

"""simple docstring"""





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()




def   _A  (  UpperCamelCase_     :       List[Any],			UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       int,			UpperCamelCase_     :       List[str])	->						Any:
   '''simple docstring'''


   __lowercase								=						FunnelConfig.from_json_file(UpperCamelCase_)
   print(F"""Building PyTorch model from configuration: {config}""")
   __lowercase								=						FunnelBaseModel(UpperCamelCase_) if base_model else FunnelModel(UpperCamelCase_)

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict(),			UpperCamelCase_)


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--base_model', action='store_true', help='Whether you want just the base model (no decoder) or not.'
   )
   _a       =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )










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		:				List[str]       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Dict,  A		:					int,  A		:					Optional[int]   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					np.ndarray,  A		:					Optional[str],  A		:					Optional[str] = None   ):






			'''simple docstring'''







			a    =			tesseract_config if tesseract_config is not None else ""

			# apply OCR
			a    =			to_pil_image(A   )
			a    ,		a    =			pil_image.size
			a    =			pytesseract.image_to_data(A,  lang=A,  output_type="dict",  config=A   )
			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(A   ) if not word.strip()]
			a    =			[word for idx, word in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]

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

			# finally, normalize the bounding boxes
			a    =			[]
			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     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					["""pixel_values"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = ""					,      **__lowerCamelCase				:     Tuple					,      )					->     None:
							super().__init__(**__lowerCamelCase							)
							a    =			size if size is not None else {"height": 2_24, "width": 2_24}
							a    =			get_size_dict(__lowerCamelCase							)

							a    =			do_resize
							a    =			size
							a    =			resample
							a    =			apply_ocr
							a    =			ocr_lang
							a    =			tesseract_config





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     np.ndarray					,      __lowerCamelCase				:     Dict[str, int]					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     np.ndarray:
							a    =			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()}"""							)
							a    =			(size["height"], size["width"])
							return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     ImageInput					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = None					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase				:     ChannelDimension = ChannelDimension.FIRST					,      **__lowerCamelCase				:     Optional[Any]					,      )					->     PIL.Image.Image:
							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(__lowerCamelCase							)
							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(__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."							)

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

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

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

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

							a    =			BatchFeature(data={"pixel_values": images}					,      tensor_type=__lowerCamelCase							)

							if apply_ocr:
										a    =			words_batch
										a    =			boxes_batch
							return data






