





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLModel,
  )


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Any      ,				UpperCAmelCase__   :			int      ,				)		->List[str]:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					13
           A__   				=					7
           A__   				=					30
           A__   				=					self.seq_length + self.mem_len
           A__   				=					15
           A__   				=					True
           A__   				=					True
           A__   				=					99
           A__   				=					[10, 50, 80]
           A__   				=					32
           A__   				=					32
           A__   				=					4
           A__   				=					8
           A__   				=					128
           A__   				=					2
           A__   				=					2
           A__   				=					None
           A__   				=					1
           A__   				=					0
           A__   				=					3
           A__   				=					self.vocab_size - 1
           A__   				=					0.01


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[Any]:





           '''simple docstring'''

           A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)
           A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)

           A__   				=					None
           if self.use_labels:
                  A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)

           A__   				=					TransfoXLConfig(
               vocab_size=self.vocab_size      ,				mem_len=self.mem_len      ,				clamp_len=self.clamp_len      ,				cutoffs=self.cutoffs      ,				d_model=self.hidden_size      ,				d_embed=self.d_embed      ,				n_head=self.num_attention_heads      ,				d_head=self.d_head      ,				d_inner=self.d_inner      ,				div_val=self.div_val      ,				n_layer=self.num_hidden_layers      ,				eos_token_id=self.eos_token_id      ,				pad_token_id=self.vocab_size - 1      ,				init_range=self.init_range      ,				num_labels=self.num_labels      ,				)

           return (config, input_ids_a, input_ids_a, lm_labels)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Dict:





           '''simple docstring'''

           random.seed(self.seed)
           tf.random.set_seed(self.seed)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->Dict:





           '''simple docstring'''

           A__   				=					TFTransfoXLModel(lowerCAmelCase_)

           A__				,							A__   				=					model(lowerCAmelCase_).to_tuple()

           A__   				=					{'''input_ids''': input_ids_a, '''mems''': mems_a}

           A__				,							A__   				=					model(lowerCAmelCase_).to_tuple()

           self.parent.assertEqual(hidden_states_a.shape      ,				(self.batch_size, self.seq_length, self.hidden_size))
           self.parent.assertEqual(hidden_states_a.shape      ,				(self.batch_size, self.seq_length, self.hidden_size))
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					TFTransfoXLLMHeadModel(lowerCAmelCase_)

           A__				,							A__   				=					model(lowerCAmelCase_).to_tuple()

           A__   				=					{'''input_ids''': input_ids_a, '''labels''': lm_labels}
           A__				,							A__   				=					model(lowerCAmelCase_).to_tuple()

           A__				,							A__   				=					model([input_ids_a, mems_a]).to_tuple()

           A__   				=					{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

           A__				,							A__   				=					model(lowerCAmelCase_).to_tuple()

           self.parent.assertEqual(lm_logits_a.shape      ,				(self.batch_size, self.seq_length, self.vocab_size))
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)

           self.parent.assertEqual(lm_logits_a.shape      ,				(self.batch_size, self.seq_length, self.vocab_size))
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]      ,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

           A__   				=					TFTransfoXLForSequenceClassification(lowerCAmelCase_)
           A__   				=					model(lowerCAmelCase_)
           self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels))





    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->List[str]:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()
           ((A__)				,							(A__)				,							(A__)				,							(A__))   				=					config_and_inputs
           A__   				=					{'''input_ids''': input_ids_a}
           return config, inputs_dict


@require_tf
class        UpperCamelCase_					(				__UpperCAmelCase	,      __UpperCAmelCase	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    UpperCAmelCase__													=   () if is_tf_available() else ()
    UpperCAmelCase__													=   (
        {
            '''feature-extraction''': TFTransfoXLModel,
            '''text-classification''': TFTransfoXLForSequenceClassification,
            '''text-generation''': TFTransfoXLLMHeadModel,
            '''zero-shot''': TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any])		->List[str]:





           '''simple docstring'''

           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                  # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
                  # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
                  # tokenizer.
                  return True

           return False


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           A__   				=					TFTransfoXLModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=lowerCAmelCase_      ,				d_embed=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Union[str, Any]:





           '''simple docstring'''

           self.model_tester.set_seed()
           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase_)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Dict:





           '''simple docstring'''

           self.model_tester.set_seed()
           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase_)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[Any]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase_)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Any:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           A__   				=					[TFTransfoXLForSequenceClassification]

           for model_class in self.all_model_classes:
                  A__   				=					model_class(lowerCAmelCase_)
                  assert isinstance(model.get_input_embeddings()      ,				tf.keras.layers.Layer)
                  if model_class in list_other_models_with_output_ebd:
                         A__   				=					model.get_output_embeddings()
                         assert isinstance(lowerCAmelCase_      ,				tf.keras.layers.Layer)
                         A__   				=					model.get_bias()
                         assert name is None
                  else:
                         A__   				=					model.get_output_embeddings()
                         assert x is None
                         A__   				=					model.get_bias()
                         assert name is None


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					TFTransfoXLModel.from_pretrained(lowerCAmelCase_)
                  self.assertIsNotNone(lowerCAmelCase_)





    @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Optional[Any]:





           '''simple docstring'''

           pass


@require_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @unittest.skip('''Skip test until #12651 is resolved.''')
    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''')
           # fmt: off
           A__   				=					tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]      ,				dtype=tf.intaa)  # noqa: E231
           # fmt: on
           #  In 1991 , the remains of Russian Tsar Nicholas II and his family
           #  ( except for Alexei and Maria ) are discovered .
           #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
           #  remainder of the story . 1883 Western Siberia ,
           #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
           #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
           #  father initially slaps him for making such an accusation , Rasputin watches as the
           #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
           #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
           #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

           # fmt: off
           A__   				=					[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
           # fmt: on
           #  In 1991, the remains of Russian Tsar Nicholas II and his family (
           #  except for Alexei and Maria ) are discovered. The voice of young son,
           #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
           #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
           #  and a group of men to perform magic. Rasputin has a vision and
           #  denounces one of the men as a horse thief. Although his father initially
           #  slaps him for making such an accusation, Rasputin watches as the man
           #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
           #  of the Virgin Mary, prompting him to become a priest.
           #  Rasputin quickly becomes famous, with people, even a bishop, begging for
           #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
           # Nicholas II and his family were discovered. The voice of <unk> young son,
           # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

           A__   				=					model.generate(lowerCAmelCase_      ,				max_length=200      ,				do_sample=lowerCAmelCase_)
           self.assertListEqual(output_ids[0].numpy().tolist()      ,				lowerCAmelCase_)








def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					1
       for i in range(1				,       num + 1		):
              fact *= i
       return fact







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					0
       while number > 0:
              A__   				=					number % 10
              sum_of_digits += last_digit
              A__   				=					number // 10  # Removing the last_digit from the given number
       return sum_of_digits







def SCREAMING_SNAKE_CASE       ( lowercase_ = 100		)     -> int:



       """simple docstring"""

       A__   				=					factorial(lowercase_		)
       A__   				=					split_and_add(lowercase_		)
       return result


if __name__ == "__main__":
  print(solution(int(input("""Enter the Number: """).strip())))


