






'''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 	__magic_name__( _A					):
						'''simple docstring'''





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


lowerCamelCase_				: str    	=							logging.getLogger(__name__)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						if metric == "rouge2":
												UpperCamelCase__         = """{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												UpperCamelCase__         = """{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "em":
												UpperCamelCase__         = """{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__         = ModelCheckpoint(
						    dirpath=_A						,      filename=_A						,      monitor=f"val_{metric}"						,      mode="""max"""						,      save_top_k=3						,      every_n_epochs=1						,      )
						return checkpoint_callback







def 	__magic_name__( _A						,      _A					):
						'''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	):


			'''simple docstring'''





			def 				A      (   self							:						str ,      lowercase							:						Tuple ,      lowercase							:						Optional[int]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
									pl_module.logger.log_metrics(lowercase			)






			@rank_zero_only
			def 				A      (   self							:						Any ,      lowercase							:						pl.Trainer ,      lowercase							:						pl.LightningModule ,      lowercase							:						str ,      lowercase							:						Optional[int]=True			)							->    None:

									'''simple docstring'''







									logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"			)
									UpperCamelCase__         = 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__         = Path(pl_module.hparams.output_dir			)
									if type_path == "test":
															UpperCamelCase__         = od / """test_results.txt"""
															UpperCamelCase__         = 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__         = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
															UpperCamelCase__         = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=lowercase			)
															generations_file.parent.mkdir(exist_ok=lowercase			)
									with open(lowercase ,      """a+"""			) as writer:
															for key in sorted(lowercase			):
																					if key in ["log", "progress_bar", "preds"]:
																											continue
																					UpperCamelCase__         = metrics[key]
																					if isinstance(lowercase ,      torch.Tensor			):
																											UpperCamelCase__         = val.item()
																					UpperCamelCase__         = f"{key}: {val:.6f}\n"
																					writer.write(lowercase			)

									if not save_generations:
															return

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






			@rank_zero_only
			def 				A      (   self							:						Optional[int] ,      lowercase							:						Union[str, Any] ,      lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									try:
															UpperCamelCase__         = pl_module.model.model.num_parameters()
									except AttributeError:
															UpperCamelCase__         = pl_module.model.num_parameters()

									UpperCamelCase__         = count_trainable_parameters(lowercase			)
									# 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 				A      (   self							:						List[Any] ,      lowercase							:						pl.Trainer ,      lowercase							:						pl.LightningModule			)							->    Optional[int]:

									'''simple docstring'''







									save_json(pl_module.metrics ,      pl_module.metrics_save_path			)
									return self._write_logs(lowercase ,      lowercase ,      """test"""			)







			@rank_zero_only
			def 				A      (   self							:						Any ,      lowercase							:						pl.Trainer ,      lowercase							:						Tuple			)							->    Dict:

									'''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")








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_				: Tuple    	=							{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Tuple    	=							['''LayoutLMv3TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Union[str, Any]    	=							[
					    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''LayoutLMv3ForQuestionAnswering''',
					    '''LayoutLMv3ForSequenceClassification''',
					    '''LayoutLMv3ForTokenClassification''',
					    '''LayoutLMv3Model''',
					    '''LayoutLMv3PreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							[
					    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFLayoutLMv3ForQuestionAnswering''',
					    '''TFLayoutLMv3ForSequenceClassification''',
					    '''TFLayoutLMv3ForTokenClassification''',
					    '''TFLayoutLMv3Model''',
					    '''TFLayoutLMv3PreTrainedModel''',
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							['''LayoutLMv3FeatureExtractor''']
					lowerCamelCase_				: Dict    	=							['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
					from .configuration_layoutlmva import (
					    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LayoutLMvaConfig,
					    LayoutLMvaOnnxConfig,
					)
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlmva import (
										    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMvaForQuestionAnswering,
										    TFLayoutLMvaForSequenceClassification,
										    TFLayoutLMvaForTokenClassification,
										    TFLayoutLMvaModel,
										    TFLayoutLMvaPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
										from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
					import sys

					lowerCamelCase_				: Optional[Any]    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
