




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


class  A		:



		def __init__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  data
								lowerCAmelCase_      			=  [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        SCREAMING_SNAKE_CASE__			(     UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  B'''\x80''' + B'''\x00''' * (63 - (len(self.data   ) + 8) % 64)
								lowerCAmelCase_      			=  self.data + padding + struct.pack('''>Q''',	8 * len(self.data   )   )
								return padded_data



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.padding()
								lowerCAmelCase_      			=  self.split_blocks()
								for block in self.blocks:
														lowerCAmelCase_      			=  self.expand_block(UpperCamelCase__   )
														lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  self.h
														for i in range(0,	80   ):
																				if 0 <= i < 20:
																										lowerCAmelCase_      			=  (b & c) | ((~b) & d)
																										lowerCAmelCase_      			=  0X5_a_8_2_7_9_9_9
																				elif 20 <= i < 40:
																										lowerCAmelCase_      			=  b ^ c ^ d
																										lowerCAmelCase_      			=  0X6_e_d_9_e_b_a_1
																				elif 40 <= i < 60:
																										lowerCAmelCase_      			=  (b & c) | (b & d) | (c & d)
																										lowerCAmelCase_      			=  0X8_f_1_b_b_c_d_c
																				elif 60 <= i < 80:
																										lowerCAmelCase_      			=  b ^ c ^ d
																										lowerCAmelCase_      			=  0Xc_a_6_2_c_1_d_6
																				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  (
																				    self.rotate(UpperCamelCase__,	5   ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
																				    a,
																				    self.rotate(UpperCamelCase__,	30   ),
																				    c,
																				    d,
																				)
														lowerCAmelCase_      			=  (
														    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 __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  b'''Test String'''
						assert SHAaHash(_A  ).final_hash() == hashlib.shaa(_A  ).hexdigest()  # noqa: S324





def __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  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'''  )
						lowerCAmelCase_      			=  parser.parse_args()
						lowerCAmelCase_      			=  args.input_string
						# In any case hash input should be a bytestring
						if args.input_file:
												with open(args.input_file ,  '''rb'''  ) as f:
																		lowerCAmelCase_      			=  f.read()
						else:
												lowerCAmelCase_      			=  bytes(_A ,  '''utf-8'''  )
						print(SHAaHash(_A  ).final_hash()  )


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

						doctest.testmod()










# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''merges_file''']
						with open(src_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(tgt_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(merges_file, '''w''') as fp:
												fp.write('''\n'''.join(merges))

						_A     =			FSMTTokenizer(
						    langs=['''en''', '''ru'''],
						    src_vocab_size=len(vocab),
						    tgt_vocab_size=len(vocab),
						    src_vocab_file=src_vocab_file,
						    tgt_vocab_file=tgt_vocab_file,
						    merges_file=merges_file,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




