






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

import os
from pathlib import Path

def 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__			,snake_case__						)				->    Tuple:






						"""simple docstring"""





						_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=snake_case__			,exist_ok=snake_case__						)
						_SCREAMING_SNAKE_CASE       =    os.path.join(snake_case__			,"""README.md"""						)
						print(F'Generating {path}'						)
						with open(snake_case__			,"""w"""			,encoding="""utf-8"""						) as f:
												f.write(snake_case__						)


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









from __future__ import annotations

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    len(snake_case__						) - 1
						while left <= right:
												_SCREAMING_SNAKE_CASE       =    (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								_SCREAMING_SNAKE_CASE       =    middle + 1
																		break
												elif sorted_list[middle] < item:
																		_SCREAMING_SNAKE_CASE       =    middle + 1
												else:
																		_SCREAMING_SNAKE_CASE       =    middle - 1
						sorted_list.insert(snake_case__			,snake_case__						)
						return sorted_list

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0						)						)
												else:
																		result.append(right.pop(0						)						)
						return result + left + right

def 			__lowerCamelCase     (						snake_case__						)				->    List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    len(snake_case__						)
						if length <= 1:
												return list_ad
						_SCREAMING_SNAKE_CASE       =    length // 2
						return merge(sortlist_ad(list_ad[:middle]						)			,sortlist_ad(list_ad[middle:]						)						)

def 			__lowerCamelCase     (						snake_case__						)				->    list[int]:






						"""simple docstring"""





						if len(snake_case__						) <= 1:
												return collection
						_SCREAMING_SNAKE_CASE       =    []
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(0			,len(snake_case__						)			,2						):
												if i == len(snake_case__						) - 1:
																		_SCREAMING_SNAKE_CASE       =    True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]						)
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]						)
						_SCREAMING_SNAKE_CASE       =    sortlist_ad(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]						)
						if has_last_odd_item:
												_SCREAMING_SNAKE_CASE       =    collection[-1]
												_SCREAMING_SNAKE_CASE       =    binary_search_insertion(snake_case__			,snake_case__						)
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(len(snake_case__						) - 1						):
												if result[i] == collection[-1] and has_last_odd_item:
																		_SCREAMING_SNAKE_CASE       =    True
												_SCREAMING_SNAKE_CASE       =    sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 2] + binary_search_insertion(result[i + 2 :]			,snake_case__						)
												else:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 1] + binary_search_insertion(result[i + 1 :]			,snake_case__						)

						return result


if __name__ == "__main__":
		UpperCamelCase												=      input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase												=      [int(item) for item in user_input.split(''',''')]
		print(merge_insertion_sort(unsorted))

