





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       :     Any   =     {"""LayoutLMv2Config""", """LayoutLMv3Config"""}



@is_pipeline_test
class 					A__   (       unittest.TestCase			):
      _UpperCAmelCase	:int						    =   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _UpperCAmelCase	:Any						    =   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   pipeline(
           task="text-classification"       ,							model="hf-internal-testing/tiny-random-distilbert"       ,							framework="pt"   )

       UpperCamelCase	:					str       	=   text_classifier("This is great !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "LABEL_0", "score": 0.5_04}]   )

       UpperCamelCase	:					Optional[Any]       	=   text_classifier("This is great !"       ,							top_k=2   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]   )

       UpperCamelCase	:					str       	=   text_classifier(["This is great !", "This is bad"]       ,							top_k=2   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[
               [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
               [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
           ]       ,							)

       UpperCamelCase	:					str       	=   text_classifier("This is great !"       ,							top_k=1   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "LABEL_0", "score": 0.5_04}]   )

       # Legacy behavior
       UpperCamelCase	:					str       	=   text_classifier("This is great !"       ,							return_all_scores=A_   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "LABEL_0", "score": 0.5_04}]   )

       UpperCamelCase	:					Any       	=   text_classifier("This is great !"       ,							return_all_scores=A_   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]   )

       UpperCamelCase	:					Optional[Any]       	=   text_classifier(["This is great !", "Something else"]       ,							return_all_scores=A_   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[
               [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
               [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
           ]       ,							)

       UpperCamelCase	:					Union[str, Any]       	=   text_classifier(["This is great !", "Something else"]       ,							return_all_scores=A_   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[
               {"label": "LABEL_0", "score": 0.5_04},
               {"label": "LABEL_0", "score": 0.5_04},
           ]       ,							)





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import torch

       UpperCamelCase	:					Tuple       	=   pipeline(
           task="text-classification"       ,							model="hf-internal-testing/tiny-random-distilbert"       ,							framework="pt"       ,							device=torch.device("cpu"   )       ,							)

       UpperCamelCase	:					str       	=   text_classifier("This is great !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "LABEL_0", "score": 0.5_04}]   )





      @require_tf
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   pipeline(
           task="text-classification"       ,							model="hf-internal-testing/tiny-random-distilbert"       ,							framework="tf"   )

       UpperCamelCase	:					Tuple       	=   text_classifier("This is great !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "LABEL_0", "score": 0.5_04}]   )





      @slow
      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   pipeline("text-classification"   )

       UpperCamelCase	:					Any       	=   text_classifier("This is great !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "POSITIVE", "score": 1.0}]   )
       UpperCamelCase	:					Dict       	=   text_classifier("This is bad !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "NEGATIVE", "score": 1.0}]   )
       UpperCamelCase	:					List[Any]       	=   text_classifier("Birds are a type of animal"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "POSITIVE", "score": 0.9_88}]   )





      @slow
      @require_tf
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   pipeline("text-classification"       ,							framework="tf"   )

       UpperCamelCase	:					Dict       	=   text_classifier("This is great !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "POSITIVE", "score": 1.0}]   )
       UpperCamelCase	:					Dict       	=   text_classifier("This is bad !"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "NEGATIVE", "score": 1.0}]   )
       UpperCamelCase	:					Optional[int]       	=   text_classifier("Birds are a type of animal"   )
       self.assertEqual(nested_simplify(A_   )       ,							[{"label": "POSITIVE", "score": 0.9_88}]   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   TextClassificationPipeline(model=A_       ,							tokenizer=A_   )
       return text_classifier, ["HuggingFace is in", "This is another test"]





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       UpperCamelCase	:					Optional[int]       	=   "HuggingFace is in"
       UpperCamelCase	:					List[Any]       	=   text_classifier(A_   )

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

       UpperCamelCase	:					Union[str, Any]       	=   ["HuggingFace is in ", "Paris is in France"]
       UpperCamelCase	:					Optional[int]       	=   text_classifier(A_   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[{"label": ANY(A_   ), "score": ANY(A_   )}, {"label": ANY(A_   ), "score": ANY(A_   )}]       ,							)
       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
       UpperCamelCase	:					List[str]       	=   text_classifier(A_       ,							top_k=A_   )
       UpperCamelCase	:					int       	=   len(model.config.idalabel.values()   )
       self.assertEqual(
           nested_simplify(A_   )       ,							[[{"label": ANY(A_   ), "score": ANY(A_   )}] * N, [{"label": ANY(A_   ), "score": ANY(A_   )}] * N]       ,							)

       UpperCamelCase	:					Optional[int]       	=   {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
       UpperCamelCase	:					List[Any]       	=   text_classifier(A_   )
       self.assertEqual(
           nested_simplify(A_   )       ,							{"label": ANY(A_   ), "score": ANY(A_   )}       ,							)
       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.
       UpperCamelCase	:					int       	=   [["HuggingFace is in ", "Paris is in France"]]
       with self.assertRaises(A_   ):
        text_classifier(A_   )

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







def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					List[Any]       	=   ""
 for ch in key:
  if ch == " " or ch not in key_no_dups and ch.isalpha():
   key_no_dups += ch
 return key_no_dups



def 		A_				(     _lowerCAmelCase     )						-> dict[str, str]:
 UpperCamelCase	:					Optional[Any]       	=   [chr(i + 65     ) for i in range(26     )]
 # Remove duplicate characters from key
 UpperCamelCase	:					Tuple       	=   remove_duplicates(key.upper()     )
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )
 # First fill cipher with key characters
 UpperCamelCase	:					int       	=   {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase     )}
 # Then map remaining characters in alphabet to
 # the alphabet from the beginning
 for i in range(len(_lowerCAmelCase     )							,   26     ):
  UpperCamelCase	:					Optional[Any]       	=   alphabet[i - offset]
  # Ensure we are not mapping letters to letters previously mapped
  while char in key:
   offset -= 1
   UpperCamelCase	:					List[str]       	=   alphabet[i - offset]
  UpperCamelCase	:					List[Any]       	=   char
 return cipher_alphabet



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 return "".join(cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     )						-> None:
 UpperCamelCase	:					int       	=   input("Enter message to encode or decode: "     ).strip()
 UpperCamelCase	:					str       	=   input("Enter keyword: "     ).strip()
 UpperCamelCase	:					Union[str, Any]       	=   input("Encipher or decipher? E/D:"     ).strip()[0].lower()
 try:
  UpperCamelCase	:					List[str]       	=   {"e": encipher, "d": decipher}[option]
 except KeyError:
  raise KeyError("invalid input option"     )
 UpperCamelCase	:					str       	=   create_cipher_map(_lowerCAmelCase     )
 print(func(_lowerCAmelCase							,   _lowerCAmelCase     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
