





"""simple docstring"""





import argparse
import struct
import unittest
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




				def __init__(  self	:Tuple    ,     __lowercase	:bytes      ):
											__lowerCamelCase   :   str       =data

											# Initialize hash values
											__lowerCamelCase   :   List[Any]       =[
											    0X6a_09e_667,
											    0Xbb_67a_e85,
											    0X3c_6ef_372,
											    0Xa5_4ff_53a,
											    0X51_0e5_27f,
											    0X9b_056_88c,
											    0X1f_83d_9ab,
											    0X5b_e0c_d19,
											]

											# Initialize round constants
											__lowerCamelCase   :   Dict       =[
											    0X42_8a2_f98,
											    0X71_374_491,
											    0Xb5_c0f_bcf,
											    0Xe9_b5d_ba5,
											    0X39_56c_25b,
											    0X59_f11_1f1,
											    0X92_3f8_2a4,
											    0Xab_1c5_ed5,
											    0Xd8_07a_a98,
											    0X12_835_b01,
											    0X24_318_5be,
											    0X55_0c7_dc3,
											    0X72_be5_d74,
											    0X80_deb_1fe,
											    0X9b_dc0_6a7,
											    0Xc1_9bf_174,
											    0Xe4_9b6_9c1,
											    0Xef_be4_786,
											    0X0f_c19_dc6,
											    0X24_0ca_1cc,
											    0X2d_e92_c6f,
											    0X4a_748_4aa,
											    0X5c_b0a_9dc,
											    0X76_f98_8da,
											    0X98_3e5_152,
											    0Xa8_31c_66d,
											    0Xb0_032_7c8,
											    0Xbf_597_fc7,
											    0Xc6_e00_bf3,
											    0Xd5_a79_147,
											    0X06_ca6_351,
											    0X14_292_967,
											    0X27_b70_a85,
											    0X2e_1b2_138,
											    0X4d_2c6_dfc,
											    0X53_380_d13,
											    0X65_0a7_354,
											    0X76_6a0_abb,
											    0X81_c2c_92e,
											    0X92_722_c85,
											    0Xa2_bfe_8a1,
											    0Xa8_1a6_64b,
											    0Xc2_4b8_b70,
											    0Xc7_6c5_1a3,
											    0Xd1_92e_819,
											    0Xd6_990_624,
											    0Xf4_0e3_585,
											    0X10_6aa_070,
											    0X19_a4c_116,
											    0X1e_376_c08,
											    0X27_487_74c,
											    0X34_b0b_cb5,
											    0X39_1c0_cb3,
											    0X4e_d8a_a4a,
											    0X5b_9cc_a4f,
											    0X68_2e6_ff3,
											    0X74_8f8_2ee,
											    0X78_a56_36f,
											    0X84_c87_814,
											    0X8c_c70_208,
											    0X90_bef_ffa,
											    0Xa4_506_ceb,
											    0Xbe_f9a_3f7,
											    0Xc6_717_8f2,
											]

											__lowerCamelCase   :   Any       =self.preprocessing(self.data      )
											self.final_hash()




				@staticmethod
				def        __lowercase	(  __lowercase	:bytes      ):
											__lowerCamelCase   :   Any       =B'''\x80''' + (B'''\x00''' * (63 - (len(__lowercase      ) + 8) % 64))
											__lowerCamelCase   :   List[Any]       =struct.pack('''>Q'''    ,     (len(__lowercase      ) * 8)      )
											return data + padding + big_endian_integer




				def        __lowercase	(  self	:Optional[Any]      ):
											# Convert into blocks of 64 bytes
											__lowerCamelCase   :   Tuple       =[
											    self.preprocessed_data[x : x + 64]
											    for x in range(0    ,     len(self.preprocessed_data      )    ,     64      )
											]

											for block in self.blocks:
																		# Convert the given block into a list of 4 byte integers
																		__lowerCamelCase   :   Union[str, Any]       =list(struct.unpack('''>16L'''    ,     __lowercase      )      )
																		# add 48 0-ed integers
																		words += [0] * 48

																		__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =self.hashes

																		for index in range(0    ,     64      ):
																									if index > 15:
																																# modify the zero-ed indexes at the end of the array
																																__lowerCamelCase   :   List[str]       =(
																																    self.ror(words[index - 15]    ,     7      )
																																    ^ self.ror(words[index - 15]    ,     18      )
																																    ^ (words[index - 15] >> 3)
																																)
																																__lowerCamelCase   :   int       =(
																																    self.ror(words[index - 2]    ,     17      )
																																    ^ self.ror(words[index - 2]    ,     19      )
																																    ^ (words[index - 2] >> 10)
																																)

																																__lowerCamelCase   :   Any       =(
																																    words[index - 16] + sa + words[index - 7] + sa
																																) % 0X100_000_000

