





'''simple docstring'''

import os
from pathlib import Path







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {
				    "en": "Machine learning is great, isn't it?",
				    "ru": "Машинное обучение - это здорово, не так ли?",
				    "de": "Maschinelles Lernen ist großartig, oder?",
				}

				# BLUE scores as follows:
				# "pair": [fairseq, transformers]
				lowercase         = {
				    "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
				    "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
				    "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
				    "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
				}
				lowercase         = f'{src_lang}-{tgt_lang}'

				lowercase         = f'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\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- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
				os.makedirs(lowerCAmelCase_		,							exist_ok=lowerCAmelCase_							)
				lowercase         = os.path.join(lowerCAmelCase_		,							"README.md"							)
				print(f'Generating {path}'							)
				with open(lowerCAmelCase_		,							"w"		,							encoding="utf-8"							) as f:
								f.write(lowerCAmelCase_							)


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

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			__lowerCamelCase : Dict											=			model_name.split("-")
			__lowerCamelCase : Tuple											=			model_cards_dir / "facebook" / model_name
			write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)











'''simple docstring'''

import functools







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if not isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for day in days							):
								raise ValueError("The parameter days should be a list of integers"							)

				if len(lowerCAmelCase_							) != 3 or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for cost in costs							):
								raise ValueError("The parameter costs should be a list of three integers"							)

				if len(lowerCAmelCase_							) == 0:
								return 0

				if min(lowerCAmelCase_							) <= 0:
								raise ValueError("All days elements should be greater than 0"							)

				if max(lowerCAmelCase_							) >= 366:
								raise ValueError("All days elements should be less than 366"							)

				lowercase         = set(lowerCAmelCase_							)

				@functools.cache
				def dynamic_programming(lowerCAmelCase_							) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1							)

								return min(
								    costs[0] + dynamic_programming(index + 1							)		,							costs[1] + dynamic_programming(index + 7							)		,							costs[2] + dynamic_programming(index + 30							)		,							)

				return dynamic_programming(1							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




