





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DetaImageProcessor

class      A       (	unittest.TestCase						):



      def __init__(	self:						Any		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						List[Any]=7		,  _lowerCAmelCase:						List[Any]=3		,  _lowerCAmelCase:						Any=30		,  _lowerCAmelCase:						List[Any]=400		,  _lowerCAmelCase:						int=True		,  _lowerCAmelCase:						Dict=None		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						Any=[0.5, 0.5, 0.5]		,  _lowerCAmelCase:						Optional[int]=[0.5, 0.5, 0.5]		,  _lowerCAmelCase:						List[str]=True		,  _lowerCAmelCase:						int=1 / 255		,  _lowerCAmelCase:						Dict=True		,  )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         UpperCAmelCase_											=parent
         UpperCAmelCase_											=batch_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=min_resolution
         UpperCAmelCase_											=max_resolution
         UpperCAmelCase_											=do_resize
         UpperCAmelCase_											=size
         UpperCAmelCase_											=do_normalize
         UpperCAmelCase_											=image_mean
         UpperCAmelCase_											=image_std
         UpperCAmelCase_											=do_rescale
         UpperCAmelCase_											=rescale_factor
         UpperCAmelCase_											=do_pad



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						int=False       )						->    int:
         '''simple docstring'''

         if not batched:
            UpperCAmelCase_											=image_inputs[0]
            if isinstance(_lowerCAmelCase		,  Image.Image       ):
               UpperCAmelCase_						,       UpperCAmelCase_											=image.size
            else:
               UpperCAmelCase_						,       UpperCAmelCase_											=image.shape[1], image.shape[2]
            if w < h:
               UpperCAmelCase_											=int(self.size["shortest_edge"] * h / w       )
               UpperCAmelCase_											=self.size["shortest_edge"]
            elif w > h:
               UpperCAmelCase_											=self.size["shortest_edge"]
               UpperCAmelCase_											=int(self.size["shortest_edge"] * w / h       )
            else:
               UpperCAmelCase_											=self.size["shortest_edge"]
               UpperCAmelCase_											=self.size["shortest_edge"]

         else:
            UpperCAmelCase_											=[]
            for image in image_inputs:
               UpperCAmelCase_						,       UpperCAmelCase_											=self.get_expected_values([image]       )
               expected_values.append((expected_height, expected_width)       )
            UpperCAmelCase_											=max(_lowerCAmelCase		,  key=lambda _lowerCAmelCase       : item[0]       )[0]
            UpperCAmelCase_											=max(_lowerCAmelCase		,  key=lambda _lowerCAmelCase       : item[1]       )[1]

         return expected_height, expected_width

@require_torch
@require_vision
class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =DetaImageProcessor if is_vision_available() else None



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=DetaImageProcessingTester(self       )



      @property
      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "image_mean"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "image_std"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_normalize"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_resize"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_rescale"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_pad"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "size"       )       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size		,  {"shortest_edge": 18, "longest_edge": 1333}       )
         self.assertEqual(image_processor.do_pad		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						Any       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         UpperCAmelCase_											=prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowerCAmelCase       )
         for image in image_inputs:
            self.assertIsInstance(_lowerCAmelCase		,  Image.Image       )

         # Test not batched input
         UpperCAmelCase_											=image_processing(image_inputs[0]		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase       )

         self.assertEqual(
             encoded_images.shape		,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,  )

         # Test batched
         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase		,  batched=_lowerCAmelCase       )

         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape		,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,  )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         UpperCAmelCase_											=prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowerCAmelCase		,  numpify=_lowerCAmelCase       )
         for image in image_inputs:
            self.assertIsInstance(_lowerCAmelCase		,  np.ndarray       )

         # Test not batched input
         UpperCAmelCase_											=image_processing(image_inputs[0]		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase       )

         self.assertEqual(
             encoded_images.shape		,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,  )

         # Test batched
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase		,  batched=_lowerCAmelCase       )

         self.assertEqual(
             encoded_images.shape		,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,  )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         UpperCAmelCase_											=prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowerCAmelCase		,  torchify=_lowerCAmelCase       )
         for image in image_inputs:
            self.assertIsInstance(_lowerCAmelCase		,  torch.Tensor       )

