

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

import os
from pathlib import Path

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     {
          '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_				:  List[Any]           =     {
          '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_				:  Tuple           =     f"{src_lang}-{tgt_lang}"

      SCREAMING_SNAKE_CASE_				:  Tuple           =     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(a					,   exist_ok=a  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     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
lowerCAmelCase     :   List[str]           =  Path(__file__).resolve().parent.parent.parent
lowerCAmelCase     :   Optional[int]           =  repo_dir / 'model_cards'

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









import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     UNetaDModel(
            sample_size=(32, 64) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     UNetaDConditionModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,					cross_attention_dim=10 ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoencoderKL(
            sample_size=(128, 64) ,					in_channels=1 ,					out_channels=1 ,					latent_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     UNetaDModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return vqvae, unet



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Any           =     Mel(
            x_res=self.dummy_unet.config.sample_size[1] ,					y_res=self.dummy_unet.config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_				:  str           =     AudioDiffusionPipeline(vqvae=_SCREAMING_SNAKE_CASE ,					unet=self.dummy_unet ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4 ,					return_dict=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     output[0][0]

        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
        assert (
            image.height == self.dummy_unet.config.sample_size[0]
            and image.width == self.dummy_unet.config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image_from_tuple.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     Mel(
            x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  str           =     DDIMScheduler()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_vqvae_and_unet
        SCREAMING_SNAKE_CASE_				:  int           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=dummy_vqvae_and_unet[1] ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.random.uniform(-1 ,					1 ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     pipe(raw_audio=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					start_step=5 ,					steps=10					)
        SCREAMING_SNAKE_CASE_				:  int           =     output.images[0]

        assert (
            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  int           =     np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_unet_condition
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=_SCREAMING_SNAKE_CASE ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.rand((1, 1, 10)					)
        SCREAMING_SNAKE_CASE_				:  Any           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					encoding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

@slow
@require_torch_gpu
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch_device

        SCREAMING_SNAKE_CASE_				:  str           =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     output.images[0]

        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0






