

'''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 						__magic_name__   :







 def __init__(       self  , snake_case_  , ):
   lowercase									=parent
   lowercase									=13
   lowercase									=7
   lowercase									=30
   lowercase									=self.seq_length + self.mem_len
   lowercase									=15
   lowercase									=True
   lowercase									=True
   lowercase									=99
   lowercase									=[10, 50, 80]
   lowercase									=32
   lowercase									=32
   lowercase									=4
   lowercase									=8
   lowercase									=1_28
   lowercase									=2
   lowercase									=2
   lowercase									=None
   lowercase									=1
   lowercase									=0
   lowercase									=3
   lowercase									=self.vocab_size - 1
   lowercase									=0.01







 def 						_A(       self   ):
   lowercase									=ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size   )
   lowercase									=ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size   )

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

   lowercase									=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 						_A(       self   ):
   random.seed(self.seed   )
   tf.random.set_seed(self.seed   )







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase									=TFTransfoXLModel(__lowerCamelCase   )

   lowercase							, lowercase									=model(__lowerCamelCase   ).to_tuple()

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

   lowercase							, lowercase									=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 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase									=TFTransfoXLLMHeadModel(__lowerCamelCase   )

   lowercase							, lowercase									=model(__lowerCamelCase   ).to_tuple()

   lowercase									={'''input_ids''': input_ids_a, '''labels''': lm_labels}
   lowercase							, lowercase									=model(__lowerCamelCase   ).to_tuple()

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

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

   lowercase							, lowercase									=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 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   lowercase									=TFTransfoXLForSequenceClassification(__lowerCamelCase   )
   lowercase									=model(__lowerCamelCase   )
   self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_labels)   )







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

@require_tf
class 						__magic_name__   (							__snake_case ,				__snake_case ,				unittest.TestCase ):
 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 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):







   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 						_A(       self   ):
   lowercase									=TFTransfoXLModelTester(self   )
   lowercase									=ConfigTester(self  , config_class=__lowerCamelCase  , d_embed=37   )







 def 						_A(       self   ):
   self.config_tester.run_common_tests()







 def 						_A(       self   ):
   self.model_tester.set_seed()
   lowercase									=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_transfo_xl_model(*__lowerCamelCase   )







 def 						_A(       self   ):
   self.model_tester.set_seed()
   lowercase									=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_transfo_xl_lm_head(*__lowerCamelCase   )







 def 						_A(       self   ):
   lowercase									=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__lowerCamelCase   )







 def 						_A(       self   ):
   lowercase							, lowercase									=self.model_tester.prepare_config_and_inputs_for_common()
   lowercase									=[TFTransfoXLForSequenceClassification]

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







 def 						_A(       self   ):
   pass







 @slow
 def 						_A(       self   ):







   for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase									=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 						_A(       self   ):
   pass

@require_tf
class 						__magic_name__   (							unittest.TestCase ):







 @unittest.skip('''Skip test until #12651 is resolved.'''   )
 @slow
 def 						_A(       self   ):
   lowercase									=TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''   )
   # fmt: off
   lowercase									=tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]  , 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
   lowercase									=[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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>

   lowercase									=model.generate(__lowerCamelCase  , max_length=2_00  , do_sample=__lowerCamelCase   )
   self.assertListEqual(output_ids[0].numpy().tolist()  , __lowerCamelCase   )




from __future__ import annotations
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   # Checks if the entire collection has been sorted
   if len(lowerCAmelCase_ ) <= 1 or n <= 1:
      return

   insert_next(lowerCAmelCase_	,    n - 1 )
   rec_insertion_sort(lowerCAmelCase_	,    n - 1 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   # Checks order between adjacent elements
   if index >= len(lowerCAmelCase_ ) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   UpperCAmelCase					,	UpperCAmelCase      					=  (
       collection[index],
       collection[index - 1],
   )

   insert_next(lowerCAmelCase_	,    index + 1 )


if __name__ == "__main__":
 __a	 =      input("""Enter integers separated by spaces: """)
 __a	 =      [int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)



