


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

import os
from pathlib import Path
def        UpperCamelCase_(				_A   :int				,     _A   :Dict				,     _A   :Optional[int]				,     _A   :Any  )->		List[str]:
			UpperCamelCase__					=       {
			    "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]
			UpperCamelCase__					=       {
			    "wmt16-en-de-dist-12-1": [2_8.3, 2_7.5_2],
			    "wmt16-en-de-dist-6-1": [2_7.4, 2_7.1_1],
			    "wmt16-en-de-12-1": [2_6.9, 2_5.7_5],
			}
			UpperCamelCase__					=       F'''{src_lang}-{tgt_lang}'''

			UpperCamelCase__					=       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=_A				,     exist_ok=_A  )
			UpperCamelCase__					=       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
__UpperCamelCase    		=       Path(__file__).resolve().parent.parent.parent
__UpperCamelCase    		=       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"]:
						__UpperCamelCase    		=       model_cards_dir / 'allenai' / model_name
						write_model_card(model_card_dir, src_lang='en', tgt_lang='de', model_name=model_name)






import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    VideoMAEForPreTraining,
						    VideoMAEForVideoClassification,
						    VideoMAEModel,
						)
						from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=10      ,						snake_case=3      ,						snake_case=2      ,						snake_case=2      ,						snake_case=2      ,						snake_case=True      ,						snake_case=True      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=10      ,						snake_case=0.02      ,						snake_case=0.9      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       patch_size
						UpperCamelCase__					=       tubelet_size
						UpperCamelCase__					=       num_frames
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       type_sequence_label_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       mask_ratio
						UpperCamelCase__					=       scope

						# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
						UpperCamelCase__					=       (image_size // patch_size) ** 2
						UpperCamelCase__					=       (num_frames // tubelet_size) * self.num_patches_per_frame

						# use this variable to define bool_masked_pos
						UpperCamelCase__					=       int(mask_ratio * self.seq_length     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor(
						    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

						UpperCamelCase__					=       None
						if self.use_labels:
									UpperCamelCase__					=       ids_tensor([self.batch_size]      ,						self.type_sequence_label_size     )

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return VideoMAEConfig(
						    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						num_frames=self.num_frames      ,						tubelet_size=self.tubelet_size      ,						hidden_size=self.hidden_size      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						hidden_act=self.hidden_act      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						is_decoder=snake_case      ,						initializer_range=self.initializer_range      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining(snake_case     )
						model.to(snake_case     )
						model.eval()
						# important: each video needs to have the same number of masked patches
						# hence we define a single mask, which we then repeat for each example in the batch
						UpperCamelCase__					=       torch.ones((self.num_masks,)     )
						UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0     )     )]     )
						UpperCamelCase__					=       mask.expand(self.batch_size      ,						-1     ).bool()

						UpperCamelCase__					=       model(snake_case      ,						snake_case     )
						# model only returns predictions for masked patches
						UpperCamelCase__					=       mask.sum().item()
						UpperCamelCase__					=       3 * self.tubelet_size * self.patch_size**2
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, num_masked_patches, decoder_num_labels)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		(
			    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
			)
			_UpperCamelCase		:					Union[str, Any]    =		(
			    {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					Optional[int]    =		False
			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					int    =		False
			_UpperCamelCase		:					Any    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=False     ):

						'''simple docstring'''


						UpperCamelCase__					=       copy.deepcopy(snake_case     )

						if model_class == VideoMAEForPreTraining:
									# important: each video needs to have the same number of masked patches
									# hence we define a single mask, which we then repeat for each example in the batch
									UpperCamelCase__					=       torch.ones((self.model_tester.num_masks,)     )
									UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0     )     )]     )
									UpperCamelCase__					=       mask.expand(self.model_tester.batch_size      ,						-1     ).bool()
									UpperCamelCase__					=       bool_masked_pos.to(snake_case     )

						if return_labels:
									if model_class in [
									    *get_values(snake_case     ),
									]:
												UpperCamelCase__					=       torch.zeros(
												    self.model_tester.batch_size      ,						dtype=torch.long      ,						device=snake_case     )

						return inputs_dict




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="VideoMAE does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(snake_case     )
									self.assertIsInstance(model.get_input_embeddings()      ,						(nn.Module)     )
									UpperCamelCase__					=       model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case      ,						nn.Linear     )     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(snake_case     )
									UpperCamelCase__					=       inspect.signature(model.forward     )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCamelCase__					=       [*signature.parameters.keys()]

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       VideoMAEModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if not self.has_attentions:
									pass

						else:
									UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase__					=       True

									for model_class in self.all_model_classes:
												UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
												UpperCamelCase__					=       (
												    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
												)

												UpperCamelCase__					=       True
												UpperCamelCase__					=       False
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)
												UpperCamelCase__					=       len(snake_case     )

												# Check attention is always last and order is fine
												UpperCamelCase__					=       True
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

												self.assertEqual(out_len + 1      ,						len(snake_case     )     )

												UpperCamelCase__					=       outputs.attentions

												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.hidden_states
									UpperCamelCase__					=       self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(snake_case     )      ,						snake_case     )

									UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
									UpperCamelCase__					=       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]     )      ,						[seq_length, self.model_tester.hidden_size]      ,						)

						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       True
									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__					=       True

									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass



def        UpperCamelCase_(				)->		Union[str, Any]:
			UpperCamelCase__					=       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"				,     filename="eating_spaghetti.npy"				,     repo_type="dataset"  )
			UpperCamelCase__					=       np.load(_A  )
			return list(_A  )







@require_torch
@require_vision
class        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return (
						    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,						image_std=[0.5, 0.5, 0.5]     )
						    if is_vision_available()
						    else None
						)




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"     ).to(
						    snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 400)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )

						UpperCamelCase__					=       torch.tensor([0.3669, -0.0688, -0.2421]     ).to(snake_case     )

						self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,						snake_case      ,						atol=1E-4     )     )







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"     ).to(snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# add boolean mask, indicating which patches to mask
						UpperCamelCase__					=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"      ,						filename="bool_masked_pos.pt"     )
						UpperCamelCase__					=       torch.load(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size([1, 1408, 1536]     )
						UpperCamelCase__					=       torch.tensor(
						    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]      ,						device=snake_case     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )
						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `True`)
						UpperCamelCase__					=       torch.tensor([0.5142]      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `False`)
						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"      ,						norm_pix_loss=snake_case     ).to(
						    snake_case     )

						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						UpperCamelCase__					=       torch.tensor(torch.tensor([0.6469]     )      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )


