


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




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Dict										=	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size   )
							a						:Optional[Any]										=	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size   )

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

							a						: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 	SCREAMING_SNAKE_CASE__    (		self   ):
							random.seed(self.seed   )
							tf.random.set_seed(self.seed   )




	def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
							a						:int										=	TFTransfoXLModel(_lowerCamelCase   )

							a						:List[Any]										=	model(_lowerCamelCase   ).to_tuple()

							a						:List[str]										=	{'''input_ids''': input_ids_a, '''mems''': mems_a}

							a						:Optional[int]										=	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 ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
							a						:str										=	TFTransfoXLLMHeadModel(_lowerCamelCase   )

							a						:Tuple										=	model(_lowerCamelCase   ).to_tuple()

							a						:Any										=	{'''input_ids''': input_ids_a, '''labels''': lm_labels}
							a						:Dict										=	model(_lowerCamelCase   ).to_tuple()

							a						:Dict										=	model([input_ids_a, mems_a]   ).to_tuple()

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

							a						:Any										=	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 ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
							a						:Optional[Any]										=	TFTransfoXLForSequenceClassification(_lowerCamelCase   )
							a						:Any										=	model(_lowerCamelCase   )
							self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:str										=	self.prepare_config_and_inputs()
							(a)						:Optional[int]										=	config_and_inputs
							a						:Union[str, Any]										=	{'''input_ids''': input_ids_a}
							return config, inputs_dict







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




	def 	SCREAMING_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 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:str										=	TFTransfoXLModelTester(self   )
							a						:str										=	ConfigTester(self ,  config_class=_lowerCamelCase ,  d_embed=37   )




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




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							self.model_tester.set_seed()
							a						:str										=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							self.model_tester.set_seed()
							a						:Dict										=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:List[Any]										=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Any										=	self.model_tester.prepare_config_and_inputs_for_common()
							a						:int										=	[TFTransfoXLForSequenceClassification]

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




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




	@slow
	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													a						:List[Any]										=	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   ):
							pass







@require_tf
class 		_snake_case  (				unittest.TestCase							):




	@unittest.skip('''Skip test until #12651 is resolved.'''   )
	@slow
	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Any										=	TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''   )
							# fmt: off
							a						:Union[str, Any]										=	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
							a						:List[Any]										=	[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>

							a						:Optional[Any]										=	model.generate(_lowerCamelCase ,  max_length=200 ,  do_sample=_lowerCamelCase   )
							self.assertListEqual(output_ids[0].numpy().tolist() ,  _lowerCamelCase   )









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





