

import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image

 from transformers.image_utils import load_image
else:






 class 						__magic_name__					:






      @staticmethod
      def 	UpperCAmelCase_				(				*_lowercase		,						**_lowercase	)->     Optional[int]:
          pass




 def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Dict:



     """simple docstring"""






     return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
SCREAMING_SNAKE_CASE		:Tuple						=     (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)






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






     @require_pytesseract
     @require_vision
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						pipeline(
             "document-question-answering"		,						model=A_		,						tokenizer=A_		,						image_processor=A_	)

         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						list(zip(*apply_tesseract(load_image(A_	)		,						A_		,						""	)	)	)
         UpperCamelCase_			  =						'''What is the placebo?'''
         UpperCamelCase_			  =						[
             {
                 '''image''': load_image(A_	),
                 '''question''': question,
             },
             {
                 '''image''': image,
                 '''question''': question,
             },
             {
                 '''image''': image,
                 '''question''': question,
                 '''word_boxes''': word_boxes,
             },
         ]
         return dqa_pipeline, examples






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Optional[Any]:
         UpperCamelCase_			  =						dqa_pipeline(A_		,						top_k=2	)
         self.assertEqual(
             A_		,						[
                 [
                     {"score": ANY(A_	), "answer": ANY(A_	), "start": ANY(A_	), "end": ANY(A_	)},
                     {"score": ANY(A_	), "answer": ANY(A_	), "start": ANY(A_	), "end": ANY(A_	)},
                 ]
             ]
             * 3		,						)






     @require_torch
     @require_detectrona
     @require_pytesseract
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						pipeline("document-question-answering"		,						model="hf-internal-testing/tiny-random-layoutlmv2"	)
         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						'''How many cats are there?'''

         UpperCamelCase_			  =						[
             {'''score''': 0.0_001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
             {'''score''': 0.0_001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
         ]
         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(nested_simplify(A_		,						decimals=4	)		,						A_	)

         UpperCamelCase_			  =						dqa_pipeline({"image": image, "question": question}		,						top_k=2	)
         self.assertEqual(nested_simplify(A_		,						decimals=4	)		,						A_	)

         # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
         # Empty answer probably
         UpperCamelCase_			  =						'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(A_		,						[]	)

         # We can optionnally pass directly the words and bounding boxes
         UpperCamelCase_			  =						'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
         UpperCamelCase_			  =						[]
         UpperCamelCase_			  =						[]
         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						words=A_		,						boxes=A_		,						top_k=2	)
         self.assertEqual(A_		,						[]	)






     @slow
     @require_torch
     @require_detectrona
     @require_pytesseract
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						pipeline(
             "document-question-answering"		,						model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"		,						revision="9977165"		,						)
         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						'''What is the invoice number?'''

         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline({"image": image, "question": question}		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 [
                     {"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
                 ],
             ]
             * 2		,						)






     @slow
     @require_torch
     @require_detectrona
     @require_pytesseract
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						pipeline(
             "document-question-answering"		,						model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"		,						revision="9977165"		,						max_seq_len=50		,						)
         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						'''What is the invoice number?'''

         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
                 {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline({"image": image, "question": question}		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
                 {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 [
                     {"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
                     {"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
                 ]
             ]
             * 2		,						)






     @slow
     @require_torch
     @require_pytesseract
     @require_vision
     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						AutoTokenizer.from_pretrained(
             "impira/layoutlm-document-qa"		,						revision="3dc6de3"		,						add_prefix_space=A_	)
         UpperCamelCase_			  =						pipeline(
             "document-question-answering"		,						model="impira/layoutlm-document-qa"		,						tokenizer=A_		,						revision="3dc6de3"		,						)
         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						'''What is the invoice number?'''

         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline({"image": image, "question": question}		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 [
                     {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
                 ]
             ]
             * 2		,						)

         UpperCamelCase_			  =						list(zip(*apply_tesseract(load_image(A_	)		,						A_		,						""	)	)	)

         # This model should also work if `image` is set to None
         UpperCamelCase_			  =						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
             ]		,						)






     @slow
     @require_torch
     @require_pytesseract
     @require_vision
     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						AutoTokenizer.from_pretrained(
             "impira/layoutlm-document-qa"		,						revision="3dc6de3"		,						add_prefix_space=A_	)
         UpperCamelCase_			  =						pipeline(
             "document-question-answering"		,						model="impira/layoutlm-document-qa"		,						tokenizer=A_		,						revision="3dc6de3"		,						max_seq_len=50		,						)
         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						'''What is the invoice number?'''

         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)

         UpperCamelCase_			  =						dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 [
                     {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
                 ]
             ]
             * 2		,						)

         UpperCamelCase_			  =						list(zip(*apply_tesseract(load_image(A_	)		,						A_		,						""	)	)	)

         # This model should also work if `image` is set to None
         UpperCamelCase_			  =						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}		,						top_k=2	)
         self.assertEqual(
             nested_simplify(A_		,						decimals=4	)		,						[
                 {"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
             ]		,						)






     @slow
     @require_torch
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						pipeline(
             "document-question-answering"		,						model="naver-clova-ix/donut-base-finetuned-docvqa"		,						tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"	)		,						feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa"		,						)

         UpperCamelCase_			  =						INVOICE_URL
         UpperCamelCase_			  =						'''What is the invoice number?'''
         UpperCamelCase_			  =						dqa_pipeline(image=A_		,						question=A_		,						top_k=2	)
         self.assertEqual(nested_simplify(A_		,						decimals=4	)		,						[{"answer": "us-001"}]	)






     @require_tf
     @unittest.skip("Document question answering not implemented in TF"	)
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         pass


"""simple docstring"""




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

import os
from pathlib import Path




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  {
      '''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]
  _lowercase:      Dict   			=  {
      '''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'''],
  }
  _lowercase:      Optional[Any]   			=  f'''{src_lang}-{tgt_lang}'''

  _lowercase:      Tuple   			=  f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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

- 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)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
  os.makedirs(_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  _lowercase:      List[str]   			=  os.path.join(_UpperCamelCase			,					'''README.md'''    )
  print(f'''Generating {path}'''    )
  with open(_UpperCamelCase			,					'''w'''			,					encoding='''utf-8'''    ) as f:
    f.write(_UpperCamelCase    )


# make sure we are under the root of the project
A__ :       List[str]				= Path(__file__).resolve().parent.parent.parent
A__ :       Optional[int]				= repo_dir / 'model_cards'

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