



"""simple docstring"""

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

a_			   =					logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  np.ndarray				,    SCREAMING_SNAKE_CASE__ :  Optional[str]				,    SCREAMING_SNAKE_CASE__ :  Optional[str] = None			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					tesseract_config if tesseract_config is not None else """"""

					# apply OCR
					snake_case_					:		Optional[int] 					=					to_pil_image(SCREAMING_SNAKE_CASE__			)
					snake_case_					,			snake_case_					:		Union[str, Any] 					=					pil_image.size
					snake_case_					:		Optional[Any] 					=					pytesseract.image_to_data(SCREAMING_SNAKE_CASE__				,    lang=SCREAMING_SNAKE_CASE__				,    output_type="""dict"""				,    config=SCREAMING_SNAKE_CASE__			)
					snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Dict 					=					data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

					# filter empty words and corresponding coordinates
					snake_case_					:		List[Any] 					=					[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE__			) if not word.strip()]
					snake_case_					:		Any 					=					[word for idx, word in enumerate(SCREAMING_SNAKE_CASE__			) if idx not in irrelevant_indices]
					snake_case_					:		Optional[int] 					=					[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__			) if idx not in irrelevant_indices]
					snake_case_					:		List[Any] 					=					[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__			) if idx not in irrelevant_indices]
					snake_case_					:		Any 					=					[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__			) if idx not in irrelevant_indices]
					snake_case_					:		List[Any] 					=					[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
					snake_case_					:		List[Any] 					=					[]
					for x, y, w, h in zip(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		Tuple 					=					[x, y, x + w, y + h]
										actual_boxes.append(SCREAMING_SNAKE_CASE__			)

					# finally, normalize the bounding boxes
					snake_case_					:		Union[str, Any] 					=					[]
					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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							int							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BILINEAR   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = ""   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		List[Any] 					=					size if size is not None else {"""height""": 2_24, """width""": 2_24}
											snake_case_					:		Any 					=					get_size_dict(lowercase__       )

											snake_case_					:		Optional[int] 					=					do_resize
											snake_case_					:		Any 					=					size
											snake_case_					:		Optional[int] 					=					resample
											snake_case_					:		Optional[int] 					=					apply_ocr
											snake_case_					:		List[Any] 					=					ocr_lang
											snake_case_					:		Tuple 					=					tesseract_config







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = PILImageResampling.BILINEAR   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		int 					=					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()}'       )
											snake_case_					:		Dict 					=					(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__   ,		):
											snake_case_					:		str 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		List[str] 					=					size if size is not None else self.size
											snake_case_					:		List[str] 					=					get_size_dict(lowercase__       )
											snake_case_					:		str 					=					resample if resample is not None else self.resample
											snake_case_					:		Any 					=					apply_ocr if apply_ocr is not None else self.apply_ocr
											snake_case_					:		str 					=					ocr_lang if ocr_lang is not None else self.ocr_lang
											snake_case_					:		str 					=					tesseract_config if tesseract_config is not None else self.tesseract_config

											snake_case_					:		List[str] 					=					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.
											snake_case_					:		str 					=					[to_numpy_array(lowercase__       ) for image in images]

											if apply_ocr:
																requires_backends(self   ,		"""pytesseract"""       )
																snake_case_					:		Tuple 					=					[]
																snake_case_					:		Tuple 					=					[]
																for image in images:
																					snake_case_					,			snake_case_					:		Optional[int] 					=					apply_tesseract(lowercase__   ,		lowercase__   ,		lowercase__       )
																					words_batch.append(lowercase__       )
																					boxes_batch.append(lowercase__       )

											if do_resize:
																snake_case_					:		Union[str, Any] 					=					[self.resize(image=lowercase__   ,		size=lowercase__   ,		resample=lowercase__       ) for image in images]

											# flip color channels from RGB to BGR (as Detectron2 requires this)
											snake_case_					:		Any 					=					[flip_channel_order(lowercase__       ) for image in images]
											snake_case_					:		int 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		str 					=					BatchFeature(data={"""pixel_values""": images}   ,		tensor_type=lowercase__       )

											if apply_ocr:
																snake_case_					:		List[str] 					=					words_batch
																snake_case_					:		Tuple 					=					boxes_batch
											return data









"""simple docstring"""

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					data
											snake_case_					:		List[str] 					=					[0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]







						@staticmethod
						def 						__UpperCamelCase							(lowercase__   ,		lowercase__       ):
											return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					B"""\x80""" + B"""\x00""" * (63 - (len(self.data       ) + 8) % 64)
											snake_case_					:		Tuple 					=					self.data + padding + struct.pack(""">Q"""   ,		8 * len(self.data       )       )
											return padded_data







						def 						__UpperCamelCase							(self       ):
											return [
											    self.padded_data[i : i + 64] for i in range(0   ,		len(self.padded_data       )   ,		64       )
											]







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					list(struct.unpack(""">16L"""   ,		lowercase__       )       ) + [0] * 64
											for i in range(16   ,		80       ):
																snake_case_					:		Dict 					=					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])   ,		1       )
											return w







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.padding()
											snake_case_					:		Any 					=					self.split_blocks()
											for block in self.blocks:
																snake_case_					:		Any 					=					self.expand_block(lowercase__       )
																snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[Any] 					=					self.h
																for i in range(0   ,		80       ):
																					if 0 <= i < 20:
																										snake_case_					:		Optional[Any] 					=					(b & c) | ((~b) & d)
																										snake_case_					:		List[str] 					=					0X5_a_8_2_7_9_9_9
																					elif 20 <= i < 40:
																										snake_case_					:		Union[str, Any] 					=					b ^ c ^ d
																										snake_case_					:		Tuple 					=					0X6_e_d_9_e_b_a_1
																					elif 40 <= i < 60:
																										snake_case_					:		str 					=					(b & c) | (b & d) | (c & d)
																										snake_case_					:		List[str] 					=					0X8_f_1_b_b_c_d_c
																					elif 60 <= i < 80:
																										snake_case_					:		Tuple 					=					b ^ c ^ d
																										snake_case_					:		str 					=					0Xc_a_6_2_c_1_d_6
																					snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Optional[Any] 					=					(
																					    self.rotate(lowercase__   ,		5       ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
																					    a,
																					    self.rotate(lowercase__   ,		30       ),
																					    c,
																					    d,
																					)
																snake_case_					:		Any 					=					(
																    self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
																    self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
																    self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
																    self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
																    self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
																)
											return ("{:08x}" * 5).format(*self.h       )







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					b"""Test String"""
					assert SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__			).hexdigest()  # noqa: S324







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		int 					=					argparse.ArgumentParser(description="""Process some strings or files"""			)
					parser.add_argument(
					    """--string"""				,    dest="""input_string"""				,    default="""Hello World!! Welcome to Cryptography"""				,    help="""Hash the string"""				,    )
					parser.add_argument("""--file"""				,    dest="""input_file"""				,    help="""Hash contents of a file"""			)
					snake_case_					:		Optional[int] 					=					parser.parse_args()
					snake_case_					:		Optional[int] 					=					args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file				,    """rb"""			) as f:
															snake_case_					:		List[str] 					=					f.read()
					else:
										snake_case_					:		Dict 					=					bytes(SCREAMING_SNAKE_CASE__				,    """utf-8"""			)
					print(SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash()			)


if __name__ == "__main__":
			main()
			import doctest

			doctest.testmod()




