




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





class    __lowercase						:







   def __init__(	self      ,							UpperCamelCase      )				->    str:
     __a			=					data
     __a			=					[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__			(	UpperCamelCase      ,							UpperCamelCase      )				->    Union[str, Any]:
     return ((n << b) | (n >> (32 - b))) & 0xF_F_F_F_F_F_F_F







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					b'\x80' + b'\x00' * (63 - (len(self.data      ) + 8) % 64)
     __a			=					self.data + padding + struct.pack('>Q'      ,							8 * len(self.data      )      )
     return padded_data







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







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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.padding()
     __a			=					self.split_blocks()
     for block in self.blocks:
       __a			=					self.expand_block(UpperCamelCase      )
       __a	,       __a	,       __a	,       __a	,       __a			=					self.h
       for i in range(0      ,							80      ):
         if 0 <= i < 20:
           __a			=					(b & c) | ((~b) & d)
           __a			=					0x5_A_8_2_7_9_9_9
         elif 20 <= i < 40:
           __a			=					b ^ c ^ d
           __a			=					0x6_E_D_9_E_B_A_1
         elif 40 <= i < 60:
           __a			=					(b & c) | (b & d) | (c & d)
           __a			=					0x8_F_1_B_B_C_D_C
         elif 60 <= i < 80:
           __a			=					b ^ c ^ d
           __a			=					0xC_A_6_2_C_1_D_6
         __a	,       __a	,       __a	,       __a	,       __a			=					(
             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,
         )
       __a			=					(
           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       (      ):
  __a			=					B'Test String'
  assert SHAaHash(__SCREAMING_SNAKE_CASE			).final_hash() == hashlib.shaa(__SCREAMING_SNAKE_CASE			).hexdigest()  # noqa: S324


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					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			=					parser.parse_args()
  __a			=					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			=					f.read()
  else:
    __a			=					bytes(__SCREAMING_SNAKE_CASE				,					'utf-8'			)
  print(SHAaHash(__SCREAMING_SNAKE_CASE			).final_hash()			)


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

  doctest.testmod()









'''simple docstring'''


# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							int				,					a_	:							Tuple				,					a_	:							Tuple			):
  __a			=					{
      'en': 'Machine learning is great, isn\'t it?',
      'ru': 'Машинное обучение - это здорово, не так ли?',
      'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  __a			=					{
      'wmt16-en-de-dist-12-1': [28.3, 27.52],
      'wmt16-en-de-dist-6-1': [27.4, 27.11],
      'wmt16-en-de-12-1': [26.9, 25.75],
  }
  __a			=					f"{src_lang}-{tgt_lang}"

  __a			=					f"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
  model_card_dir.mkdir(parents=a_				,					exist_ok=a_			)
  __a			=					os.path.join(a_				,					'README.md'			)
  print(f"Generating {path}"			)
  with open(a_				,					'w'				,					encoding='utf-8'			) as f:
    f.write(a_			)


# make sure we are under the root of the project
UpperCAmelCase_ 		=      Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_ 		=      repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
  UpperCAmelCase_ 		=      model_cards_dir / "allenai" / model_name
  write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



