


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

import os
from pathlib import Path

def 				lowercase_		(		_A      :   Any   , _A      :   Dict   , _A      :   int   , _A      :   Optional[Any]						):

      """simple docstring"""




      lowerCamelCase__		:    Tuple   =  {
          "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]
      lowerCamelCase__		:    List[str]   =  {
          "wmt19-de-en-6-6-base": [0, 38.37],
          "wmt19-de-en-6-6-big": [0, 39.90],
      }
      lowerCamelCase__		:    int   =  F"{src_lang}-{tgt_lang}"

      lowerCamelCase__		:    List[Any]   =  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=_A   , exist_ok=_A						)
      lowerCamelCase__		:    int   =  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
A				:   Any		= Path(__file__).resolve().parent.parent.parent
A				:   Tuple		= repo_dir / "model_cards"

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
    A				:   Any		= model_cards_dir / "allenai" / model_name
    write_model_card(model_card_dir, src_lang="de", tgt_lang="en", model_name=model_name)






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import EfficientNetImageProcessor







class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Optional[Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      int=13 ,				__lowerCamelCase							:      Tuple=3 ,				__lowerCamelCase							:      Optional[Any]=18 ,				__lowerCamelCase							:      List[Any]=30 ,				__lowerCamelCase							:      Union[str, Any]=400 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      Union[str, Any]=None ,				__lowerCamelCase							:      Optional[Any]=True ,				__lowerCamelCase							:      str=[0.5, 0.5, 0.5] ,				__lowerCamelCase							:      Dict=[0.5, 0.5, 0.5] ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  size if size is not None else {"height": 18, "width": 18}
             lowerCamelCase__		:    Dict   =  parent
             lowerCamelCase__		:    str   =  batch_size
             lowerCamelCase__		:    Tuple   =  num_channels
             lowerCamelCase__		:    Tuple   =  image_size
             lowerCamelCase__		:    Any   =  min_resolution
             lowerCamelCase__		:    Dict   =  max_resolution
             lowerCamelCase__		:    Tuple   =  do_resize
             lowerCamelCase__		:    List[str]   =  size
             lowerCamelCase__		:    Optional[Any]   =  do_normalize
             lowerCamelCase__		:    Union[str, Any]   =  image_mean
             lowerCamelCase__		:    Optional[Any]   =  image_std





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
             }







@require_torch
@require_vision
class       _lowercase			(							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      EfficientNetImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  EfficientNetImageProcessorTester(self	)





       @property
       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  self.image_processing_class(**self.image_processor_dict	)
             self.assertTrue(hasattr(__lowerCamelCase ,				"image_mean"	)	)
             self.assertTrue(hasattr(__lowerCamelCase ,				"image_std"	)	)
             self.assertTrue(hasattr(__lowerCamelCase ,				"do_normalize"	)	)
             self.assertTrue(hasattr(__lowerCamelCase ,				"do_resize"	)	)
             self.assertTrue(hasattr(__lowerCamelCase ,				"size"	)	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.image_processing_class.from_dict(self.image_processor_dict	)
             self.assertEqual(image_processor.size ,				{"height": 18, "width": 18}	)

             lowerCamelCase__		:    Optional[int]   =  self.image_processing_class.from_dict(self.image_processor_dict ,				size=42	)
             self.assertEqual(image_processor.size ,				{"height": 42, "width": 42}	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  self.image_processing_class(**self.image_processor_dict	)
             # create random PIL images
             lowerCamelCase__		:    Any   =  prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__lowerCamelCase	)
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase ,				Image.Image	)

             # Test not batched input
             lowerCamelCase__		:    Union[str, Any]   =  image_processing(image_inputs[0] ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    Dict   =  image_processing(__lowerCamelCase ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  self.image_processing_class(**self.image_processor_dict	)
             # create random numpy tensors
             lowerCamelCase__		:    List[Any]   =  prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__lowerCamelCase ,				numpify=__lowerCamelCase	)
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase ,				np.ndarray	)

             # Test not batched input
             lowerCamelCase__		:    List[str]   =  image_processing(image_inputs[0] ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    Union[str, Any]   =  image_processing(__lowerCamelCase ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.image_processing_class(**self.image_processor_dict	)
             # create random PyTorch tensors
             lowerCamelCase__		:    int   =  prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__lowerCamelCase ,				torchify=__lowerCamelCase	)
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase ,				torch.Tensor	)

             # Test not batched input
             lowerCamelCase__		:    Dict   =  image_processing(image_inputs[0] ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    List[Any]   =  image_processing(__lowerCamelCase ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)


