


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCamelCase        =     {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}


@is_pipeline_test
class   a__	(						unittest.TestCase    ):
       lowerCamelCase__:     List[Any]							    =     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       lowerCamelCase__:     Optional[int]							    =     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              lowerCamelCase__:     List[Any]							    =     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              lowerCamelCase__:     Tuple							    =     {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }

       @require_torch
       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     str										=    pipeline(
                task="""text-classification"""      ,							model="""hf-internal-testing/tiny-random-distilbert"""      ,							framework="""pt"""    )

            a_    :     Union[str, Any]										=    text_classifier("""This is great !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

            a_    :     Optional[int]										=    text_classifier("""This is great !"""      ,							top_k=2    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]    )

            a_    :     Optional[Any]										=    text_classifier(["""This is great !""", """This is bad"""]      ,							top_k=2    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[
                    [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
                    [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
                ]      ,							)

            a_    :     Dict										=    text_classifier("""This is great !"""      ,							top_k=1    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

            # Legacy behavior
            a_    :     Tuple										=    text_classifier("""This is great !"""      ,							return_all_scores=lowerCamelCase_    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

            a_    :     Dict										=    text_classifier("""This is great !"""      ,							return_all_scores=lowerCamelCase_    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]]    )

            a_    :     Union[str, Any]										=    text_classifier(["""This is great !""", """Something else"""]      ,							return_all_scores=lowerCamelCase_    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[
                    [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
                    [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
                ]      ,							)

            a_    :     List[str]										=    text_classifier(["""This is great !""", """Something else"""]      ,							return_all_scores=lowerCamelCase_    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[
                    {"""label""": """LABEL_0""", """score""": 0.5_0_4},
                    {"""label""": """LABEL_0""", """score""": 0.5_0_4},
                ]      ,							)

       @require_torch
       def 						UpperCAmelCase(   self      :     Dict    ):
            import torch

            a_    :     List[Any]										=    pipeline(
                task="""text-classification"""      ,							model="""hf-internal-testing/tiny-random-distilbert"""      ,							framework="""pt"""      ,							device=torch.device("""cpu"""    )      ,							)

            a_    :     Any										=    text_classifier("""This is great !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

       @require_tf
       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Tuple										=    pipeline(
                task="""text-classification"""      ,							model="""hf-internal-testing/tiny-random-distilbert"""      ,							framework="""tf"""    )

            a_    :     Optional[Any]										=    text_classifier("""This is great !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

       @slow
       @require_torch
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     List[str]										=    pipeline("""text-classification"""    )

            a_    :     Optional[int]										=    text_classifier("""This is great !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """POSITIVE""", """score""": 1.0}]    )
            a_    :     int										=    text_classifier("""This is bad !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """NEGATIVE""", """score""": 1.0}]    )
            a_    :     Dict										=    text_classifier("""Birds are a type of animal"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]    )

       @slow
       @require_tf
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Union[str, Any]										=    pipeline("""text-classification"""      ,							framework="""tf"""    )

            a_    :     Optional[int]										=    text_classifier("""This is great !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """POSITIVE""", """score""": 1.0}]    )
            a_    :     Tuple										=    text_classifier("""This is bad !"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """NEGATIVE""", """score""": 1.0}]    )
            a_    :     Tuple										=    text_classifier("""Birds are a type of animal"""    )
            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Tuple    ):
            a_    :     Tuple										=    TextClassificationPipeline(model=lowerCamelCase_      ,							tokenizer=lowerCamelCase_    )
            return text_classifier, ["HuggingFace is in", "This is another test"]

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     List[Any]										=    text_classifier.model
            # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
            a_    :     List[Any]										=    """HuggingFace is in"""
            a_    :     Optional[int]										=    text_classifier(lowerCamelCase_    )

            self.assertEqual(nested_simplify(lowerCamelCase_    )      ,							[{"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}]    )
            self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()    )

            a_    :     int										=    ["""HuggingFace is in """, """Paris is in France"""]
            a_    :     Optional[Any]										=    text_classifier(lowerCamelCase_    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[{"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}, {"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}]      ,							)
            self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()    )
            self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()    )

            # Forcing to get all results with `top_k=None`
            # This is NOT the legacy format
            a_    :     List[Any]										=    text_classifier(lowerCamelCase_      ,							top_k=lowerCamelCase_    )
            a_    :     Optional[int]										=    len(model.config.idalabel.values()    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[[{"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}] * N, [{"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}] * N]      ,							)

            a_    :     Dict										=    {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
            a_    :     List[str]										=    text_classifier(lowerCamelCase_    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							{"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}      ,							)
            self.assertTrue(outputs["""label"""] in model.config.idalabel.values()    )

            # This might be used a text pair, but tokenizer + pipe interaction
            # makes it hard to understand that it's not using the pair properly
            # https://github.com/huggingface/transformers/issues/17305
            # We disabled this usage instead as it was outputting wrong outputs.
            a_    :     int										=    [["""HuggingFace is in """, """Paris is in France"""]]
            with self.assertRaises(lowerCamelCase_    ):
                 text_classifier(lowerCamelCase_    )

            # This used to be valid for doing text pairs
            # We're keeping it working because of backward compatibility
            a_    :     Union[str, Any]										=    text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]    )
            self.assertEqual(
                nested_simplify(lowerCamelCase_    )      ,							[{"""label""": ANY(lowerCamelCase_    ), """score""": ANY(lowerCamelCase_    )}]      ,							)
            self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()    )







from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase        =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]



def  _a			(						):
     a_    :     List[str]										=    Github(os.environ["""GITHUB_TOKEN"""] )
     a_    :     str										=    g.get_repo("""huggingface/transformers""" )
     a_    :     List[str]										=    repo.get_issues(state="""open""" )

     for issue in open_issues:
          a_    :     Optional[Any]										=    sorted([comment for comment in issue.get_comments()]  ,  key=lambda __UpperCamelCase : i.created_at  ,  reverse=__UpperCamelCase )
          a_    :     List[Any]										=    comments[0] if len(__UpperCamelCase ) > 0 else None
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and (dt.utcnow() - issue.updated_at).days > 7
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
               issue.edit(state="""closed""" )
          elif (
              (dt.utcnow() - issue.updated_at).days > 2_3
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would add stale comment to {issue.number}")
               issue.create_comment(
                   """This issue has been automatically marked as stale because it has not had """
                   """recent activity. If you think this still needs to be addressed """
                   """please comment on this thread.\n\nPlease note that issues that do not follow the """
                   """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                   """are likely to be ignored.""" )


if __name__ == "__main__":
       main()



