




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

import os
from pathlib import Path







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> List[str]:



       """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"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
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


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The 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.

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=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
       model_card_dir.mkdir(parents=lowercase_				,       exist_ok=lowercase_		)
       A__   				=					os.path.join(lowercase_				,       '''README.md'''		)
       print(f"""Generating {path}"""		)
       with open(lowercase_				,       '''w'''				,       encoding='''utf-8'''		) as f:
              f.write(lowercase_		)


# make sure we are under the root of the project
_lowerCamelCase			:	Tuple							 =     Path(__file__).resolve().parent.parent.parent
_lowerCamelCase			:	int							 =     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"]:
  _lowerCamelCase			:	str							 =     model_cards_dir / """allenai""" / model_name
  write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

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


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Dict:





           '''simple docstring'''

           A__   				=					1
           A__   				=					3
           A__   				=					(32, 32)

           A__   				=					floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0)).to(UpperCAmelCase__)
           return image


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					UNetaDConditionModel(
               block_out_channels=(32, 32, 64)      ,				layers_per_block=2      ,				sample_size=32      ,				in_channels=7      ,				out_channels=4      ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')      ,				up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,				cross_attention_dim=32      ,				attention_head_dim=8      ,				use_linear_projection=UpperCAmelCase__      ,				only_cross_attention=(True, True, False)      ,				num_class_embeds=100      ,				)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					AutoencoderKL(
               block_out_channels=[32, 32, 64]      ,				in_channels=3      ,				out_channels=3      ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,				latent_channels=4      ,				)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					CLIPTextConfig(
               bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=32      ,				intermediate_size=37      ,				layer_norm_eps=1e-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1_000      ,				hidden_act='''gelu'''      ,				projection_dim=512      ,				)
           return CLIPTextModel(UpperCAmelCase__)


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





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)

           A__   				=					output.images

           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				return_dict=UpperCAmelCase__      ,				)[0]

           A__   				=					image[0, -3:, -3:, -1]
           A__   				=					image_from_tuple[0, -3:, -3:, -1]

           A__   				=					low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           A__   				=					np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


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





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					sd_pipe(
               2 * [prompt]      ,				image=2 * [low_res_image]      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)
           A__   				=					output.images
           assert image.shape[0] == 2

           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_images_per_prompt=2      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)
           A__   				=					output.images
           assert image.shape[0] == 2





    @unittest.skipIf(torch_device != '''cuda'''      ,				'''This test requires a GPU''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # put models in fp16, except vae as it overflows in fp16
           A__   				=					unet.half()
           A__   				=					text_encoder.half()

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					torch.manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=2      ,				output_type='''np'''      ,				).images

           A__   				=					low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)


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

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->str:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
               '''/upsampled_cat.npy''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				output_type='''np'''      ,				)
           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 1e-3


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
               '''/upsampled_cat_fp16.npy''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(
               UpperCAmelCase__      ,				torch_dtype=torch.floataa      ,				)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				output_type='''np'''      ,				)
           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 5e-1





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





           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(
               UpperCAmelCase__      ,				torch_dtype=torch.floataa      ,				)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=5      ,				output_type='''np'''      ,				)

           A__   				=					torch.cuda.max_memory_allocated()
           # make sure that less than 2.9 GB is allocated
           assert mem_bytes < 2.9 * 10**9


