'''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    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
   A				:							int			     =     '''LayoutLMv2ImageProcessor'''
   A				:							Tuple			     =     ('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')
   def __init__(self							,					_a=None							,					_a=None							,					**_a					)     ->  str:
        lowercase_       :							Union[str, Any]  					=       None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'							,					_a							,					)
             lowercase_       :							Optional[Any]  					=       kwargs.pop('feature_extractor'					)

        lowercase_       :							Dict  					=       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__(_a							,					_a					)
   def __call__(self							,					_a							,					_a = None							,					_a = None							,					_a = None							,					_a = None							,					_a = True							,					_a = False							,					_a = False							,					_a = None							,					_a = 0							,					_a = None							,					_a = None							,					_a = None							,					_a = False							,					_a = False							,					_a = False							,					_a = False							,					_a = True							,					_a = None							,					**_a							,					)     ->  BatchEncoding:
        # verify input
        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
        lowercase_       :							List[Any]  					=       self.image_processor(images=_a							,					return_tensors=_a					)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(_a							,					_a					):
                  lowercase_       :							Union[str, Any]  					=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowercase_       :							Any  					=       features['words']

        lowercase_       :							int  					=       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=_a							,					add_special_tokens=_a							,					padding=_a							,					truncation=_a							,					max_length=_a							,					stride=_a							,					pad_to_multiple_of=_a							,					return_token_type_ids=_a							,					return_attention_mask=_a							,					return_overflowing_tokens=_a							,					return_special_tokens_mask=_a							,					return_offsets_mapping=_a							,					return_length=_a							,					verbose=_a							,					return_tensors=_a							,					**_a							,					)

        # add pixel values
        lowercase_       :							Union[str, Any]  					=       features.pop('pixel_values'					)
        if return_overflowing_tokens is True:
             lowercase_       :							Dict  					=       self.get_overflowing_images(_a							,					encoded_inputs['overflow_to_sample_mapping']					)
        lowercase_       :							List[Any]  					=       images

        return encoded_inputs
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  List[str]:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        lowercase_       :							Optional[Any]  					=       []
        for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]					)

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

        return images_with_overflow
   def 		_lowerCamelCase			(self							,					*_a							,					**_a					)     ->  Any:
        return self.tokenizer.batch_decode(*_a							,					**_a					)
   def 		_lowerCamelCase			(self							,					*_a							,					**_a					)     ->  Dict:
        return self.tokenizer.decode(*_a							,					**_a					)
   @property
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
   @property
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,					_a							,					)
        return self.image_processor_class





   @property
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,					_a							,					)
        return self.image_processor





'''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 import save_json

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       filter(lambda SCREAMING_SNAKE_CASE_				: p.requires_grad	,							model.parameters()				)
     lowercase_       :							Tuple  					=       sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	(							pl.Callback      ):






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

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_       :							Optional[Any]  					=       '\n'.join(metrics['preds']					)
             generations_file.open('w+'					).write(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Tuple:
        try:
             lowercase_       :							List[Any]  					=       pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_       :							Dict  					=       pl_module.model.num_parameters()

        lowercase_       :							Optional[Any]  					=       count_trainable_parameters(_a					)
        # 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							,					_a							,					_a					)     ->  int:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        return self._write_logs(_a							,					_a							,					'test'					)





   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  List[str]:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=1	,							SCREAMING_SNAKE_CASE_=False				):
     if metric == "rouge2":
          lowercase_       :							Optional[int]  					=       '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_       :							int  					=       '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "loss":
          lowercase_       :							List[Any]  					=       '{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              ' this function.'				)

     lowercase_       :							Tuple  					=       ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE_	,							filename=SCREAMING_SNAKE_CASE_	,							monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							save_top_k=SCREAMING_SNAKE_CASE_	,							)
     return checkpoint_callback






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return EarlyStopping(
         monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							patience=SCREAMING_SNAKE_CASE_	,							verbose=SCREAMING_SNAKE_CASE_	,							)




