

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

_a:    Optional[int] 							=			logging.get_logger(__name__)



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



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

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

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

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

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

	return words, normalized_boxes




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['pixel_values']




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						float = 1 / 255		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[str] = ""		,	**lowerCAmelCase	:						Union[str, Any]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)
		UpperCAmelCase_       				=			size if size is not None else {"height": 224, "width": 224}
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)

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




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)
		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()}"							)
		UpperCAmelCase_       				=			(size["height"], size["width"])
		return resize(lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[int, float]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







		return rescale(lowerCAmelCase		,	scale=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[float, Iterable[float]]		,	lowerCAmelCase	:						Union[float, Iterable[float]]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						str		,	):


		'''simple docstring'''







		return normalize(lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						List[str]=None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[Union[str, TensorType]] = None		,	lowerCAmelCase	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			do_resize if do_resize is not None else self.do_resize
		UpperCAmelCase_       				=			size if size is not None else self.size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)
		UpperCAmelCase_       				=			resample if resample is not None else self.resample
		UpperCAmelCase_       				=			do_rescale if do_rescale is not None else self.do_rescale
		UpperCAmelCase_       				=			rescale_factor if rescale_factor is not None else self.rescale_factor
		UpperCAmelCase_       				=			do_normalize if do_normalize is not None else self.do_normalize
		UpperCAmelCase_       				=			image_mean if image_mean is not None else self.image_mean
		UpperCAmelCase_       				=			image_std if image_std is not None else self.image_std
		UpperCAmelCase_       				=			apply_ocr if apply_ocr is not None else self.apply_ocr
		UpperCAmelCase_       				=			ocr_lang if ocr_lang is not None else self.ocr_lang
		UpperCAmelCase_       				=			tesseract_config if tesseract_config is not None else self.tesseract_config

		UpperCAmelCase_       				=			make_list_of_images(lowerCAmelCase							)

		if not valid_images(lowerCAmelCase							):
			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.
		UpperCAmelCase_       				=			[to_numpy_array(lowerCAmelCase							) for image in images]

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

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

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

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

		UpperCAmelCase_       				=			[to_channel_dimension_format(lowerCAmelCase		,	lowerCAmelCase							) for image in images]

		UpperCAmelCase_       				=			BatchFeature(data={"pixel_values": images}		,	tensor_type=lowerCAmelCase							)

		if apply_ocr:
			UpperCAmelCase_       				=			words_batch
			UpperCAmelCase_       				=			boxes_batch
		return data


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a:    int 							=			logging.getLogger(__name__)



def       __lowerCAmelCase      (						A       ,     A    ):
	return (preds == labels).mean()

@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,     )


@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()						)}						)
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'Should contain the data files for the task.'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=1_28     ,     metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }     ,     )
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}						)



def       __lowerCAmelCase      (						):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	UpperCAmelCase_       				=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir    )
	    and os.listdir(training_args.output_dir    )
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
		    " --overwrite_output_dir to overcome."    )

	# Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,     datefmt="%m/%d/%Y %H:%M:%S"       ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,     )
	logger.warning(
	    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"       ,     training_args.local_rank       ,     training_args.device       ,     training_args.n_gpu       ,     bool(training_args.local_rank != -1    )       ,     training_args.fpaa       ,     )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank    ):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"       ,     A    )

	# Set seed
	set_seed(training_args.seed    )

	try:
		UpperCAmelCase_       				=			processors[data_args.task_name]()
		UpperCAmelCase_       				=			processor.get_labels()
		UpperCAmelCase_       				=			len(A    )
	except KeyError:
		raise ValueError("Task not found: %s" % (data_args.task_name)    )

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	UpperCAmelCase_       				=			AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,     num_labels=A       ,     finetuning_task=data_args.task_name       ,     cache_dir=model_args.cache_dir       ,     )
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,     cache_dir=model_args.cache_dir       ,     )
	UpperCAmelCase_       				=			AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path       ,     from_tf=bool(".ckpt" in model_args.model_name_or_path    )       ,     config=A       ,     cache_dir=model_args.cache_dir       ,     )

	# Get datasets
	UpperCAmelCase_       				=			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir       ,     tokenizer=A       ,     task=data_args.task_name       ,     max_seq_length=data_args.max_seq_length       ,     overwrite_cache=data_args.overwrite_cache       ,     mode=Split.train       ,     )
	    if training_args.do_train
	    else None
	)
	UpperCAmelCase_       				=			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir       ,     tokenizer=A       ,     task=data_args.task_name       ,     max_seq_length=data_args.max_seq_length       ,     overwrite_cache=data_args.overwrite_cache       ,     mode=Split.dev       ,     )
	    if training_args.do_eval
	    else None
	)

	def compute_metrics(A    ) -> Dict:
		UpperCAmelCase_       				=			np.argmax(p.predictions       ,     axis=1    )
		return {"acc": simple_accuracy(A       ,     p.label_ids    )}

	# Data collator
	UpperCAmelCase_       				=			DataCollatorWithPadding(A       ,     pad_to_multiple_of=8    ) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCAmelCase_       				=			Trainer(
	    model=A       ,     args=A       ,     train_dataset=A       ,     eval_dataset=A       ,     compute_metrics=A       ,     data_collator=A       ,     )

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
	UpperCAmelCase_       				=			{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"    )

		UpperCAmelCase_       				=			trainer.evaluate()

		UpperCAmelCase_       				=			os.path.join(training_args.output_dir       ,     "eval_results.txt"    )
		if trainer.is_world_master():
			with open(A       ,     "w"    ) as writer:
				logger.info("***** Eval results *****"    )
				for key, value in result.items():
					logger.info("  %s = %s"       ,     A       ,     A    )
					writer.write("%s = %s\n" % (key, value)    )

				results.update(A    )

	return results



def       __lowerCAmelCase      (						A    ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()