
"""simple docstring"""





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

import os
from pathlib import Path
def 							A_     (__a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					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_					:						int				= Path(__file__).resolve().parent.parent.parent
UpperCamelCase_					:						Dict				= 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_					:						Optional[int]				= model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)






"""simple docstring"""





def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						current_set.copy()
					for row_index, row in enumerate(__a				):
										A_				 =						row[0]
										for column_index, column in enumerate(__a				):
															if magnitude == 0:
																				A_				 =						column
																				continue
															A_				 =						column / magnitude
    # Subtract to cancel term
					A_				 =						current_set[0]
					A_				 =						[first_row]
					A_				 =						current_set[1::]
					for row in current_set:
										A_				 =						[]
										# If first term is 0, it is already in form we want, so we preserve it
										if row[0] == 0:
															final_set.append(__a				)
															continue
										for column_index in range(len(__a				)				):
															temp_row.append(first_row[column_index] - row[column_index]				)
										final_set.append(__a				)
					# Create next recursion iteration set
					if len(final_set[0]				) != 3:
										A_				 =						final_set[0]
										A_				 =						[]
										A_				 =						[]
										for row in final_set[1::]:
															current_first_column.append(row[0]				)
															next_iteration.append(row[1::]				)
										A_				 =						simplify(__a				)
										for i in range(len(__a				)				):
															resultant[i].insert(0				,   current_first_column[i]				)
										resultant.insert(0				,   __a				)
										A_				 =						resultant
					return final_set
def 							A_     (__a				):

					'''simple docstring'''





					if len(__a				) == 0:
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					A_				 =						len(__a				) + 1
					if any(len(__a				) != _length for item in equations				):
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					for row in equations:
										if any(not isinstance(__a				,   (int, float)				) for column in row				):
															raise ValueError("solve_simultaneous() requires lists of integers"				)
					if len(__a				) == 1:
										return [equations[0][-1] / equations[0][0]]
					A_				 =						equations.copy()
					if any(0 in row for row in data_set				):
										A_				 =						data_set.copy()
										A_				 =						[]
										for row_index, row in enumerate(__a				):
															if 0 not in row:
																				A_				 =						data_set.pop(__a				)
																				break
										if not full_row:
															raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
										data_set.insert(0				,   __a				)
					A_				 =						data_set.copy()
					A_				 =						simplify(__a				)
					A_				 =						simplified[::-1]
					A_				 =						[]
					for row in simplified:
										A_				 =						row[-1]
										if not solutions:
															if row[-2] == 0:
																				solutions.append(0				)
																				continue
															solutions.append(current_solution / row[-2]				)
															continue
										A_				 =						row.copy()[: len(__a				) - 1 :]
										while temp_row[0] == 0:
															temp_row.pop(0				)
										if len(__a				) == 0:
															solutions.append(0				)
															continue
										A_				 =						temp_row[1::]
										A_				 =						temp_row[::-1]
										for column_index, column in enumerate(__a				):
															current_solution -= column * solutions[column_index]
										solutions.append(__a				)
					A_				 =						[]
					for item in solutions:
										final.append(float(round(__a				,   5				)				)				)
					return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCamelCase_					:						List[Any]				= [
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))




