





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

import os
from pathlib import Path






def 							_lowerCAmelCase (  _a   :					List[str]					,   _a   :					Dict					,   _a   :					int					,   _a   :					int			)	->			Any:
     lowerCAmelCase_					:       List[Any]    =		{
         """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_					:       str    =		{
         """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_					:       Any    =		F'{src_lang}-{tgt_lang}'

     lowerCAmelCase_					:       Tuple    =		F'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "allenai/{model_name}"\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\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe 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.\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=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="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\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n'
     model_card_dir.mkdir(parents=_a					,   exist_ok=_a			)
     lowerCAmelCase_					:       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
UpperCAmelCase_					:   Any 							=						Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_					:   Optional[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"]:
      UpperCAmelCase_					:   Union[str, Any] 							=						model_cards_dir / """allenai""" / model_name
      write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)







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      lowercase__    :







        @staticmethod
        def 	UpperCAmelCase__		(	*_lowercase    ,						**_lowercase						):
             pass






      def 							_lowerCAmelCase (  _a   :					List[str]			)	->			Any:
           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.
UpperCAmelCase_					:   Optional[Any] 							=						(
    """https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)





@is_pipeline_test
@require_torch
@require_vision
class      lowercase__    (							unittest.TestCase  ):
  __UpperCamelCase	     =  MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







  @require_pytesseract
  @require_vision
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		pipeline(
           """document-question-answering"""    ,						model=_lowercase    ,						tokenizer=_lowercase    ,						image_processor=_lowercase						)

       lowerCAmelCase_					:       List[str]    =		INVOICE_URL
       lowerCAmelCase_					:       str    =		list(zip(*apply_tesseract(load_image(_lowercase						)    ,						_lowercase    ,						""""""						)						)						)
       lowerCAmelCase_					:       int    =		"""What is the placebo?"""
       lowerCAmelCase_					:       str    =		[
           {
               """image""": load_image(_lowercase						),
               """question""": question,
           },
           {
               """image""": image,
               """question""": question,
           },
           {
               """image""": image,
               """question""": question,
               """word_boxes""": word_boxes,
           },
       ]
       return dqa_pipeline, examples







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       List[str]    =		dqa_pipeline(_lowercase    ,						top_k=2						)
       self.assertEqual(
           _lowercase    ,						[
               [
                   {"""score""": ANY(_lowercase						), """answer""": ANY(_lowercase						), """start""": ANY(_lowercase						), """end""": ANY(_lowercase						)},
                   {"""score""": ANY(_lowercase						), """answer""": ANY(_lowercase						), """start""": ANY(_lowercase						), """end""": ANY(_lowercase						)},
               ]
           ]
           * 3    ,						)







  @require_torch
  @require_detectrona
  @require_pytesseract
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		pipeline("""document-question-answering"""    ,						model="""hf-internal-testing/tiny-random-layoutlmv2"""						)
       lowerCAmelCase_					:       str    =		INVOICE_URL
       lowerCAmelCase_					:       int    =		"""How many cats are there?"""

       lowerCAmelCase_					:       List[Any]    =		[
           {"""score""": 0.0001, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
           {"""score""": 0.0001, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
       ]
       lowerCAmelCase_					:       Optional[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(nested_simplify(_lowercase    ,						decimals=4						)    ,						_lowercase						)

       lowerCAmelCase_					:       Any    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(nested_simplify(_lowercase    ,						decimals=4						)    ,						_lowercase						)

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

       # We can optionnally pass directly the words and bounding boxes
       lowerCAmelCase_					:       int    =		"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
       lowerCAmelCase_					:       int    =		[]
       lowerCAmelCase_					:       List[Any]    =		[]
       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						words=_lowercase    ,						boxes=_lowercase    ,						top_k=2						)
       self.assertEqual(_lowercase    ,						[]						)







  @slow
  @require_torch
  @require_detectrona
  @require_pytesseract
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		pipeline(
           """document-question-answering"""    ,						model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""    ,						revision="""9977165"""    ,						)
       lowerCAmelCase_					:       Dict    =		INVOICE_URL
       lowerCAmelCase_					:       int    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       Union[str, Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       List[str]    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
                   {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
               ],
           ]
           * 2    ,						)







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

       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
               {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       Optional[Any]    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
               {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
                   {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
               ]
           ]
           * 2    ,						)







  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		AutoTokenizer.from_pretrained(
           """impira/layoutlm-document-qa"""    ,						revision="""3dc6de3"""    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Any    =		pipeline(
           """document-question-answering"""    ,						model="""impira/layoutlm-document-qa"""    ,						tokenizer=_lowercase    ,						revision="""3dc6de3"""    ,						)
       lowerCAmelCase_					:       int    =		INVOICE_URL
       lowerCAmelCase_					:       Union[str, Any]    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       Optional[Any]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
           ]    ,						)

       lowerCAmelCase_					:       int    =		dqa_pipeline({"""image""": image, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
           ]    ,						)

       lowerCAmelCase_					:       Union[str, Any]    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
                   {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
               ]
           ]
           * 2    ,						)

       lowerCAmelCase_					:       int    =		list(zip(*apply_tesseract(load_image(_lowercase						)    ,						_lowercase    ,						""""""						)						)						)

       # This model should also work if `image` is set to None
       lowerCAmelCase_					:       List[Any]    =		dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
           ]    ,						)







  @slow
  @require_torch
  @require_pytesseract
  @require_vision
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		AutoTokenizer.from_pretrained(
           """impira/layoutlm-document-qa"""    ,						revision="""3dc6de3"""    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		pipeline(
           """document-question-answering"""    ,						model="""impira/layoutlm-document-qa"""    ,						tokenizer=_lowercase    ,						revision="""3dc6de3"""    ,						max_seq_len=50    ,						)
       lowerCAmelCase_					:       Tuple    =		INVOICE_URL
       lowerCAmelCase_					:       Tuple    =		"""What is the invoice number?"""

       lowerCAmelCase_					:       Optional[int]    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)

       lowerCAmelCase_					:       int    =		dqa_pipeline(
           [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               [
                   {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
                   {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
               ]
           ]
           * 2    ,						)

       lowerCAmelCase_					:       Union[str, Any]    =		list(zip(*apply_tesseract(load_image(_lowercase						)    ,						_lowercase    ,						""""""						)						)						)

       # This model should also work if `image` is set to None
       lowerCAmelCase_					:       List[str]    =		dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}    ,						top_k=2						)
       self.assertEqual(
           nested_simplify(_lowercase    ,						decimals=4						)    ,						[
               {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
               {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
           ]    ,						)







  @slow
  @require_torch
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		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"""    ,						)

       lowerCAmelCase_					:       Union[str, Any]    =		INVOICE_URL
       lowerCAmelCase_					:       int    =		"""What is the invoice number?"""
       lowerCAmelCase_					:       str    =		dqa_pipeline(image=_lowercase    ,						question=_lowercase    ,						top_k=2						)
       self.assertEqual(nested_simplify(_lowercase    ,						decimals=4						)    ,						[{"""answer""": """us-001"""}]						)







  @require_tf
  @unittest.skip("""Document question answering not implemented in TF"""						)
  def 	UpperCAmelCase__		(	self						):
       pass
