


"""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   __lowerCAmelCase						(						lowercase  :  Optional[Any]			)     ->						List[Any]:


    """simple docstring"""





    snake_case   :				int				 =   filter(lambda lowercase			: p.requires_grad						,							model.parameters()			)
    snake_case   :				List[str]				 =   sum([np.prod(p.size()			) for p in model_parameters]			)
    return params


__snake_case             =   logging.getLogger(__name__)







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  List[str]			)     ->						str:


    """simple docstring"""





    if metric == "rouge2":
        snake_case   :				Optional[Any]				 =   "{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        snake_case   :				Union[str, Any]				 =   "{val_avg_bleu:.4f}-{step_count}"
    elif metric == "em":
        snake_case   :				List[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."			)

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







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int			)     ->						int:


    """simple docstring"""





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







class _lowerCAmelCase    (				pl.Callback     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   {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						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__=True						)     ->		None:

       '''simple docstring'''




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

       if not save_generations:
           return

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







   @rank_zero_only
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       try:
           snake_case   :				Optional[Any]				 =   pl_module.model.model.num_parameters()
       except AttributeError:
           snake_case   :				Dict				 =   pl_module.model.num_parameters()

       snake_case   :				Optional[int]				 =   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						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




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







   @rank_zero_only
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[int]:

       '''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"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase			:	str							  =  '''LayoutLMv2ImageProcessor'''
   __UpperCAmelCase			:	Dict							  =  ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."						,     UpperCamelCase__						,     )
           snake_case   :				Dict				 =   kwargs.pop("feature_extractor"						)

       snake_case   :				int				 =   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(UpperCamelCase__						,     UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchEncoding:

       '''simple docstring'''




       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes "
               "if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       snake_case   :				Any				 =   self.image_processor(images=UpperCamelCase__						,     return_tensors=UpperCamelCase__						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				Optional[int]				 =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case   :				Optional[Any]				 =   features["words"]

       snake_case   :				Dict				 =   self.tokenizer(
           text=text if text is not None else features["words"]						,     text_pair=text_pair if text_pair is not None else None						,     boxes=boxes if boxes is not None else features["boxes"]						,     word_labels=UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						,     padding=UpperCamelCase__						,     truncation=UpperCamelCase__						,     max_length=UpperCamelCase__						,     stride=UpperCamelCase__						,     pad_to_multiple_of=UpperCamelCase__						,     return_token_type_ids=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						,     return_overflowing_tokens=UpperCamelCase__						,     return_special_tokens_mask=UpperCamelCase__						,     return_offsets_mapping=UpperCamelCase__						,     return_length=UpperCamelCase__						,     verbose=UpperCamelCase__						,     return_tensors=UpperCamelCase__						,     **UpperCamelCase__						,     )

       # add pixel values
       snake_case   :				Dict				 =   features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           snake_case   :				Any				 =   self.get_overflowing_images(UpperCamelCase__						,     encoded_inputs["overflow_to_sample_mapping"]						)
       snake_case   :				str				 =   images

       return encoded_inputs







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(UpperCamelCase__						) != len(UpperCamelCase__						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F' {len(UpperCamelCase__						)} and {len(UpperCamelCase__						)}'						)

       return images_with_overflow







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       return self.tokenizer.batch_decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       return self.tokenizer.decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       return ["input_ids", "bbox", "attention_mask", "image"]







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,     UpperCamelCase__						,     )
       return self.image_processor_class







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,     UpperCamelCase__						,     )
       return self.image_processor





