




"""simple docstring"""




from datetime import datetime

import requests



def     a__ (				snake_case__				)						->					bytes:
			lowerCamelCase			      =					"https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
			lowerCamelCase			      =					requests.get(base_url + url				).json()[0]["urls"][0]["src"]
			return requests.get(_lowerCamelCase				).content


if __name__ == "__main__":
					lowerCAmelCase							:			int     							=							input("""Enter Video/IGTV url: """).strip()
					lowerCAmelCase							:			Union[str, Any]     							=							F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
					with open(file_name, """wb""") as fp:
										fp.write(download_video(url))
					print(F"""Done. Video saved to disk as {file_name}.""")





# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Any)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					{
							    "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]
							__UpperCamelCase	:   Any			   =					{
							    "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"],
							}
							__UpperCamelCase	:   str			   =					F'{src_lang}-{tgt_lang}'

							__UpperCamelCase	:   Tuple			   =					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)
							__UpperCamelCase	:   Dict			   =					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
lowercase  :					List[str]													=       Path(__file__).resolve().parent.parent.parent
lowercase  :					Union[str, Any]													=       repo_dir / 'model_cards'

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