





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 	__lowerCAmelCase      (       unittest.TestCase	):





   def __init__(			self       ,      snake_case       ,      snake_case=7       ,      snake_case=3       ,      snake_case=30       ,      snake_case=400       ,      snake_case=True       ,      snake_case=None       ,      snake_case=True       ,      snake_case=[0.5, 0.5, 0.5]       ,      snake_case=[0.5, 0.5, 0.5]       ,      snake_case=True       ,      snake_case=1 / 255       ,      snake_case=True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Any   		=	size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
      a__						:    Optional[int]   		=	parent
      a__						:    str   		=	batch_size
      a__						:    Any   		=	num_channels
      a__						:    Optional[int]   		=	min_resolution
      a__						:    Optional[int]   		=	max_resolution
      a__						:    List[str]   		=	do_resize
      a__						:    Dict   		=	size
      a__						:    Optional[Any]   		=	do_normalize
      a__						:    Optional[int]   		=	image_mean
      a__						:    List[str]   		=	image_std
      a__						:    Any   		=	do_rescale
      a__						:    Dict   		=	rescale_factor
      a__						:    Optional[int]   		=	do_pad





   def 						_snake_case			(			self   )					->	str:
      """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 						_snake_case			(			self       ,      snake_case       ,      snake_case=False   )					->	Optional[int]:
      """simple docstring"""





      if not batched:
         a__						:    Optional[int]   		=	image_inputs[0]
         if isinstance(snake_case       ,      Image.Image   ):
            a__		,  a__						:    List[Any]   		=	image.size
         else:
            a__		,  a__						:    Tuple   		=	image.shape[1], image.shape[2]
         if w < h:
            a__						:    Dict   		=	int(self.size["shortest_edge"] * h / w   )
            a__						:    List[Any]   		=	self.size["shortest_edge"]
         elif w > h:
            a__						:    Any   		=	self.size["shortest_edge"]
            a__						:    List[Any]   		=	int(self.size["shortest_edge"] * w / h   )
         else:
            a__						:    List[Any]   		=	self.size["shortest_edge"]
            a__						:    Tuple   		=	self.size["shortest_edge"]

      else:
         a__						:    Optional[Any]   		=	[]
         for image in image_inputs:
            a__		,  a__						:    str   		=	self.get_expected_values([image]   )
            expected_values.append((expected_height, expected_width)   )
         a__						:    Union[str, Any]   		=	max(snake_case       ,      key=lambda snake_case   : item[0]   )[0]
         a__						:    Union[str, Any]   		=	max(snake_case       ,      key=lambda snake_case   : item[1]   )[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	__lowerCAmelCase      (       _UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    Dict  		=					DetaImageProcessor if is_vision_available() else None





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Any   		=	DetaImageProcessingTester(self   )





   @property
   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(snake_case       ,      "image_mean"   )   )
      self.assertTrue(hasattr(snake_case       ,      "image_std"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_normalize"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_resize"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_rescale"   )   )
      self.assertTrue(hasattr(snake_case       ,      "do_pad"   )   )
      self.assertTrue(hasattr(snake_case       ,      "size"   )   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    Any   		=	self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size       ,      {"shortest_edge": 18, "longest_edge": 1_333}   )
      self.assertEqual(image_processor.do_pad       ,      snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      pass





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    List[str]   		=	self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      a__						:    Tuple   		=	prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=snake_case   )
      for image in image_inputs:
         self.assertIsInstance(snake_case       ,      Image.Image   )

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

      a__		,  a__						:    Dict   		=	self.image_processor_tester.get_expected_values(snake_case   )

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

      # Test batched
      a__		,  a__						:    str   		=	self.image_processor_tester.get_expected_values(snake_case       ,      batched=snake_case   )

      a__						:    Optional[int]   		=	image_processing(snake_case       ,      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 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    int   		=	self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      a__						:    Union[str, Any]   		=	prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=snake_case       ,      numpify=snake_case   )
      for image in image_inputs:
         self.assertIsInstance(snake_case       ,      np.ndarray   )

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

      a__		,  a__						:    Optional[Any]   		=	self.image_processor_tester.get_expected_values(snake_case   )

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

      # Test batched
      a__						:    str   		=	image_processing(snake_case       ,      return_tensors="pt"   ).pixel_values

      a__		,  a__						:    Tuple   		=	self.image_processor_tester.get_expected_values(snake_case       ,      batched=snake_case   )

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





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      a__						:    List[Any]   		=	prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=snake_case       ,      torchify=snake_case   )
      for image in image_inputs:
         self.assertIsInstance(snake_case       ,      torch.Tensor   )

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

      a__		,  a__						:    int   		=	self.image_processor_tester.get_expected_values(snake_case   )

