



from collections.abc import Generator
from math import sin






def      _A				(UpperCamelCase		:      bytes )  ->bytes:



			'''simple docstring'''







			if len(UpperCamelCase ) != 32:
						raise ValueError("""Input must be of length 32""" )

			lowerCamelCase__ :      Union[str, Any]								=		B""""""
			for i in [3, 2, 1, 0]:
						little_endian += string_aa[8 * i : 8 * i + 8]
			return little_endian






def      _A				(UpperCamelCase		:      int )  ->bytes:



			'''simple docstring'''







			if i < 0:
						raise ValueError("""Input must be non-negative""" )

			lowerCamelCase__ :      Optional[Any]								=		format(UpperCamelCase      ,			"""08x""" )[-8:]
			lowerCamelCase__ :      Optional[Any]								=		B""""""
			for i in [3, 2, 1, 0]:
						little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
			return little_endian_hex






def      _A				(UpperCamelCase		:      bytes )  ->bytes:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		B""""""
			for char in message:
						bit_string += format(UpperCamelCase      ,			"""08b""" ).encode("""utf-8""" )
			lowerCamelCase__ :      Optional[Any]								=		format(len(UpperCamelCase )      ,			"""064b""" ).encode("""utf-8""" )

			# Pad bit_string to a multiple of 512 chars
			bit_string += b"1"
			while len(UpperCamelCase ) % 512 != 448:
						bit_string += b"0"
			bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

			return bit_string






def      _A				(UpperCamelCase		:      bytes )  ->Generator[list[int], None, None]:



			'''simple docstring'''







			if len(UpperCamelCase ) % 512 != 0:
						raise ValueError("""Input must have length that's a multiple of 512""" )

			for pos in range(0      ,			len(UpperCamelCase )      ,			512 ):
						lowerCamelCase__ :      List[str]								=		bit_string[pos : pos + 512]
						lowerCamelCase__ :      Tuple								=		[]
						for i in range(0      ,			512      ,			32 ):
									block_words.append(int(to_little_endian(block[i : i + 32] )      ,			2 ) )
						yield block_words






def      _A				(UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			if i < 0:
						raise ValueError("""Input must be non-negative""" )

			lowerCamelCase__ :      List[str]								=		format(UpperCamelCase      ,			"""032b""" )
			lowerCamelCase__ :      int								=		""""""
			for c in i_str:
						new_str += "1" if c == "0" else "0"
			return int(UpperCamelCase      ,			2 )






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			return (a + b) % 2**32






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			if i < 0:
						raise ValueError("""Input must be non-negative""" )
			if shift < 0:
						raise ValueError("""Shift must be non-negative""" )
			return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def      _A				(UpperCamelCase		:      bytes )  ->bytes:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		preprocess(UpperCamelCase )

			lowerCamelCase__ :      Tuple								=		[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

			# Starting states
			lowerCamelCase__ :      Union[str, Any]								=		0x6745_2301
			lowerCamelCase__ :      Tuple								=		0xEFCD_AB89
			lowerCamelCase__ :      int								=		0x98BA_DCFE
			lowerCamelCase__ :      Dict								=		0x1032_5476

			lowerCamelCase__ :      str								=		[
			    7,
			    12,
			    17,
			    22,
			    7,
			    12,
			    17,
			    22,
			    7,
			    12,
			    17,
			    22,
			    7,
			    12,
			    17,
			    22,
			    5,
			    9,
			    14,
			    20,
			    5,
			    9,
			    14,
			    20,
			    5,
			    9,
			    14,
			    20,
			    5,
			    9,
			    14,
			    20,
			    4,
			    11,
			    16,
			    23,
			    4,
			    11,
			    16,
			    23,
			    4,
			    11,
			    16,
			    23,
			    4,
			    11,
			    16,
			    23,
			    6,
			    10,
			    15,
			    21,
			    6,
			    10,
			    15,
			    21,
			    6,
			    10,
			    15,
			    21,
			    6,
			    10,
			    15,
			    21,
			]

			# Process bit string in chunks, each with 16 32-char words
			for block_words in get_block_words(UpperCamelCase ):
						lowerCamelCase__ :      Optional[int]								=		aa
						lowerCamelCase__ :      Any								=		ba
						lowerCamelCase__ :      List[Any]								=		ca
						lowerCamelCase__ :      int								=		da

						# Hash current chunk
						for i in range(64 ):
									if i <= 15:
												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
												lowerCamelCase__ :      Optional[int]								=		d ^ (b & (c ^ d))
												lowerCamelCase__ :      Optional[int]								=		i
									elif i <= 31:
												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
												lowerCamelCase__ :      Dict								=		c ^ (d & (b ^ c))
												lowerCamelCase__ :      int								=		(5 * i + 1) % 16
									elif i <= 47:
												lowerCamelCase__ :      Union[str, Any]								=		b ^ c ^ d
												lowerCamelCase__ :      Union[str, Any]								=		(3 * i + 5) % 16
									else:
												lowerCamelCase__ :      int								=		c ^ (b | not_aa(UpperCamelCase ))
												lowerCamelCase__ :      Optional[Any]								=		(7 * i) % 16
									lowerCamelCase__ :      Optional[int]								=		(f + a + added_consts[i] + block_words[g]) % 2**32
									lowerCamelCase__ :      List[Any]								=		d
									lowerCamelCase__ :      int								=		c
									lowerCamelCase__ :      str								=		b
									lowerCamelCase__ :      str								=		sum_aa(UpperCamelCase      ,			left_rotate_aa(UpperCamelCase      ,			shift_amounts[i] ) )

						# Add hashed chunk to running total
						lowerCamelCase__ :      Dict								=		sum_aa(UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      List[Any]								=		sum_aa(UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      int								=		sum_aa(UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      Dict								=		sum_aa(UpperCamelCase      ,			UpperCamelCase )

			lowerCamelCase__ :      Union[str, Any]								=		reformat_hex(UpperCamelCase ) + reformat_hex(UpperCamelCase ) + reformat_hex(UpperCamelCase ) + reformat_hex(UpperCamelCase )
			return digest


if __name__ == "__main__":
				import doctest

				doctest.testmod()








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}" )
			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						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 T5 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.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