         # Test not batched input
         UpperCAmelCase_											=image_processing(image_inputs[0]		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase       )

         self.assertEqual(
             encoded_images.shape		,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,  )

         # Test batched
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase		,  batched=_lowerCAmelCase       )

         self.assertEqual(
             encoded_images.shape		,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,  )



      @slow
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,  "r"       ) as f:
            UpperCAmelCase_											=json.loads(f.read()       )

         UpperCAmelCase_											={"image_id": 3_9769, "annotations": target}

         # encode them
         UpperCAmelCase_											=DetaImageProcessor()
         UpperCAmelCase_											=image_processing(images=_lowerCAmelCase		,  annotations=_lowerCAmelCase		,  return_tensors="pt"       )

         # verify pixel values
         UpperCAmelCase_											=torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["pixel_values"].shape		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )

         # verify area
         UpperCAmelCase_											=torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,  _lowerCAmelCase       )       )
         # verify boxes
         UpperCAmelCase_											=torch.Size([6, 4]       )
         self.assertEqual(encoding["labels"][0]["boxes"].shape		,  _lowerCAmelCase       )
         UpperCAmelCase_											=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  _lowerCAmelCase		,  atol=1e-3       )       )
         # verify image_id
         UpperCAmelCase_											=torch.tensor([3_9769]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,  _lowerCAmelCase       )       )
         # verify is_crowd
         UpperCAmelCase_											=torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,  _lowerCAmelCase       )       )
         # verify class_labels
         UpperCAmelCase_											=torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,  _lowerCAmelCase       )       )
         # verify orig_size
         UpperCAmelCase_											=torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,  _lowerCAmelCase       )       )
         # verify size
         UpperCAmelCase_											=torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  _lowerCAmelCase       )       )






      @slow
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"		,  "r"       ) as f:
            UpperCAmelCase_											=json.loads(f.read()       )

         UpperCAmelCase_											={"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

         UpperCAmelCase_											=pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

         # encode them
         UpperCAmelCase_											=DetaImageProcessor(format="coco_panoptic"       )
         UpperCAmelCase_											=image_processing(images=_lowerCAmelCase		,  annotations=_lowerCAmelCase		,  masks_path=_lowerCAmelCase		,  return_tensors="pt"       )

         # verify pixel values
         UpperCAmelCase_											=torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["pixel_values"].shape		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )

         # verify area
         UpperCAmelCase_											=torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,  _lowerCAmelCase       )       )
         # verify boxes
         UpperCAmelCase_											=torch.Size([6, 4]       )
         self.assertEqual(encoding["labels"][0]["boxes"].shape		,  _lowerCAmelCase       )
         UpperCAmelCase_											=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  _lowerCAmelCase		,  atol=1e-3       )       )
         # verify image_id
         UpperCAmelCase_											=torch.tensor([3_9769]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,  _lowerCAmelCase       )       )
         # verify is_crowd
         UpperCAmelCase_											=torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,  _lowerCAmelCase       )       )
         # verify class_labels
         UpperCAmelCase_											=torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,  _lowerCAmelCase       )       )
         # verify masks
         UpperCAmelCase_											=82_2873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,  _lowerCAmelCase       )
         # verify orig_size
         UpperCAmelCase_											=torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,  _lowerCAmelCase       )       )
         # verify size
         UpperCAmelCase_											=torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  _lowerCAmelCase       )       )











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

import os
from pathlib import Path




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={
       "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]
   UpperCAmelCase_											={
       "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"],
   }
   UpperCAmelCase_											=F'{src_lang}-{tgt_lang}'

   UpperCAmelCase_											=F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\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- 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)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$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\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
   os.makedirs(lowercase__  ,    exist_ok=lowercase__  )
   UpperCAmelCase_											=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
__lowercase  :   int						      =Path(__file__).resolve().parent.parent.parent
__lowercase  :   Any						      =repo_dir / """model_cards"""

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




