






import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase__							=     """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__							=     importlib.util.spec_from_file_location(
    """transformers""",
    os.path.join(PATH_TO_TRANSFORMERS, """__init__.py"""),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowerCamelCase__							=     spec.loader.load_module()

lowerCamelCase__							=     transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCamelCase__							=     re.compile("""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


lowerCamelCase__							=     {
    """CLIPConfigMixin""",
    """DecisionTransformerConfigMixin""",
    """EncoderDecoderConfigMixin""",
    """RagConfigMixin""",
    """SpeechEncoderDecoderConfigMixin""",
    """VisionEncoderDecoderConfigMixin""",
    """VisionTextDualEncoderConfigMixin""",
}






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]

     for config_class in list(CONFIG_MAPPING.values()  ):
          __snake_case			:Optional[Any]          =						False

          # source code of `config_class`
          __snake_case			:Dict          =						inspect.getsource(snake_case__  )
          __snake_case			:int          =						_re_checkpoint.findall(snake_case__  )

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __snake_case       , __snake_case			:Union[str, Any]          =						checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __snake_case			:Any          =						f'''https://huggingface.co/{ckpt_name}'''
               if ckpt_link == ckpt_link_from_name:
                    __snake_case			:Optional[int]          =						True
                    break

          __snake_case			:Tuple          =						config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(snake_case__  )

     if len(snake_case__  ) > 0:
          __snake_case			:Dict          =						"""\n""".join(sorted(snake_case__  )  )
          raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}'''  )


if __name__ == "__main__":
     check_config_docstrings_have_checkpoints()










import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    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 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				List[Any] 				= MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       lowerCamelCase							:				Tuple 				= 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							:				List[str] 				= {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						ZeroShotClassificationPipeline(
                model=a__			,						tokenizer=a__			,						candidate_labels=["""polics""", """health"""]    )
            return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Tuple          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels="""politics"""    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            # No kwarg
            __snake_case			:Optional[int]          =						classifier("""Who are you voting for in 2020?"""			,						["""politics"""]    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            __snake_case			:Tuple          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels=["""politics"""]    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            __snake_case			:List[str]          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels="""politics, public health"""    )
            self.assertEqual(
                a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}    )
            self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]    )    )			,						1.0    )

            __snake_case			:Optional[Any]          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health"""]    )
            self.assertEqual(
                a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}    )
            self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]    )    )			,						1.0    )

            __snake_case			:List[str]          =						classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels="""politics"""			,						hypothesis_template="""This text is about {}"""    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            # https://github.com/huggingface/transformers/issues/13846
            __snake_case			:Union[str, Any]          =						classifier(["""I am happy"""]			,						["""positive""", """negative"""]    )
            self.assertEqual(
                a__			,						[
                    {"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}
                    for i in range(1    )
                ]			,						)
            __snake_case			:Tuple          =						classifier(["""I am happy""", """I am sad"""]			,						["""positive""", """negative"""]    )
            self.assertEqual(
                a__			,						[
                    {"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}
                    for i in range(2    )
                ]			,						)

            with self.assertRaises(a__    ):
                 classifier(""""""			,						candidate_labels="""politics"""    )

            with self.assertRaises(a__    ):
                 classifier(a__			,						candidate_labels="""politics"""    )

            with self.assertRaises(a__    ):
                 classifier("""Who are you voting for in 2020?"""			,						candidate_labels=""""""    )

            with self.assertRaises(a__    ):
                 classifier("""Who are you voting for in 2020?"""			,						candidate_labels=a__    )

            with self.assertRaises(a__    ):
                 classifier(
                     """Who are you voting for in 2020?"""			,						candidate_labels="""politics"""			,						hypothesis_template="""Not formatting template"""			,						)

            with self.assertRaises(a__    ):
                 classifier(
                     """Who are you voting for in 2020?"""			,						candidate_labels="""politics"""			,						hypothesis_template=a__			,						)

            self.run_entailment_id(a__    )



       def 					__lowercase						(							self			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						zero_shot_classifier.model.config
            __snake_case			:Optional[Any]          =						config.labelaid
            __snake_case			:Dict          =						zero_shot_classifier.entailment_id

