




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowerCamelCase			:			str								=       get_tests_dir('fixtures')




class 		__lowercase						(unittest.TestCase				):




      """simple docstring"""





      def       UpperCAmelCase ( self      )						->							Optional[Any]:
         snake_case :							str           =			mock.Mock()
         snake_case :							Dict           =			5_0_0
         snake_case :							Optional[int]           =			{}
         snake_case :							Dict           =			HTTPError
         snake_case :							List[str]           =			{}

         # Download this model to make sure it's in the cache.
         snake_case :							Dict           =			ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""      )
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch("""requests.Session.request"""							, return_value=_SCREAMING_SNAKE_CASE      ) as mock_head:
            snake_case :							List[Any]           =			ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""      )
            # This check we did call the fake head request
            mock_head.assert_called()

      def       UpperCAmelCase ( self      )						->							List[str]:
         snake_case :							Optional[Any]           =			ViTImageProcessor.from_pretrained(
             """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""      )

      def       UpperCAmelCase ( self      )						->							int:

         with self.assertRaises(_SCREAMING_SNAKE_CASE      ):
            # config is in subfolder, the following should not work without specifying the subfolder
            snake_case :							Dict           =			AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""      )

         snake_case :							Optional[int]           =			AutoImageProcessor.from_pretrained(
             """hf-internal-testing/stable-diffusion-all-variants"""							, subfolder="""feature_extractor"""      )

         self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )




@is_staging_test
class 		__lowercase						(unittest.TestCase				):




      """simple docstring"""





      @classmethod
      def       UpperCAmelCase ( cls      )						->							Optional[int]:
         snake_case :							str           =			TOKEN
         HfFolder.save_token(_SCREAMING_SNAKE_CASE      )

      @classmethod
      def       UpperCAmelCase ( cls      )						->							Optional[Any]:

         try:
            delete_repo(token=cls._token							, repo_id="""test-image-processor"""      )
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token							, repo_id="""valid_org/test-image-processor-org"""      )
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token							, repo_id="""test-dynamic-image-processor"""      )
         except HTTPError:
            pass

      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         snake_case :							int           =			ViTImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE      )
         image_processor.push_to_hub("""test-image-processor"""							, use_auth_token=self._token      )

         snake_case :							Tuple           =			ViTImageProcessor.from_pretrained(f"""{USER}/test-image-processor"""      )
         for k, v in image_processor.__dict__.items():
            self.assertEqual(_SCREAMING_SNAKE_CASE							, getattr(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      )      )

         # Reset repo
         delete_repo(token=self._token							, repo_id="""test-image-processor"""      )

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                _SCREAMING_SNAKE_CASE							, repo_id="""test-image-processor"""							, push_to_hub=_SCREAMING_SNAKE_CASE							, use_auth_token=self._token      )

         snake_case :							List[Any]           =			ViTImageProcessor.from_pretrained(f"""{USER}/test-image-processor"""      )
         for k, v in image_processor.__dict__.items():
            self.assertEqual(_SCREAMING_SNAKE_CASE							, getattr(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      )      )

      def       UpperCAmelCase ( self      )						->							Optional[Any]:
         snake_case :							List[Any]           =			ViTImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE      )
         image_processor.push_to_hub("""valid_org/test-image-processor"""							, use_auth_token=self._token      )

         snake_case :							int           =			ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""      )
         for k, v in image_processor.__dict__.items():
            self.assertEqual(_SCREAMING_SNAKE_CASE							, getattr(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      )      )

         # Reset repo
         delete_repo(token=self._token							, repo_id="""valid_org/test-image-processor"""      )

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                _SCREAMING_SNAKE_CASE							, repo_id="""valid_org/test-image-processor-org"""							, push_to_hub=_SCREAMING_SNAKE_CASE							, use_auth_token=self._token      )

         snake_case :							Union[str, Any]           =			ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""      )
         for k, v in image_processor.__dict__.items():
            self.assertEqual(_SCREAMING_SNAKE_CASE							, getattr(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      )      )

      def       UpperCAmelCase ( self      )						->							Any:
         CustomImageProcessor.register_for_auto_class()
         snake_case :							Tuple           =			CustomImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE      )

         image_processor.push_to_hub("""test-dynamic-image-processor"""							, use_auth_token=self._token      )

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             image_processor.auto_map							, {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}							, )

         snake_case :							Dict           =			AutoImageProcessor.from_pretrained(
             f"""{USER}/test-dynamic-image-processor"""							, trust_remote_code=_SCREAMING_SNAKE_CASE      )
         # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
         self.assertEqual(new_image_processor.__class__.__name__							, """CustomImageProcessor"""      )








'''simple docstring'''



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

import datasets


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

_UpperCamelCase						:  Any							       =				"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_UpperCamelCase						:  Dict							       =				"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"



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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = False      , _SCREAMING_SNAKE_CASE = False      , _SCREAMING_SNAKE_CASE = False      , _SCREAMING_SNAKE_CASE = False      , ):





      '''simple docstring'''

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

      lowerCAmelCase 						=							TER(
          normalized=_SCREAMING_SNAKE_CASE      , no_punct=_SCREAMING_SNAKE_CASE      , asian_support=_SCREAMING_SNAKE_CASE      , case_sensitive=_SCREAMING_SNAKE_CASE      , )
      lowerCAmelCase 						=							sb_ter.corpus_score(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

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





