


'''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 		_snake_case					:




					def __init__(      self							,							_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     parent
								UpperCAmelCase__  :					Tuple        =     13
								UpperCAmelCase__  :					List[str]        =     7
								UpperCAmelCase__  :					Optional[int]        =     30
								UpperCAmelCase__  :					Optional[int]        =     self.seq_length + self.mem_len
								UpperCAmelCase__  :					int        =     15
								UpperCAmelCase__  :					Union[str, Any]        =     True
								UpperCAmelCase__  :					Optional[int]        =     True
								UpperCAmelCase__  :					Union[str, Any]        =     99
								UpperCAmelCase__  :					Dict        =     [10, 50, 80]
								UpperCAmelCase__  :					List[Any]        =     32
								UpperCAmelCase__  :					List[str]        =     32
								UpperCAmelCase__  :					Tuple        =     4
								UpperCAmelCase__  :					Any        =     8
								UpperCAmelCase__  :					str        =     128
								UpperCAmelCase__  :					Optional[Any]        =     2
								UpperCAmelCase__  :					Optional[int]        =     2
								UpperCAmelCase__  :					int        =     None
								UpperCAmelCase__  :					str        =     1
								UpperCAmelCase__  :					List[str]        =     0
								UpperCAmelCase__  :					Optional[Any]        =     3
								UpperCAmelCase__  :					List[Any]        =     self.vocab_size - 1
								UpperCAmelCase__  :					int        =     0.01




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)
								UpperCAmelCase__  :					List[Any]        =     ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

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

								UpperCAmelCase__  :					Union[str, Any]        =     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):
								random.seed(self.seed)
								tf.random.set_seed(self.seed)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     TFTransfoXLModel(_lowerCamelCase)

								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     model(_lowerCamelCase).to_tuple()

								UpperCAmelCase__  :					str        =     {"""input_ids""": input_ids_a, """mems""": mems_a}

								UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					int        =     TFTransfoXLLMHeadModel(_lowerCamelCase)

								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     model(_lowerCamelCase).to_tuple()

								UpperCAmelCase__  :					List[str]        =     {"""input_ids""": input_ids_a, """labels""": lm_labels}
								UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     model(_lowerCamelCase).to_tuple()

								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     model([input_ids_a, mems_a]).to_tuple()

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

								UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     TFTransfoXLForSequenceClassification(_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     model(_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.prepare_config_and_inputs()
								((UpperCAmelCase__)   ,       (UpperCAmelCase__)   ,       (UpperCAmelCase__)   ,       (UpperCAmelCase__))  :					Union[str, Any]        =     config_and_inputs
								UpperCAmelCase__  :					Optional[Any]        =     {"""input_ids""": input_ids_a}
								return config, inputs_dict


@require_tf
class 		_snake_case					(     a__       ,   a__       ,   unittest.TestCase	):
					lowerCAmelCase      :Tuple   =					(
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					lowerCAmelCase      :Dict   =					() if is_tf_available() else ()
					lowerCAmelCase      :List[Any]   =					(
					    {
					        '''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
					lowerCAmelCase      :List[str]   =					False
					lowerCAmelCase      :Any   =					False
					lowerCAmelCase      :Union[str, Any]   =					False
					lowerCAmelCase      :str   =					False




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								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):
								UpperCAmelCase__  :					int        =     TFTransfoXLModelTester(self)
								UpperCAmelCase__  :					Tuple        =     ConfigTester(self							,							config_class=_lowerCamelCase							,							d_embed=37)




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




					def 				snake_case__						(      self):
								self.model_tester.set_seed()
								UpperCAmelCase__  :					Optional[int]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase)




					def 				snake_case__						(      self):
								self.model_tester.set_seed()
								UpperCAmelCase__  :					Tuple        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Dict        =     self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase__  :					List[Any]        =     [TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
											UpperCAmelCase__  :					Tuple        =     model_class(_lowerCamelCase)
											assert isinstance(model.get_input_embeddings()							,							tf.keras.layers.Layer)
											if model_class in list_other_models_with_output_ebd:
														UpperCAmelCase__  :					Dict        =     model.get_output_embeddings()
														assert isinstance(_lowerCamelCase							,							tf.keras.layers.Layer)
														UpperCAmelCase__  :					Optional[Any]        =     model.get_bias()
														assert name is None
											else:
														UpperCAmelCase__  :					str        =     model.get_output_embeddings()
														assert x is None
														UpperCAmelCase__  :					int        =     model.get_bias()
														assert name is None




					def 				snake_case__						(      self):
								# TODO JP: Make TransfoXL XLA compliant
								pass




					@slow
					def 				snake_case__						(      self):
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase__  :					int        =     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 				snake_case__						(      self):
								pass


@require_tf
class 		_snake_case					(     unittest.TestCase	):




					@unittest.skip("""Skip test until #12651 is resolved.""")
					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""")
								# fmt: off
								UpperCAmelCase__  :					Dict        =     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__  :					Tuple        =     [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__  :					int        =     model.generate(_lowerCamelCase							,							max_length=200							,							do_sample=_lowerCamelCase)
								self.assertListEqual(output_ids[0].numpy().tolist()							,							_lowerCamelCase)



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()