

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

lowerCAmelCase__	   = logging.get_logger(__name__)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Tuple:




				'''simple docstring'''





				return [
				    int(1_0_0_0 * (box[0] / width)      ),
				    int(1_0_0_0 * (box[1] / height)      ),
				    int(1_0_0_0 * (box[2] / width)      ),
				    int(1_0_0_0 * (box[3] / height)      ),
				]



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							np.ndarray	,       SCREAMING_SNAKE_CASE_:							Optional[str]	,       SCREAMING_SNAKE_CASE_:							Optional[str] = None      )			->      List[Any]:




				'''simple docstring'''





				A__        =			tesseract_config if tesseract_config is not None else ""

				# apply OCR
				A__        =			to_pil_image(SCREAMING_SNAKE_CASE_      )
				A__    ,				A__        =			pil_image.size
				A__        =			pytesseract.image_to_data(SCREAMING_SNAKE_CASE_	,       lang=SCREAMING_SNAKE_CASE_	,       output_type="dict"	,       config=SCREAMING_SNAKE_CASE_      )
				A__    ,				A__    ,				A__    ,				A__    ,				A__        =			data["text"], data["left"], data["top"], data["width"], data["height"]

				# filter empty words and corresponding coordinates
				A__        =			[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_      ) if not word.strip()]
				A__        =			[word for idx, word in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[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
				A__        =			[]
				for x, y, w, h in zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			[x, y, x + w, y + h]
								actual_boxes.append(SCREAMING_SNAKE_CASE_      )

				# finally, normalize the bounding boxes
				A__        =			[]
				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__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       ['pixel_values']


			def __init__(    self      ,      lowercase = True      ,      lowercase = None      ,      lowercase = PILImageResampling.BILINEAR      ,      lowercase = True      ,      lowercase = None      ,      lowercase = ""      ,      **lowercase      ,      )  ->						None:
							'''simple docstring'''






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

							A__        =			do_resize
							A__        =			size
							A__        =			resample
							A__        =			apply_ocr
							A__        =			ocr_lang
							A__        =			tesseract_config


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = PILImageResampling.BILINEAR      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			get_size_dict(lowercase       )
							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()}'       )
							A__        =			(size["height"], size["width"])
							return resize(lowercase      ,      size=lowercase      ,      resample=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = ChannelDimension.FIRST      ,      **lowercase      ,      )  ->						PIL.Image.Image:
							'''simple docstring'''






							A__        =			do_resize if do_resize is not None else self.do_resize
							A__        =			size if size is not None else self.size
							A__        =			get_size_dict(lowercase       )
							A__        =			resample if resample is not None else self.resample
							A__        =			apply_ocr if apply_ocr is not None else self.apply_ocr
							A__        =			ocr_lang if ocr_lang is not None else self.ocr_lang
							A__        =			tesseract_config if tesseract_config is not None else self.tesseract_config

							A__        =			make_list_of_images(lowercase       )

							if not valid_images(lowercase       ):
											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."       )

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

							if apply_ocr:
											requires_backends(self      ,      "pytesseract"       )
											A__        =			[]
											A__        =			[]
											for image in images:
															A__    ,				A__        =			apply_tesseract(lowercase      ,      lowercase      ,      lowercase       )
															words_batch.append(lowercase       )
															boxes_batch.append(lowercase       )

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							A__        =			[flip_channel_order(lowercase       ) for image in images]
							A__        =			[to_channel_dimension_format(lowercase      ,      lowercase       ) for image in images]

							A__        =			BatchFeature(data={"pixel_values": images}      ,      tensor_type=lowercase       )

							if apply_ocr:
											A__        =			words_batch
											A__        =			boxes_batch
							return data




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    metadata={'help': 'The output directory where the model will be written.'}							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The encoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train an encoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The decoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train a decoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}			)



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			HfArgumentParser((ModelArguments,)      )
				((A__)    ,				)        =			parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_config_name      )
				# Use pretrained encoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_config_name      )
				# Use pretrained decoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path      )

				# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
				A__        =			True
				A__        =			True

				A__        =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
				    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path	,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path	,       encoder_config=SCREAMING_SNAKE_CASE_	,       decoder_config=SCREAMING_SNAKE_CASE_	,       )

				# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
				A__        =			decoder_config.decoder_start_token_id
				A__        =			decoder_config.pad_token_id
				if decoder_start_token_id is None:
								A__        =			decoder_config.bos_token_id
				if pad_token_id is None:
								A__        =			decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				A__        =			decoder_config.eos_token_id
				A__        =			decoder_start_token_id
				A__        =			pad_token_id

				A__        =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

				A__        =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
				A__        =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id      )

				model.save_pretrained(model_args.output_dir      )
				image_processor.save_pretrained(model_args.output_dir      )
				tokenizer.save_pretrained(model_args.output_dir      )


if __name__ == "__main__":
	main()

