters]  )
				return params


__UpperCamelCase					:		Any	  =						logging.getLogger(__name__)
def      a_ (      _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				if metric == "rouge2":
								snake_case__									=     '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								snake_case__									=     '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "em":
								snake_case__									=     '{val_avg_em:.4f}-{step_count}'
				elif metric == "loss":
								snake_case__									=     '{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.'  )

				snake_case__									=     ModelCheckpoint(
				    dirpath=_A      ,   filename=_A      ,   monitor=f'''val_{metric}'''      ,   mode='max'      ,   save_top_k=1      ,   every_n_epochs=1      ,   )

				return checkpoint_callback
def      a_ (      _A      ,   _A  )			->			Optional[Any]:







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


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

											if not save_generations:
															return

											if "preds" in metrics:
															snake_case__									=     '\n'.join(metrics['preds']						)
															generations_file.open('w+'						).write(UpperCamelCase						)
							@rank_zero_only
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      Union[str, Any]						)		->		Optional[Any]:
											try:
															snake_case__									=     pl_module.model.model.num_parameters()
											except AttributeError:
															snake_case__									=     pl_module.model.num_parameters()

											snake_case__									=     count_trainable_parameters(UpperCamelCase						)
											# 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 lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      pl.Trainer       ,	UpperCamelCase:      pl.LightningModule						)		->		Dict:
											save_json(pl_module.metrics       ,	pl_module.metrics_save_path						)
											return self._write_logs(UpperCamelCase       ,	UpperCamelCase       ,	'test'						)


							@rank_zero_only
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      pl.Trainer       ,	UpperCamelCase:      Union[str, Any]						)		->		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")












from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


class   __SCREAMING_SNAKE_CASE(	a_	):
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
															snake_case__									=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															snake_case__									=     {0: 'batch', 1: 'sequence'}
											snake_case__									=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]						)




