






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.
UpperCAmelCase__				   =		{"LayoutLMv2Config", "LayoutLMv3Config"}






@is_pipeline_test
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      __snake_case       =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						Optional[Any]  )    ->Optional[int]:




       """simple docstring"""


       a		  =      ZeroShotClassificationPipeline(
           model=__UpperCAmelCase ,					tokenizer=__UpperCAmelCase ,					candidate_labels=['''polics''', '''health''']  )
       return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						List[Any]  )    ->int:




       """simple docstring"""


       a		  =      classifier('''Who are you voting for in 2020?''' ,					candidate_labels='''politics'''  )
       self.assertEqual(__UpperCAmelCase ,					{'''sequence''': ANY(__UpperCAmelCase  ), '''labels''': [ANY(__UpperCAmelCase  )], '''scores''': [ANY(__UpperCAmelCase  )]}  )

       # No kwarg
       a		  =      classifier('''Who are you voting for in 2020?''' ,					['''politics''']  )
       self.assertEqual(__UpperCAmelCase ,					{'''sequence''': ANY(__UpperCAmelCase  ), '''labels''': [ANY(__UpperCAmelCase  )], '''scores''': [ANY(__UpperCAmelCase  )]}  )

       a		  =      classifier('''Who are you voting for in 2020?''' ,					candidate_labels=['''politics''']  )
       self.assertEqual(__UpperCAmelCase ,					{'''sequence''': ANY(__UpperCAmelCase  ), '''labels''': [ANY(__UpperCAmelCase  )], '''scores''': [ANY(__UpperCAmelCase  )]}  )

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

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

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

       # https://github.com/huggingface/transformers/issues/13846
       a		  =      classifier(['''I am happy'''] ,					['''positive''', '''negative''']  )
       self.assertEqual(
           __UpperCAmelCase ,					[
               {'''sequence''': ANY(__UpperCAmelCase  ), '''labels''': [ANY(__UpperCAmelCase  ), ANY(__UpperCAmelCase  )], '''scores''': [ANY(__UpperCAmelCase  ), ANY(__UpperCAmelCase  )]}
               for i in range(1  )
           ] ,					)
       a		  =      classifier(['''I am happy''', '''I am sad'''] ,					['''positive''', '''negative''']  )
       self.assertEqual(
           __UpperCAmelCase ,					[
               {'''sequence''': ANY(__UpperCAmelCase  ), '''labels''': [ANY(__UpperCAmelCase  ), ANY(__UpperCAmelCase  )], '''scores''': [ANY(__UpperCAmelCase  ), ANY(__UpperCAmelCase  )]}
               for i in range(2  )
           ] ,					)

       with self.assertRaises(__UpperCAmelCase  ):
        classifier('''''' ,					candidate_labels='''politics'''  )

       with self.assertRaises(__UpperCAmelCase  ):
        classifier(__UpperCAmelCase ,					candidate_labels='''politics'''  )

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

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

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

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

       self.run_entailment_id(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Pipeline  )    ->int:




       """simple docstring"""


       a		  =      zero_shot_classifier.model.config
       a		  =      config.labelaid
       a		  =      zero_shot_classifier.entailment_id

       a		  =      {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
       self.assertEqual(zero_shot_classifier.entailment_id ,					-1  )

       a		  =      {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
       self.assertEqual(zero_shot_classifier.entailment_id ,					0  )

       a		  =      {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
       self.assertEqual(zero_shot_classifier.entailment_id ,					0  )

       a		  =      {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
       self.assertEqual(zero_shot_classifier.entailment_id ,					2  )

       a		  =      original_labelaid
       self.assertEqual(__UpperCAmelCase ,					zero_shot_classifier.entailment_id  )







      @require_torch
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->str:




       """simple docstring"""


       a		  =      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?''' * 100 ,					candidate_labels=['''politics''', '''public health''', '''science''']  )







      @require_torch
      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[Any]:




       """simple docstring"""


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

       self.assertEqual(
           nested_simplify(__UpperCAmelCase  ) ,					{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''science''', '''public health''', '''politics'''],
               '''scores''': [0.333, 0.333, 0.333],
           } ,					)







      @require_tf
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


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

       self.assertEqual(
           nested_simplify(__UpperCAmelCase  ) ,					{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''science''', '''public health''', '''politics'''],
               '''scores''': [0.333, 0.333, 0.333],
           } ,					)







      @slow
      @require_torch
      def       __lowerCAmelCase					(      self      :						int  )    ->List[Any]:




       """simple docstring"""


       a		  =      pipeline('''zero-shot-classification''' ,					model='''roberta-large-mnli''' ,					framework='''pt'''  )
       a		  =      zero_shot_classifier(
           '''Who are you voting for in 2020?''' ,					candidate_labels=['''politics''', '''public health''', '''science''']  )

       self.assertEqual(
           nested_simplify(__UpperCAmelCase  ) ,					{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''politics''', '''public health''', '''science'''],
               '''scores''': [0.976, 0.015, 0.009],
           } ,					)
       a		  =      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=__UpperCAmelCase ,					)
       self.assertEqual(
           nested_simplify(__UpperCAmelCase  ) ,					{
               '''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''': [0.817, 0.713, 0.018, 0.018],
           } ,					)







      @slow
      @require_tf
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      pipeline('''zero-shot-classification''' ,					model='''roberta-large-mnli''' ,					framework='''tf'''  )
       a		  =      zero_shot_classifier(
           '''Who are you voting for in 2020?''' ,					candidate_labels=['''politics''', '''public health''', '''science''']  )

       self.assertEqual(
           nested_simplify(__UpperCAmelCase  ) ,					{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''politics''', '''public health''', '''science'''],
               '''scores''': [0.976, 0.015, 0.009],
           } ,					)
       a		  =      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=__UpperCAmelCase ,					)
       self.assertEqual(
           nested_simplify(__UpperCAmelCase  ) ,					{
               '''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''': [0.817, 0.713, 0.018, 0.018],
           } ,					)










from math import factorial

UpperCAmelCase__				   =		{str(digit): factorial(digit) for digit in range(10)}
def 						_a    (      a :int )							->			int:
 if not isinstance(a	,							a ):
  raise TypeError('''Parameter number must be int''' )

 if number < 0:
  raise ValueError('''Parameter number must be greater than or equal to 0''' )

 # Converts number in string to iterate on its digits and adds its factorial.
 return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )
def 						_a    (      a :int = 60	,							a :int = 1_000_000 )							->			int:

 if not isinstance(a	,							a ) or not isinstance(a	,							a ):
  raise TypeError('''Parameters chain_length and number_limit must be int''' )

 if chain_length <= 0 or number_limit <= 0:
  raise ValueError(
      '''Parameters chain_length and number_limit must be greater than 0''' )

 # the counter for the chains with the exact desired length
 a		  =      0
 # the cached sizes of the previous chains
 a		  =      {}

 for start_chain_element in range(1	,							a ):
  # The temporary set will contain the elements of the chain
  a		  =      set()
  a		  =      0

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  a		  =      start_chain_element
  while (
      chain_element not in chain_sets_lengths
      and chain_element not in chain_set
      and chain_set_length <= chain_length
  ):
   chain_set.add(a )
   chain_set_length += 1
   a		  =      digit_factorial_sum(a )

  if chain_element in chain_sets_lengths:
   chain_set_length += chain_sets_lengths[chain_element]

  a		  =      chain_set_length

  # If chain contains the exact amount of elements increase the counter
  if chain_set_length == chain_length:
   chains_counter += 1

 return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f"""{solution()}""")


