






"""simple docstring"""
import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:







			class      UpperCamelCase_			:





				@staticmethod
				def 	_SCREAMING_SNAKE_CASE ( *lowerCAmelCase_   :					Dict      ,      **lowerCAmelCase_   :					Dict		)		->     Optional[int]:
							pass







@is_pipeline_test
@require_vision
@require_torch
class      UpperCamelCase_			(unittest.TestCase	):
	__magic_name__       =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					int		)		->     List[str]:
				UpperCAmelCase_	:					Tuple     	=   pipeline(
				    "zero-shot-object-detection"      ,      model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

				UpperCAmelCase_	:					Optional[Any]     	=   [
				    {
				        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
				        "candidate_labels": ["cat", "remote", "couch"],
				    }
				]
				return object_detector, examples





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   object_detector(examples[0]      ,      threshold=0.0		)

				UpperCAmelCase_	:					Tuple     	=   len(lowerCAmelCase_		)
				self.assertGreater(lowerCAmelCase_      ,      0		)
				self.assertEqual(
				    lowerCAmelCase_      ,      [
				        {
				            "score": ANY(lowerCAmelCase_		),
				            "label": ANY(lowerCAmelCase_		),
				            "box": {"xmin": ANY(lowerCAmelCase_		), "ymin": ANY(lowerCAmelCase_		), "xmax": ANY(lowerCAmelCase_		), "ymax": ANY(lowerCAmelCase_		)},
				        }
				        for i in range(lowerCAmelCase_		)
				    ]      ,      )





	@require_tf
	@unittest.skip("Zero Shot Object Detection not implemented in TF"		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Tuple:
				pass





	@require_torch
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   pipeline(
				    "zero-shot-object-detection"      ,      model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

				UpperCAmelCase_	:					Dict     	=   object_detector(
				    "./tests/fixtures/tests_samples/COCO/000000039769.png"      ,      candidate_labels=["cat", "remote", "couch"]      ,      threshold=0.6_4      ,      )

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				        {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				        {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				        {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				        {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				        {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				        {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				        {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
				        {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				    ]      ,      )

				UpperCAmelCase_	:					Dict     	=   object_detector(
				    [
				        {
				            "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
				            "candidate_labels": ["cat", "remote", "couch"],
				        }
				    ]      ,      threshold=0.6_4      ,      )

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        [
				            {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				            {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				            {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				            {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				            {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				            {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				            {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				            {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
				            {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				        ]
				    ]      ,      )





	@require_torch
	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Tuple:
				UpperCAmelCase_	:					int     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					Any     	=   object_detector(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"      ,      candidate_labels=["cat", "remote", "couch"]      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				        {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
				        {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
				    ]      ,      )

				UpperCAmelCase_	:					List[str]     	=   object_detector(
				    [
				        {
				            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
				            "candidate_labels": ["cat", "remote", "couch"],
				        },
				        {
				            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
				            "candidate_labels": ["cat", "remote", "couch"],
				        },
				    ]      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        [
				            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
				            {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
				        ],
				        [
				            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
				            {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
				        ],
				    ]      ,      )





	@require_tf
	@unittest.skip("Zero Shot Object Detection not implemented in TF"		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				pass





	@require_torch
	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[str]:
				UpperCAmelCase_	:					Optional[int]     	=   0.2
				UpperCAmelCase_	:					Any     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					Optional[int]     	=   object_detector(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"      ,      candidate_labels=["cat", "remote", "couch"]      ,      threshold=lowerCAmelCase_      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				    ]      ,      )





	@require_torch
	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Any:
				UpperCAmelCase_	:					Optional[int]     	=   2
				UpperCAmelCase_	:					Tuple     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					Dict     	=   object_detector(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"      ,      candidate_labels=["cat", "remote", "couch"]      ,      top_k=lowerCAmelCase_      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				    ]      ,      )





"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  {
						    """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     =  {
						    """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],
						}
						__lowerCAmelCase     =  f"""{src_lang}-{tgt_lang}"""

						__lowerCAmelCase     =  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=_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    """README.md"""						)
						print(f"""Generating {path}"""						)
						with open(_lowerCAmelCase ,    """w""" ,    encoding="""utf-8"""						) as f:
												f.write(_lowerCAmelCase						)


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