																									# Compression
																									__lowerCamelCase   :   Union[str, Any]       =self.ror(__lowercase    ,     6      ) ^ self.ror(__lowercase    ,     11      ) ^ self.ror(__lowercase    ,     25      )
																									__lowerCamelCase   :   Tuple       =(e & f) ^ ((~e & 0Xff_fff_fff) & g)
																									__lowerCamelCase   :   Tuple       =(
																									    h + sa + ch + self.round_constants[index] + words[index]
																									) % 0X100_000_000
																									__lowerCamelCase   :   Optional[int]       =self.ror(__lowercase    ,     2      ) ^ self.ror(__lowercase    ,     13      ) ^ self.ror(__lowercase    ,     22      )
																									__lowerCamelCase   :   List[str]       =(a & b) ^ (a & c) ^ (b & c)
																									__lowerCamelCase   :   str       =(sa + maj) % 0X100_000_000

																									__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Dict       =(
																									    g,
																									    f,
																									    e,
																									    ((d + tempa) % 0X100_000_000),
																									    c,
																									    b,
																									    a,
																									    ((tempa + tempa) % 0X100_000_000),
																									)

																		__lowerCamelCase   :   str       =[a, b, c, d, e, f, g, h]

																		# Modify final values
																		__lowerCamelCase   :   List[Any]       =[
																		    ((element + mutated_hash_values[index]) % 0X100_000_000)
																		    for index, element in enumerate(self.hashes      )
																		]

											__lowerCamelCase   :   Union[str, Any]       =''''''.join([hex(__lowercase      )[2:].zfill(8      ) for value in self.hashes]      )




				def        __lowercase	(  self	:str    ,     __lowercase	:int    ,     __lowercase	:int      ):
											return 0Xff_fff_fff & (value << (32 - rotations)) | (value >> rotations)

class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Tuple      ):
											import hashlib

											__lowerCamelCase   :   str       =bytes('''Test String'''    ,     '''utf-8'''      )
											self.assertEqual(SHAaaa(__lowercase      ).hash    ,     hashlib.shaaaa(__lowercase      ).hexdigest()      )



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							import doctest

							doctest.testmod()

							__lowerCamelCase   :   str       =argparse.ArgumentParser()
							parser.add_argument(
							    '''-s'''     ,    '''--string'''     ,    dest='''input_string'''     ,    default='''Hello World!! Welcome to Cryptography'''     ,    help='''Hash the string'''     ,    )
							parser.add_argument(
							    '''-f'''     ,    '''--file'''     ,    dest='''input_file'''     ,    help='''Hash contents of a file''' )

							__lowerCamelCase   :   Optional[Any]       =parser.parse_args()

							__lowerCamelCase   :   Any       =args.input_string

							# hash input should be a bytestring
							if args.input_file:
														with open(args.input_file     ,    '''rb''' ) as f:
																					__lowerCamelCase   :   str       =f.read()
							else:
														__lowerCamelCase   :   int       =bytes(SCREAMING_SNAKE_CASE     ,    '''utf-8''' )

							print(SHAaaa(SCREAMING_SNAKE_CASE ).hash )


if __name__ == "__main__":
							main()











"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Tuple									=		"""marian"""
				__snake_case :							Any									=		["""past_key_values"""]
				__snake_case :							Optional[Any]									=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self	:List[Any]    ,     __lowercase	:Any=5_8101    ,     __lowercase	:Tuple=None    ,     __lowercase	:Union[str, Any]=1024    ,     __lowercase	:Dict=12    ,     __lowercase	:int=4096    ,     __lowercase	:int=16    ,     __lowercase	:List[Any]=12    ,     __lowercase	:Dict=4096    ,     __lowercase	:Dict=16    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:List[Any]=True    ,     __lowercase	:int=True    ,     __lowercase	:Tuple="gelu"    ,     __lowercase	:str=1024    ,     __lowercase	:Optional[int]=0.1    ,     __lowercase	:List[str]=0.0    ,     __lowercase	:Union[str, Any]=0.0    ,     __lowercase	:Dict=0.02    ,     __lowercase	:Tuple=5_8100    ,     __lowercase	:Optional[Any]=False    ,     __lowercase	:int=5_8100    ,     __lowercase	:Any=0    ,     __lowercase	:str=0    ,     __lowercase	:str=True    ,     **__lowercase	:Any    ,     ):
											__lowerCamelCase   :   List[Any]       =vocab_size
											__lowerCamelCase   :   Optional[int]       =decoder_vocab_size or vocab_size
											__lowerCamelCase   :   Tuple       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =d_model
											__lowerCamelCase   :   Any       =encoder_ffn_dim
											__lowerCamelCase   :   str       =encoder_layers
											__lowerCamelCase   :   List[str]       =encoder_attention_heads
											__lowerCamelCase   :   str       =decoder_ffn_dim
											__lowerCamelCase   :   Tuple       =decoder_layers
											__lowerCamelCase   :   Any       =decoder_attention_heads
											__lowerCamelCase   :   List[Any]       =dropout
											__lowerCamelCase   :   Any       =attention_dropout
											__lowerCamelCase   :   Union[str, Any]       =activation_dropout
											__lowerCamelCase   :   Optional[int]       =activation_function
											__lowerCamelCase   :   Dict       =init_std
											__lowerCamelCase   :   List[Any]       =encoder_layerdrop
											__lowerCamelCase   :   Optional[Any]       =decoder_layerdrop
											__lowerCamelCase   :   Any       =use_cache
											__lowerCamelCase   :   Any       =encoder_layers
											__lowerCamelCase   :   Optional[int]       =scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCamelCase   :   int       =share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=__lowercase    ,     eos_token_id=__lowercase    ,     is_encoder_decoder=__lowercase    ,     decoder_start_token_id=__lowercase    ,     forced_eos_token_id=__lowercase    ,     **__lowercase    ,     )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def        __lowercase	(  self	:int      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )

