

'''simple docstring'''







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    A__ :



    def __init__(   self						,			UpperCamelCase__						,			)  -> 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    snake_case_     (   self    )  -> Dict:





           '''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    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           random.seed(self.seed    )
           tf.random.set_seed(self.seed    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           A_		   = TFTransfoXLModel(UpperCamelCase__    )

           A_		   = model(UpperCamelCase__    ).to_tuple()

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

           A_		   = model(UpperCamelCase__    ).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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           A_		   = TFTransfoXLLMHeadModel(UpperCamelCase__    )

           A_		   = model(UpperCamelCase__    ).to_tuple()

           A_		   = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
           A_		   = model(UpperCamelCase__    ).to_tuple()

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

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

           A_		   = model(UpperCamelCase__    ).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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           A_		   = TFTransfoXLForSequenceClassification(UpperCamelCase__    )
           A_		   = model(UpperCamelCase__    )
           self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)    )







    def    snake_case_     (   self    )  -> Optional[Any]:





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


@require_tf
class    A__ ( _snake_case  ,  _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						(
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    lowercase		   =						() if is_tf_available() else ()
    lowercase		   =						(
        {
            "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
    lowercase		   =						False
    lowercase		   =						False
    lowercase		   =						False
    lowercase		   =						False



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> 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    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = TFTransfoXLModelTester(self    )
           A_		   = ConfigTester(self						,			config_class=UpperCamelCase__						,			d_embed=37    )



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           self.model_tester.set_seed()
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__    )



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





           '''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(*UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__    )



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





           '''simple docstring'''
           A_		   = self.model_tester.prepare_config_and_inputs_for_common()
           A_		   = [TFTransfoXLForSequenceClassification]

           for model_class in self.all_model_classes:
                  A_		   = model_class(UpperCamelCase__    )
                  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(UpperCamelCase__						,			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    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           # TODO JP: Make TransfoXL XLA compliant
           pass



    @slow
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_		   = TFTransfoXLModel.from_pretrained(UpperCamelCase__    )
                  self.assertIsNotNone(UpperCamelCase__    )







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





           '''simple docstring'''
           pass


@require_tf
class    A__ ( unittest.TestCase     ):



    @unittest.skip("""Skip test until #12651 is resolved."""    )
    @slow
    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""    )
           # fmt: off
           A_		   = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]						,			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_		   = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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(UpperCamelCase__						,			max_length=200						,			do_sample=UpperCamelCase__    )
           self.assertListEqual(output_ids[0].numpy().tolist()						,			UpperCamelCase__    )



'''simple docstring'''







import math
import sys
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if number != int(UpperCAmelCase__			):
              raise ValueError("""the value of input must be a natural number"""			)
       if number < 0:
              raise ValueError("""the value of input must not be a negative number"""			)
       if number == 0:
              return 1
       A_		   = [-1] * (number + 1)
       A_		   = 0
       for i in range(1,    number + 1			):
              A_		   = sys.maxsize
              A_		   = int(math.sqrt(UpperCAmelCase__			)			)
              for j in range(1,    root + 1			):
                     A_		   = 1 + answers[i - (j**2)]
                     A_		   = min(UpperCAmelCase__,    UpperCAmelCase__			)
              A_		   = answer
       return answers[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
