


'''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    lowercase_	(       _snake_case  ):
    __UpperCAmelCase       =      ['''image_processor''', '''tokenizer''']
    __UpperCAmelCase       =      '''LayoutLMv2ImageProcessor'''
    __UpperCAmelCase       =      ('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


    def __init__(	self    ,   a=None    ,   a=None    ,   **a       ):
          UpperCamelCase__      =							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    ,   )
                UpperCamelCase__      =							kwargs.pop("feature_extractor"       )

          UpperCamelCase__      =							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    ,   ):
          # 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
          UpperCamelCase__      =							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       ):
                      UpperCamelCase__      =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
                UpperCamelCase__      =							features['words']

          UpperCamelCase__      =							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
          UpperCamelCase__      =							features.pop("pixel_values"       )
          if return_overflowing_tokens is True:
                UpperCamelCase__      =							self.get_overflowing_images(_a    ,   encoded_inputs["overflow_to_sample_mapping"]       )
          UpperCamelCase__      =							images

          return encoded_inputs


    def   __a				(	self    ,   a    ,   a       ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCamelCase__      =							[]
          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   __a				(	self    ,   *a    ,   **a       ):
          return self.tokenizer.batch_decode(*_a    ,   **_a       )


    def   __a				(	self    ,   *a    ,   **a       ):
          return self.tokenizer.decode(*_a    ,   **_a       )


    @property
    def   __a				(	self       ):
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


    @property
    def   __a				(	self       ):
          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   __a				(	self       ):
          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_	,							)