																		if self.use_past:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch'''}
																									__lowerCamelCase   :   Any       ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																		else:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}
																									__lowerCamelCase   :   List[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}

																		if self.use_past:
																									self.fill_with_past_key_values_(__lowercase    ,     direction='''inputs'''      )
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		__lowerCamelCase   :   Any       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   List[str]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase   :   List[str]       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		    ]      )

											return common_inputs




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        __lowercase	(  self	:Optional[int]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =super().outputs
											else:
																		__lowerCamelCase   :   List[str]       =super(__lowercase    ,     self      ).outputs
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   int       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Any       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											return common_outputs




				def        __lowercase	(  self	:str    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											# Generate decoder inputs
											__lowerCamelCase   :   Optional[Any]       =seq_length if not self.use_past else 1
											__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											__lowerCamelCase   :   str       =dict(**__lowercase    ,     **__lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		__lowerCamelCase   :   Optional[Any]       =common_inputs['''decoder_input_ids'''].shape[1]
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =self.num_attention_heads
																		__lowerCamelCase   :   Any       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		__lowerCamelCase   :   Dict       =decoder_seq_length + 3
																		__lowerCamelCase   :   Tuple       =(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase    ,     __lowercase      )]    ,     dim=1      )

																		__lowerCamelCase   :   Any       =[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																		__lowerCamelCase   :   List[Any]       =min(__lowercase    ,     __lowercase      )
																		__lowerCamelCase   :   int       =max(__lowercase    ,     __lowercase      ) - min_num_layers
																		__lowerCamelCase   :   Any       ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																		for _ in range(__lowercase      ):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									    )      )
																		# TODO: test this.
																		__lowerCamelCase   :   Dict       =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																		for _ in range(__lowercase    ,     __lowercase      ):
																									common_inputs["past_key_values"].append((torch.zeros(__lowercase      ), torch.zeros(__lowercase      ))      )
											return common_inputs




				def        __lowercase	(  self	:Dict    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		__lowerCamelCase   :   str       =seqlen + 2
																		__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =self.num_layers
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =self.num_attention_heads
																		__lowerCamelCase   :   List[str]       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		__lowerCamelCase   :   Any       =common_inputs['''attention_mask'''].dtype
																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''attention_mask'''], torch.ones(__lowercase    ,     __lowercase    ,     dtype=__lowercase      )]    ,     dim=1      )
																		__lowerCamelCase   :   List[str]       =[
																		    (torch.zeros(__lowercase      ), torch.zeros(__lowercase      )) for _ in range(__lowercase      )
																		]
											return common_inputs




				def        __lowercase	(  self	:int    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											__lowerCamelCase   :   Union[str, Any]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_batch    ,     num_token_to_add=0      )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											__lowerCamelCase   :   Optional[int]       =tokenizer.num_special_tokens_to_add(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_sequence    ,     num_token_to_add=__lowercase      )

											# Generate dummy inputs according to compute batch and sequence
											__lowerCamelCase   :   Any       =[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
											__lowerCamelCase   :   Union[str, Any]       =dict(tokenizer(__lowercase    ,     return_tensors=__lowercase      )      )
											return common_inputs




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   int       =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											else:
																		__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_causal_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											return common_inputs




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:Dict    ,     __lowercase	:List[str]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   str       =super()._flatten_past_key_values_(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											else:
																		__lowerCamelCase   :   Optional[Any]       =super(__lowercase    ,     self      )._flatten_past_key_values_(
																		    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				@property
				def        __lowercase	(  self	:List[str]      ):
											return 1e-4




