


import os
from pathlib import Path







def   lowerCamelCase(				a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					={
				    '''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]
				_SCREAMING_SNAKE_CASE     					={
				    '''wmt19-de-en-6-6-base''': [0, 38.37],
				    '''wmt19-de-en-6-6-big''': [0, 39.90],
				}
				_SCREAMING_SNAKE_CASE     					=f"{src_lang}-{tgt_lang}"

				_SCREAMING_SNAKE_CASE     					=f"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 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\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\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   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\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 wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -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/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\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=lowercase_		,exist_ok=lowercase_)
				_SCREAMING_SNAKE_CASE     					=os.path.join(lowercase_		,'''README.md''')
				print(f"Generating {path}")
				with open(lowercase_		,'''w'''		,encoding='''utf-8''') as f:
								f.write(lowercase_)


# make sure we are under the root of the project
snake_case_  :     Optional[Any]							  =							Path(__file__).resolve().parent.parent.parent
snake_case_  :     List[Any]							  =							repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
						snake_case_  :     int							  =							model_cards_dir / 'allenai' / model_name
						write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)



import math







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=math.log(2		,10) * number
				_SCREAMING_SNAKE_CASE     					=round((log_number - int(a__))		,15)

				return difference







def   lowerCamelCase(				a__ = 67_8910):
				_SCREAMING_SNAKE_CASE     					=90
				_SCREAMING_SNAKE_CASE     					=0

				_SCREAMING_SNAKE_CASE     					=math.log(1.23		,10)
				_SCREAMING_SNAKE_CASE     					=math.log(1.24		,10)
				_SCREAMING_SNAKE_CASE     					=0

				while position < number:
								_SCREAMING_SNAKE_CASE     					=log_difference(a__)

								if difference >= upper_limit:
												power_iterator += 93

								elif difference < lower_limit:
												power_iterator += 196

								else:
												_SCREAMING_SNAKE_CASE     					=power_iterator
												power_iterator += 196
												position += 1

				return previous_power


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							print(f"""{solution() = }""")