



'''simple docstring'''




import os
from pathlib import Path


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Dict      ):
		__SCREAMING_SNAKE_CASE :	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]
		__SCREAMING_SNAKE_CASE :	str					 =	{
		    """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"""],
		}
		__SCREAMING_SNAKE_CASE :	Dict					 =	F"{src_lang}-{tgt_lang}"

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	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
UpperCamelCase__ :			Union[str, Any]												=				Path(__file__).resolve().parent.parent.parent
UpperCamelCase__ :			int												=				repo_dir / '''model_cards'''

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






'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	(KDPMaDiscreteScheduler,)
			_A   :	Dict    					=	10

			def UpperCamelCase__      (							self  :				Optional[int]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	{
					    """num_train_timesteps""": 1_1_0_0,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]		):
							self.check_over_configs(beta_start=lowerCAmelCase__							,  beta_end=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config(prediction_type="""v_prediction"""		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.69_34E-07		) < 1E-2
							assert abs(result_mean.item() - 6.11_12E-10		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07		) < 1E-2
							assert abs(result_mean.item() - 0.00_02		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	str					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	int					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3



			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps							,  device=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter.to(lowerCAmelCase__		) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	str					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if str(lowerCAmelCase__		).startswith("""cpu"""		):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3