





"""simple docstring"""






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



class 					__UpperCAmelCase							:
				def __init__(		self    :			List[Any]	, a_    :			Dict    )	->	str:



							'''simple docstring'''



							a__  :	Dict		 =			data
							a__  :	List[Any]		 =			[0x67452301, 0xefcdab89, 0x98badcfe, 0x10325476, 0xc3d2e1f0]
				@staticmethod
				def       UpperCAmelCase      (		a_    :			Tuple	, a_    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							return ((n << b) | (n >> (32 - b))) & 0xffffffff
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	int:



							'''simple docstring'''



							a__  :	List[str]		 =			B"\x80" + B"\x00" * (63 - (len(self.data    ) + 8) % 64)
							a__  :	Tuple		 =			self.data + padding + struct.pack(">Q"	, 8 * len(self.data    )    )
							return padded_data
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							return [
							    self.padded_data[i : i + 64] for i in range(0	, len(self.padded_data    )	, 64    )
							]
				def       UpperCAmelCase      (		self    :			Any	, a_    :			List[str]    )	->	Any:



							'''simple docstring'''



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






				def       UpperCAmelCase      (		self    :			Tuple    )	->	Any:



							'''simple docstring'''



							a__  :	str		 =			self.padding()
							a__  :	Union[str, Any]		 =			self.split_blocks()
							for block in self.blocks:
										a__  :	Optional[Any]		 =			self.expand_block(a_    )
										a__ ,	a__ ,	a__ ,	a__ ,	a__  :	str		 =			self.h
										for i in range(0	, 80    ):
													if 0 <= i < 20:
																a__  :	Dict		 =			(b & c) | ((~b) & d)
																a__  :	List[str]		 =			0x5a827999
													elif 20 <= i < 40:
																a__  :	str		 =			b ^ c ^ d
																a__  :	Optional[int]		 =			0x6ed9eba1
													elif 40 <= i < 60:
																a__  :	Optional[Any]		 =			(b & c) | (b & d) | (c & d)
																a__  :	Any		 =			0x8f1bbcdc
													elif 60 <= i < 80:
																a__  :	Tuple		 =			b ^ c ^ d
																a__  :	Any		 =			0xca62c1d6
													a__ ,	a__ ,	a__ ,	a__ ,	a__  :	Union[str, Any]		 =			(
													    self.rotate(a_	, 5    ) + f + e + k + expanded_block[i] & 0xffffffff,
													    a,
													    self.rotate(a_	, 30    ),
													    c,
													    d,
													)
										a__  :	Optional[Any]		 =			(
										    self.h[0] + a & 0xffffffff,
										    self.h[1] + b & 0xffffffff,
										    self.h[2] + c & 0xffffffff,
										    self.h[3] + d & 0xffffffff,
										    self.h[4] + e & 0xffffffff,
										)
							return ("{:08x}" * 5).format(*self.h    )






def      lowercase__ (					)    ->			Dict:



			'''simple docstring'''
			a__  :	str		 =			b"Test String"
			assert SHAaHash(lowerCAmelCase__			).final_hash() == hashlib.shaa(lowerCAmelCase__			).hexdigest()  # noqa: S324






def      lowercase__ (					)    ->			Dict:



			'''simple docstring'''
			a__  :	Optional[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"			)
			a__  :	Tuple		 =			parser.parse_args()
			a__  :	Union[str, Any]		 =			args.input_string
			# In any case hash input should be a bytestring
			if args.input_file:
						with open(args.input_file							,			"rb"			) as f:
									a__  :	List[Any]		 =			f.read()
			else:
						a__  :	Union[str, Any]		 =			bytes(lowerCAmelCase__							,			"utf-8"			)
			print(SHAaHash(lowerCAmelCase__			).final_hash()			)


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

		doctest.testmod()






"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	str		 =			["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

							a__  :	int		 =			os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["vocab_file"]    )
							with open(self.vocab_file	, "w"	, encoding="utf-8"    ) as fp:
										fp.write(json.dumps(a_    ) + "\n"    )

							a__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			os.path.join(self.tmpdirname	, a_    )
							with open(self.image_processor_file	, "w"	, encoding="utf-8"    ) as fp:
										json.dump(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

							a__  :	List[str]		 =			Image.fromarray(np.moveaxis(a_	, 0	, -1    )    )

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_tokenizer()
							a__  :	Any		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			MgpstrProcessor.from_pretrained(self.tmpdirname	, use_fast=a_    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_tokenizer()
							a__  :	Tuple		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=a_	, padding_value=1.0    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.get_image_processor()
							a__  :	Union[str, Any]		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			processor(images=a_	, return_tensors="np"    )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()	, input_processor[key].sum()	, delta=1E-2    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_image_processor()
							a__  :	List[Any]		 =			self.get_tokenizer()

							a__  :	int		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			"test"
							a__  :	str		 =			self.prepare_image_inputs()

							a__  :	Any		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, ["pixel_values", "labels"]    )

							# test if it raises when no input is passed
							with pytest.raises(a_    ):
										processor()
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

							a__  :	Dict		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	Optional[Any]		 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a__  :	Any		 =			processor.char_decode(a_    )
							a__  :	str		 =			tokenizer.batch_decode(a_    )
							a__  :	Union[str, Any]		 =			[seq.replace(" "	, ""    ) for seq in decoded_tok]

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.get_image_processor()
							a__  :	Any		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, processor.model_input_names    )






				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

							self.assertListEqual(list(results.keys()    )	, ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )