





'''simple docstring'''


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.
a__			:					Union[str, Any]       						=						{'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       _lowerCamelCase								=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              _lowerCamelCase								={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              _lowerCamelCase								={
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }

       @require_torch
       def 			__snake_case      ( self					:				Dict						):
              UpperCAmelCase  =						pipeline(
                  task='''text-classification'''       ,     model='''hf-internal-testing/tiny-random-distilbert'''       ,     framework='''pt'''						)

              UpperCAmelCase  =						text_classifier('''This is great !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''LABEL_0''', '''score''': 0.504}]						)

              UpperCAmelCase  =						text_classifier('''This is great !'''       ,     top_k=2						)
              self.assertEqual(
                  nested_simplify(a__						)       ,     [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]						)

              UpperCAmelCase  =						text_classifier(['''This is great !''', '''This is bad''']       ,     top_k=2						)
              self.assertEqual(
                  nested_simplify(a__						)       ,     [
                      [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
                      [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
                  ]       ,     )

              UpperCAmelCase  =						text_classifier('''This is great !'''       ,     top_k=1						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''LABEL_0''', '''score''': 0.504}]						)

              # Legacy behavior
              UpperCAmelCase  =						text_classifier('''This is great !'''       ,     return_all_scores=a__						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''LABEL_0''', '''score''': 0.504}]						)

              UpperCAmelCase  =						text_classifier('''This is great !'''       ,     return_all_scores=a__						)
              self.assertEqual(
                  nested_simplify(a__						)       ,     [[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]						)

              UpperCAmelCase  =						text_classifier(['''This is great !''', '''Something else''']       ,     return_all_scores=a__						)
              self.assertEqual(
                  nested_simplify(a__						)       ,     [
                      [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
                      [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
                  ]       ,     )

              UpperCAmelCase  =						text_classifier(['''This is great !''', '''Something else''']       ,     return_all_scores=a__						)
              self.assertEqual(
                  nested_simplify(a__						)       ,     [
                      {'''label''': '''LABEL_0''', '''score''': 0.504},
                      {'''label''': '''LABEL_0''', '''score''': 0.504},
                  ]       ,     )

       @require_torch
       def 			__snake_case      ( self					:				Union[str, Any]						):
              import torch

              UpperCAmelCase  =						pipeline(
                  task='''text-classification'''       ,     model='''hf-internal-testing/tiny-random-distilbert'''       ,     framework='''pt'''       ,     device=torch.device('''cpu'''						)       ,     )

              UpperCAmelCase  =						text_classifier('''This is great !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''LABEL_0''', '''score''': 0.504}]						)

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

              UpperCAmelCase  =						text_classifier('''This is great !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''LABEL_0''', '''score''': 0.504}]						)

       @slow
       @require_torch
       def 			__snake_case      ( self					:				Any						):
              UpperCAmelCase  =						pipeline('''text-classification'''						)

              UpperCAmelCase  =						text_classifier('''This is great !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''POSITIVE''', '''score''': 1.0}]						)
              UpperCAmelCase  =						text_classifier('''This is bad !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''NEGATIVE''', '''score''': 1.0}]						)
              UpperCAmelCase  =						text_classifier('''Birds are a type of animal'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''POSITIVE''', '''score''': 0.988}]						)

       @slow
       @require_tf
       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						pipeline('''text-classification'''       ,     framework='''tf'''						)

              UpperCAmelCase  =						text_classifier('''This is great !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''POSITIVE''', '''score''': 1.0}]						)
              UpperCAmelCase  =						text_classifier('''This is bad !'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''NEGATIVE''', '''score''': 1.0}]						)
              UpperCAmelCase  =						text_classifier('''Birds are a type of animal'''						)
              self.assertEqual(nested_simplify(a__						)       ,     [{'''label''': '''POSITIVE''', '''score''': 0.988}]						)

       def 			__snake_case      ( self					:				Optional[Any]       ,     a__					:				Tuple       ,     a__					:				List[Any]       ,     a__					:				Any						):
              UpperCAmelCase  =						TextClassificationPipeline(model=a__       ,     tokenizer=a__						)
              return text_classifier, ["HuggingFace is in", "This is another test"]



       def 			__snake_case      ( self					:				Any       ,     a__					:				int       ,     a__					:				Tuple						):
              UpperCAmelCase  =						text_classifier.model
              # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
              UpperCAmelCase  =						'''HuggingFace is in'''
              UpperCAmelCase  =						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  =						['''HuggingFace is in ''', '''Paris is in France''']
              UpperCAmelCase  =						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  =						text_classifier(a__       ,     top_k=a__						)
              UpperCAmelCase  =						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  =						{'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
              UpperCAmelCase  =						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  =						[['''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  =						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()						)




from decimal import Decimal, getcontext
from math import ceil, factorial

def  __lowerCamelCase					( _lowercase			)       ->      str:

     if not isinstance(_lowercase						,     _lowercase			):
          raise TypeError('Undefined for non-integers'			)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'			)

     UpperCamelCase						    =					precision
     UpperCamelCase						    =					ceil(precision / 14			)
     UpperCamelCase						    =					426880 * Decimal(10005			).sqrt()
     UpperCamelCase						    =					1
     UpperCamelCase						    =					13591409
     UpperCamelCase						    =					Decimal(_lowercase			)
     for k in range(1						,     _lowercase			):
          UpperCamelCase						    =					factorial(6 * k			) // (factorial(3 * k			) * factorial(_lowercase			) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
     return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
 _snake_case      = 50
 print(F"The first {n} digits of pi is: {pi(n)}")