            __snake_case			:List[Any]          =						{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
            self.assertEqual(zero_shot_classifier.entailment_id			,						-1    )

            __snake_case			:Optional[int]          =						{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
            self.assertEqual(zero_shot_classifier.entailment_id			,						0    )

            __snake_case			:List[Any]          =						{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
            self.assertEqual(zero_shot_classifier.entailment_id			,						0    )

            __snake_case			:str          =						{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
            self.assertEqual(zero_shot_classifier.entailment_id			,						2    )

            __snake_case			:Union[str, Any]          =						original_labelaid
            self.assertEqual(a__			,						zero_shot_classifier.entailment_id    )



       @require_torch
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:str          =						pipeline(
                """zero-shot-classification"""			,						model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""			,						framework="""pt"""			,						)
            # There was a regression in 4.10 for this
            # Adding a test so we don't make the mistake again.
            # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
            zero_shot_classifier(
                """Who are you voting for in 2020?""" * 1_00			,						candidate_labels=["""politics""", """public health""", """science"""]    )



       @require_torch
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Dict          =						pipeline(
                """zero-shot-classification"""			,						model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""			,						framework="""pt"""			,						)
            __snake_case			:Tuple          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""science""", """public health""", """politics"""],
                    """scores""": [0.3_33, 0.3_33, 0.3_33],
                }			,						)



       @require_tf
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						pipeline(
                """zero-shot-classification"""			,						model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""			,						framework="""tf"""			,						)
            __snake_case			:List[Any]          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""science""", """public health""", """politics"""],
                    """scores""": [0.3_33, 0.3_33, 0.3_33],
                }			,						)



       @slow
       @require_torch
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						pipeline("""zero-shot-classification"""			,						model="""roberta-large-mnli"""			,						framework="""pt"""    )
            __snake_case			:Optional[int]          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""politics""", """public health""", """science"""],
                    """scores""": [0.9_76, 0.0_15, 0.0_09],
                }			,						)
            __snake_case			:Union[str, Any]          =						zero_shot_classifier(
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
                """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
                """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
                """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
                """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
                """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
                """ English-to-German translation task, improving over the existing best results, including ensembles by"""
                """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
                """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
                """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
                """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
                """ large and limited training data."""			,						candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]			,						multi_label=a__			,						)
            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": (
                        """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                        """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                        """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                        """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                        """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                        """ superior in quality while being more parallelizable and requiring significantly less time to"""
                        """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                        """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                        """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                        """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                        """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                        """ other tasks by applying it successfully to English constituency parsing both with large and"""
                        """ limited training data."""
                    ),
                    """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                    """scores""": [0.8_17, 0.7_13, 0.0_18, 0.0_18],
                }			,						)



       @slow
       @require_tf
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Tuple          =						pipeline("""zero-shot-classification"""			,						model="""roberta-large-mnli"""			,						framework="""tf"""    )
            __snake_case			:Optional[int]          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""politics""", """public health""", """science"""],
                    """scores""": [0.9_76, 0.0_15, 0.0_09],
                }			,						)
            __snake_case			:Union[str, Any]          =						zero_shot_classifier(
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
                """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
                """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
                """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
                """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
                """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
                """ English-to-German translation task, improving over the existing best results, including ensembles by"""
                """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
                """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
                """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
                """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
                """ large and limited training data."""			,						candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]			,						multi_label=a__			,						)
            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": (
                        """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                        """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                        """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                        """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                        """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                        """ superior in quality while being more parallelizable and requiring significantly less time to"""
                        """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                        """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                        """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                        """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                        """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                        """ other tasks by applying it successfully to English constituency parsing both with large and"""
                        """ limited training data."""
                    ),
                    """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                    """scores""": [0.8_17, 0.7_13, 0.0_18, 0.0_18],
                }			,						)


