
"""simple docstring"""



import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				(						__lowerCamelCase	):
     __a						=					"""sequence-classification"""







     def __init__(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       if type(_lowerCamelCase			) == dict:
         _snake_case					     =       Namespace(**_lowerCamelCase			)
       _snake_case					     =       glue_output_modes[hparams.task]
       _snake_case					     =       glue_tasks_num_labels[hparams.task]

       super().__init__(_lowerCamelCase    ,      _lowerCamelCase    ,      self.mode			)







     def      lowercase	(							self			:			Optional[int]    ,      **_lowerCamelCase			:			int			):
       return self.model(**_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

       if self.config.model_type not in ["distilbert", "bart"]:
         _snake_case					     =       batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

       _snake_case					     =       self(**_lowerCamelCase			)
       _snake_case					     =       outputs[0]

       _snake_case					     =       self.trainer.lr_schedulers[0]['''scheduler''']
       _snake_case					     =       {'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss, "log": tensorboard_logs}







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.hparams
       _snake_case					     =       processors[args.task]()
       _snake_case					     =       processor.get_labels()

       for mode in ["train", "dev"]:
         _snake_case					     =       self._feature_file(_lowerCamelCase			)
         if os.path.exists(_lowerCamelCase			) and not args.overwrite_cache:
           logger.info('''Loading features from cached file %s'''    ,      _lowerCamelCase			)
         else:
           logger.info('''Creating features from dataset file at %s'''    ,      args.data_dir			)
           _snake_case					     =       (
               processor.get_dev_examples(args.data_dir			)
               if mode == '''dev'''
               else processor.get_train_examples(args.data_dir			)
           )
           _snake_case					     =       convert_examples_to_features(
               _lowerCamelCase    ,      self.tokenizer    ,      max_length=args.max_seq_length    ,      label_list=self.labels    ,      output_mode=args.glue_output_mode    ,      )
           logger.info('''Saving features into cached file %s'''    ,      _lowerCamelCase			)
           torch.save(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			List[str] = False			):
       _snake_case					     =       '''dev''' if mode == '''test''' else mode

       _snake_case					     =       self._feature_file(_lowerCamelCase			)
       logger.info('''Loading features from cached file %s'''    ,      _lowerCamelCase			)
       _snake_case					     =       torch.load(_lowerCamelCase			)
       _snake_case					     =       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long			)
       _snake_case					     =       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long			)
       _snake_case					     =       torch.tensor([f.token_type_ids for f in features]    ,      dtype=torch.long			)
       if self.hparams.glue_output_mode == "classification":
         _snake_case					     =       torch.tensor([f.label for f in features]    ,      dtype=torch.long			)
       elif self.hparams.glue_output_mode == "regression":
         _snake_case					     =       torch.tensor([f.label for f in features]    ,      dtype=torch.float			)

       return DataLoader(
           TensorDataset(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)    ,      batch_size=_lowerCamelCase    ,      shuffle=_lowerCamelCase    ,      )







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

       if self.config.model_type not in ["distilbert", "bart"]:
         _snake_case					     =       batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

       _snake_case					     =       self(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       outputs[:2]
       _snake_case					     =       logits.detach().cpu().numpy()
       _snake_case					     =       inputs['''labels'''].detach().cpu().numpy()

       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       torch.stack([x['''val_loss'''] for x in outputs]			).mean().detach().cpu().item()
       _snake_case					     =       np.concatenate([x['''pred'''] for x in outputs]    ,      axis=0			)

       if self.hparams.glue_output_mode == "classification":
         _snake_case					     =       np.argmax(_lowerCamelCase    ,      axis=1			)
       elif self.hparams.glue_output_mode == "regression":
         _snake_case					     =       np.squeeze(_lowerCamelCase			)

       _snake_case					     =       np.concatenate([x['''target'''] for x in outputs]    ,      axis=0			)
       _snake_case					     =       [[] for _ in range(out_label_ids.shape[0]			)]
       _snake_case					     =       [[] for _ in range(out_label_ids.shape[0]			)]

       _snake_case					     =       {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task    ,      _lowerCamelCase    ,      _lowerCamelCase			)}

       _snake_case					     =       dict(results.items()			)
       _snake_case					     =       results
       return ret, preds_list, out_label_list







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case     ,					_snake_case     ,					_snake_case					     =       self._eval_end(_lowerCamelCase			)
       _snake_case					     =       ret['''log''']
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case     ,					_snake_case     ,					_snake_case					     =       self._eval_end(_lowerCamelCase			)
       _snake_case					     =       ret['''log''']
       # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
       return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







