

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 						__magic_name__					(							SCREAMING_SNAKE_CASE__				):
     UpperCamelCase_	:Union[str, Any]										=      (DPMSolverSinglestepScheduler,)
     UpperCamelCase_	:Dict										=      (("""num_inference_steps""", 2_5),)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     Dict:
         UpperCamelCase_			  =						{
             '''num_train_timesteps''': 1_000,
             '''beta_start''': 0.0_001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lambda_min_clipped''': -float("inf"	),
             '''variance_type''': None,
         }

         config.update(**A_	)
         return config






     def 	UpperCAmelCase_				(				self		,						_lowercase=0		,						**_lowercase	)->     List[str]:
         UpperCamelCase_			  =						dict(self.forward_default_kwargs	)
         UpperCamelCase_			  =						kwargs.pop("num_inference_steps"		,						A_	)
         UpperCamelCase_			  =						self.dummy_sample
         UpperCamelCase_			  =						0.1 * sample
         UpperCamelCase_			  =						[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
             UpperCamelCase_			  =						self.get_scheduler_config(**A_	)
             UpperCamelCase_			  =						scheduler_class(**A_	)
             scheduler.set_timesteps(A_	)
             # copy over dummy past residuals
             UpperCamelCase_			  =						dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(A_	)
                 UpperCamelCase_			  =						scheduler_class.from_pretrained(A_	)
                 new_scheduler.set_timesteps(A_	)
                 # copy over dummy past residuals
                 UpperCamelCase_			  =						dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCamelCase_			  =						sample, sample
             for t in range(A_		,						time_step + scheduler.config.solver_order + 1	):
                 UpperCamelCase_			  =						scheduler.step(A_		,						A_		,						A_		,						**A_	).prev_sample
                 UpperCamelCase_			  =						new_scheduler.step(A_		,						A_		,						A_		,						**A_	).prev_sample

                 assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         pass






     def 	UpperCAmelCase_				(				self		,						_lowercase=0		,						**_lowercase	)->     Dict:
         UpperCamelCase_			  =						dict(self.forward_default_kwargs	)
         UpperCamelCase_			  =						kwargs.pop("num_inference_steps"		,						A_	)
         UpperCamelCase_			  =						self.dummy_sample
         UpperCamelCase_			  =						0.1 * sample
         UpperCamelCase_			  =						[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
             UpperCamelCase_			  =						self.get_scheduler_config()
             UpperCamelCase_			  =						scheduler_class(**A_	)
             scheduler.set_timesteps(A_	)

             # copy over dummy past residuals (must be after setting timesteps)
             UpperCamelCase_			  =						dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(A_	)
                 UpperCamelCase_			  =						scheduler_class.from_pretrained(A_	)
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(A_	)

                 # copy over dummy past residual (must be after setting timesteps)
                 UpperCamelCase_			  =						dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCamelCase_			  =						scheduler.step(A_		,						A_		,						A_		,						**A_	).prev_sample
             UpperCamelCase_			  =						new_scheduler.step(A_		,						A_		,						A_		,						**A_	).prev_sample

             assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"






     def 	UpperCAmelCase_				(				self		,						_lowercase=None		,						**_lowercase	)->     Optional[int]:




         if scheduler is None:
             UpperCamelCase_			  =						self.scheduler_classes[0]
             UpperCamelCase_			  =						self.get_scheduler_config(**A_	)
             UpperCamelCase_			  =						scheduler_class(**A_	)

         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config(**A_	)
         UpperCamelCase_			  =						scheduler_class(**A_	)

         UpperCamelCase_			  =						10
         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter
         scheduler.set_timesteps(A_	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						model(A_		,						A_	)
             UpperCamelCase_			  =						scheduler.step(A_		,						A_		,						A_	).prev_sample

         return sample






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
         UpperCamelCase_			  =						50
         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter
         scheduler.set_timesteps(A_	)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]	):
             UpperCamelCase_			  =						model(A_		,						A_	)
             UpperCamelCase_			  =						scheduler.step(A_		,						A_		,						A_	).prev_sample

         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.2_574	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:




         for timesteps in [25, 50, 100, 999, 1_000]:
             self.check_over_configs(num_train_timesteps=A_	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
         UpperCamelCase_			  =						self.full_loop(scheduler=A_	)
         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.2_791	) < 1e-3

         UpperCamelCase_			  =						DEISMultistepScheduler.from_config(scheduler.config	)
         UpperCamelCase_			  =						DPMSolverMultistepScheduler.from_config(scheduler.config	)
         UpperCamelCase_			  =						UniPCMultistepScheduler.from_config(scheduler.config	)
         UpperCamelCase_			  =						DPMSolverSinglestepScheduler.from_config(scheduler.config	)

         UpperCamelCase_			  =						self.full_loop(scheduler=A_	)
         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.2_791	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         self.check_over_configs(thresholding=A_	)
         for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                 for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             thresholding=A_		,						prediction_type=A_		,						sample_max_value=A_		,						algorithm_type="dpmsolver++"		,						solver_order=A_		,						solver_type=A_		,						)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:




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






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:




         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                 for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             solver_order=A_		,						solver_type=A_		,						prediction_type=A_		,						algorithm_type=A_		,						)
                         UpperCamelCase_			  =						self.full_loop(
                             solver_order=A_		,						solver_type=A_		,						prediction_type=A_		,						algorithm_type=A_		,						)
                         assert not torch.isnan(A_	).any(), "Samples have nan numbers"






     def 	UpperCAmelCase_				(				self	)->     str:
         self.check_over_configs(lower_order_final=A_	)
         self.check_over_configs(lower_order_final=A_	)






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         self.check_over_configs(lambda_min_clipped=-float("inf"	)	)
         self.check_over_configs(lambda_min_clipped=-5.1	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         self.check_over_configs(variance_type=A_	)
         self.check_over_configs(variance_type="learned_range"	)






     def 	UpperCAmelCase_				(				self	)->     List[str]:




         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
             self.check_over_forward(num_inference_steps=A_		,						time_step=0	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.full_loop()
         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.2_791	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						self.full_loop(use_karras_sigmas=A_	)
         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.2_248	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						self.full_loop(prediction_type="v_prediction"	)
         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.1_453	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						self.full_loop(prediction_type="v_prediction"		,						use_karras_sigmas=A_	)
         UpperCamelCase_			  =						torch.mean(torch.abs(A_	)	)

         assert abs(result_mean.item() - 0.0_649	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config(thresholding=A_		,						dynamic_thresholding_ratio=0	)
         UpperCamelCase_			  =						scheduler_class(**A_	)

         UpperCamelCase_			  =						10
         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter.half()
         scheduler.set_timesteps(A_	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						model(A_		,						A_	)
             UpperCamelCase_			  =						scheduler.step(A_		,						A_		,						A_	).prev_sample

         assert sample.dtype == torch.floataa


"""simple docstring"""




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

import os
from pathlib import Path




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  {
      '''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:      Dict   			=  {
      '''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:      Optional[Any]   			=  f'''{src_lang}-{tgt_lang}'''

  _lowercase:      Tuple   			=  f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- 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)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
  os.makedirs(_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  _lowercase:      List[str]   			=  os.path.join(_UpperCamelCase			,					'''README.md'''    )
  print(f'''Generating {path}'''    )
  with open(_UpperCamelCase			,					'''w'''			,					encoding='''utf-8'''    ) as f:
    f.write(_UpperCamelCase    )


# make sure we are under the root of the project
A__ :       List[str]				= Path(__file__).resolve().parent.parent.parent
A__ :       Optional[int]				= repo_dir / 'model_cards'

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