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

      # Test batched
      a__						:    Optional[int]   		=	image_processing(snake_case       ,      return_tensors="pt"   ).pixel_values

      a__		,  a__						:    Any   		=	self.image_processor_tester.get_expected_values(snake_case       ,      batched=snake_case   )

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





   @slow
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





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

      a__						:    Dict   		=	{"image_id": 39_769, "annotations": target}

      # encode them
      a__						:    List[Any]   		=	DetaImageProcessor()
      a__						:    Tuple   		=	image_processing(images=snake_case       ,      annotations=snake_case       ,      return_tensors="pt"   )

      # verify pixel values
      a__						:    Any   		=	torch.Size([1, 3, 800, 1_066]   )
      self.assertEqual(encoding["pixel_values"].shape       ,      snake_case   )

      a__						:    List[str]   		=	torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
      self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,      snake_case       ,      atol=1E-4   )   )

      # verify area
      a__						:    List[Any]   		=	torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      snake_case   )   )
      # verify boxes
      a__						:    str   		=	torch.Size([6, 4]   )
      self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      snake_case   )
      a__						:    List[Any]   		=	torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      snake_case       ,      atol=1E-3   )   )
      # verify image_id
      a__						:    int   		=	torch.tensor([39_769]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      snake_case   )   )
      # verify is_crowd
      a__						:    Optional[int]   		=	torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      snake_case   )   )
      # verify class_labels
      a__						:    Optional[Any]   		=	torch.tensor([75, 75, 63, 65, 17, 17]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      snake_case   )   )
      # verify orig_size
      a__						:    int   		=	torch.tensor([480, 640]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      snake_case   )   )
      # verify size
      a__						:    List[Any]   		=	torch.tensor([800, 1_066]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      snake_case   )   )





   @slow
   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
      with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,      "r"   ) as f:
         a__						:    List[str]   		=	json.loads(f.read()   )

      a__						:    int   		=	{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

      a__						:    List[str]   		=	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"   )

      # encode them
      a__						:    Tuple   		=	DetaImageProcessor(format="coco_panoptic"   )
      a__						:    List[Any]   		=	image_processing(images=snake_case       ,      annotations=snake_case       ,      masks_path=snake_case       ,      return_tensors="pt"   )

      # verify pixel values
      a__						:    List[str]   		=	torch.Size([1, 3, 800, 1_066]   )
      self.assertEqual(encoding["pixel_values"].shape       ,      snake_case   )

      a__						:    str   		=	torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
      self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,      snake_case       ,      atol=1E-4   )   )

      # verify area
      a__						:    Tuple   		=	torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      snake_case   )   )
      # verify boxes
      a__						:    str   		=	torch.Size([6, 4]   )
      self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      snake_case   )
      a__						:    int   		=	torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      snake_case       ,      atol=1E-3   )   )
      # verify image_id
      a__						:    Tuple   		=	torch.tensor([39_769]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      snake_case   )   )
      # verify is_crowd
      a__						:    Union[str, Any]   		=	torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      snake_case   )   )
      # verify class_labels
      a__						:    Optional[Any]   		=	torch.tensor([17, 17, 63, 75, 75, 93]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      snake_case   )   )
      # verify masks
      a__						:    List[Any]   		=	822_873
      self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,      snake_case   )
      # verify orig_size
      a__						:    int   		=	torch.tensor([480, 640]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      snake_case   )   )
      # verify size
      a__						:    Dict   		=	torch.tensor([800, 1_066]   )
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      snake_case   )   )








import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


SCREAMING_SNAKE_CASE__							:      List[Any]         =    """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

SCREAMING_SNAKE_CASE__							:      int         =    """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION	)
class 	__lowerCAmelCase      (       datasets.Metric	):





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      if version.parse(scb.__version__   ) < version.parse("1.4.12"   ):
         raise ImportWarning(
             "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
             "You can install it with `pip install \"sacrebleu>=1.4.12\"`."   )
      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      homepage="http://www.cs.umd.edu/~snover/tercom/"       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
              {
                  "predictions": datasets.Value("string"       ,      id="sequence"   ),
                  "references": datasets.Sequence(datasets.Value("string"       ,      id="sequence"   )       ,      id="references"   ),
              }   )       ,      codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]       ,      reference_urls=[
              "https://github.com/jhclark/tercom",
          ]       ,      )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    int   		=	len(references[0]   )
      if any(len(snake_case   ) != references_per_prediction for refs in references   ):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"   )
      a__						:    int   		=	[[refs[i] for refs in references] for i in range(snake_case   )]

      a__						:    Optional[Any]   		=	TER(
          normalized=snake_case       ,      no_punct=snake_case       ,      asian_support=snake_case       ,      case_sensitive=snake_case       ,      )
      a__						:    Union[str, Any]   		=	sb_ter.corpus_score(snake_case       ,      snake_case   )

      return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}