     @staticmethod
     def      lowercase	(							_lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			List[Any]			):
       BaseTransformer.add_model_specific_args(_lowerCamelCase    ,      _lowerCamelCase			)
       parser.add_argument(
           '''--max_seq_length'''    ,      default=128    ,      type=_lowerCamelCase    ,      help=(
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )    ,      )

       parser.add_argument(
           '''--task'''    ,      default=''''''    ,      type=_lowerCamelCase    ,      required=_lowerCamelCase    ,      help='''The GLUE task to run'''    ,      )
       parser.add_argument(
           '''--gpus'''    ,      default=0    ,      type=_lowerCamelCase    ,      help='''The number of GPUs allocated for this, it is by default 0 meaning none'''    ,      )

       parser.add_argument(
           '''--overwrite_cache'''    ,      action='''store_true'''    ,      help='''Overwrite the cached training and evaluation sets'''			)

       return parser






def 					_UpperCAmelCase      ( )     ->					List[str]:
  _snake_case					     =       argparse.ArgumentParser()
  add_generic_args(__SCREAMING_SNAKE_CASE ,					os.getcwd()		)
  _snake_case					     =       GLUETransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE ,					os.getcwd()		)
  _snake_case					     =       parser.parse_args()

  # If output_dir not provided, a folder will be generated in pwd
  if args.output_dir is None:
    _snake_case					     =       os.path.join(
        '''./results''' ,					f'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'		)}''' ,					)
    os.makedirs(args.output_dir		)

  _snake_case					     =       GLUETransformer(__SCREAMING_SNAKE_CASE		)
  _snake_case					     =       generic_train(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE		)

  # Optionally, predict on dev set and write to output_dir
  if args.do_predict:
    _snake_case					     =       sorted(glob.glob(os.path.join(args.output_dir ,					'''checkpoint-epoch=*.ckpt'''		) ,					recursive=__SCREAMING_SNAKE_CASE		)		)
    _snake_case					     =       model.load_from_checkpoint(checkpoints[-1]		)
    return trainer.test(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 main()





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
	import PIL

# soft dependency
if is_pytesseract_available():
	import pytesseract

UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	return [
	    int(1000 * (box[0] / width)							),
	    int(1000 * (box[1] / height)							),
	    int(1000 * (box[2] / width)							),
	    int(1000 * (box[3] / height)							),
	]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    data['text'], data['left'], data['top'], data['width'], data['height']

	# filter empty words and corresponding coordinates
	lowercase       =    [idx for idx, word in enumerate(__SCREAMING_SNAKE_CASE							) if not word.strip()]
	lowercase       =    [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]

	# turn coordinates into (left, top, left+width, top+height) format
	lowercase       =    []
	for x, y, w, h in zip(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		lowercase       =    [x, y, x + w, y + h]
		actual_boxes.append(__SCREAMING_SNAKE_CASE							)

	# finally, normalize the bounding boxes
	lowercase       =    []
	for box in actual_boxes:
		normalized_boxes.append(normalize_box(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)							)

	assert len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							), "Not as many words as there are bounding boxes"

	return words, normalized_boxes







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    snake_case = None		,    snake_case = ""		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_rescale
		lowercase       =    rescale_value
		lowercase       =    do_normalize
		lowercase       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		lowercase       =    image_std if image_std is not None else IMAGENET_STANDARD_STD
		lowercase       =    apply_ocr
		lowercase       =    ocr_lang
		lowercase       =    tesseract_config





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
		lowercase       =    (size['height'], size['width'])
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case=None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase       =    ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase       =    tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(snake_case       ):
			raise ValueError(
			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
			    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

		if do_resize and size is None:
			raise ValueError('Size must be specified if do_resize is True.'       )

		if do_rescale and rescale_factor is None:
			raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'       )

		# All transformations expect numpy arrays.
		lowercase       =    [to_numpy_array(snake_case       ) for image in images]

		# Tesseract OCR to get words + normalized bounding boxes
		if apply_ocr:
			requires_backends(self		,    'pytesseract'       )
			lowercase       =    []
			lowercase       =    []
			for image in images:
				lowercase  ,					lowercase       =    apply_tesseract(snake_case		,    snake_case		,    snake_case       )
				words_batch.append(snake_case       )
				boxes_batch.append(snake_case       )

		if do_resize:
			lowercase       =    [self.resize(image=snake_case		,    size=snake_case		,    resample=snake_case       ) for image in images]

		if do_rescale:
			lowercase       =    [self.rescale(image=snake_case		,    scale=snake_case       ) for image in images]

		if do_normalize:
			lowercase       =    [self.normalize(image=snake_case		,    mean=snake_case		,    std=snake_case       ) for image in images]

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    BatchFeature(data={'pixel_values': images}		,    tensor_type=snake_case       )

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

