





"""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 __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	13
      __UpperCamelCase      =	7
      __UpperCamelCase      =	30
      __UpperCamelCase      =	self.seq_length + self.mem_len
      __UpperCamelCase      =	15
      __UpperCamelCase      =	True
      __UpperCamelCase      =	True
      __UpperCamelCase      =	99
      __UpperCamelCase      =	[10, 50, 80]
      __UpperCamelCase      =	32
      __UpperCamelCase      =	32
      __UpperCamelCase      =	4
      __UpperCamelCase      =	8
      __UpperCamelCase      =	128
      __UpperCamelCase      =	2
      __UpperCamelCase      =	2
      __UpperCamelCase      =	None
      __UpperCamelCase      =	1
      __UpperCamelCase      =	0
      __UpperCamelCase      =	3
      __UpperCamelCase      =	self.vocab_size - 1
      __UpperCamelCase      =	0.0_1





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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

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

      __UpperCamelCase      =	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 						UpperCAmelCase (     self				):

      '''simple docstring'''




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





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	TFTransfoXLModel(SCREAMING_SNAKE_CASE_				)

      __UpperCamelCase      =	model(SCREAMING_SNAKE_CASE_				).to_tuple()

      __UpperCamelCase      =	{"""input_ids""": input_ids_a, """mems""": mems_a}

      __UpperCamelCase      =	model(SCREAMING_SNAKE_CASE_				).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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE_				)

      __UpperCamelCase      =	model(SCREAMING_SNAKE_CASE_				).to_tuple()

      __UpperCamelCase      =	{"""input_ids""": input_ids_a, """labels""": lm_labels}
      __UpperCamelCase      =	model(SCREAMING_SNAKE_CASE_				).to_tuple()

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

      __UpperCamelCase      =	{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

      __UpperCamelCase      =	model(SCREAMING_SNAKE_CASE_				).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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE_				)
      __UpperCamelCase      =	model(SCREAMING_SNAKE_CASE_				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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


@require_tf
class __lowerCAmelCase		(				_UpperCAmelCase      ,  _UpperCAmelCase      ,  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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFTransfoXLModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=SCREAMING_SNAKE_CASE_     ,					d_embed=37				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.model_tester.set_seed()
      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE_				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.model_tester.set_seed()
      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE_				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE_				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase      =	[TFTransfoXLForSequenceClassification]

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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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







     @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass


@require_tf
class __lowerCAmelCase		(				unittest.TestCase      ):





     @unittest.skip('Skip test until #12651 is resolved.'				)
     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'				)
      # fmt: off
      __UpperCamelCase      =	tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]     ,					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
      __UpperCamelCase      =	[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # 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>

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





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 __UpperCamelCase      =	0
 while n > 0:
  res += n % 1_0
  n //= 1_0
 return res






def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0		)






def 							A						(    snake_case				:int		)				->     int:
 return sum(int(snake_case		) for c in str(abs(snake_case		)		)		)






def 							A						(    )				->     None:
 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(snake_case				:Callable    , snake_case				:int		) -> None:
  __UpperCamelCase      =	f'{func.__name__}({value})'
  __UpperCamelCase      =	timeit(f'__main__.{call}'    , setup='import __main__'		)
  print(f'{call:56} = {func(snake_case		)} -- {timing:.4f} seconds'		)

 for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(snake_case    , snake_case		)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



