




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__    :
				'''simple docstring'''







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


				def      _A				(				self: Union[str, Any]		):
											SCREAMING_SNAKE_CASE_ 						=     ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size		)
											SCREAMING_SNAKE_CASE_ 						=     ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size		)

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

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


				def      _A				(				self: List[str]		,       _lowerCamelCase: Dict		,       _lowerCamelCase: List[Any]		,       _lowerCamelCase: List[Any]		,       _lowerCamelCase: Dict		):
											SCREAMING_SNAKE_CASE_ 						=     TFTransfoXLModel(_UpperCAmelCase		)

											SCREAMING_SNAKE_CASE_ 						=     model(_UpperCAmelCase		).to_tuple()

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

											SCREAMING_SNAKE_CASE_ 						=     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      _A				(				self: Any		,       _lowerCamelCase: Optional[int]		,       _lowerCamelCase: List[Any]		,       _lowerCamelCase: Tuple		,       _lowerCamelCase: Any		):
											SCREAMING_SNAKE_CASE_ 						=     TFTransfoXLLMHeadModel(_UpperCAmelCase		)

											SCREAMING_SNAKE_CASE_ 						=     model(_UpperCAmelCase		).to_tuple()

											SCREAMING_SNAKE_CASE_ 						=     {'input_ids': input_ids_a, 'labels': lm_labels}
											SCREAMING_SNAKE_CASE_ 						=     model(_UpperCAmelCase		).to_tuple()

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

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

											SCREAMING_SNAKE_CASE_ 						=     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      _A				(				self: List[Any]		,       _lowerCamelCase: Dict		,       _lowerCamelCase: Optional[int]		,       _lowerCamelCase: Tuple		,       _lowerCamelCase: Tuple		):
											SCREAMING_SNAKE_CASE_ 						=     TFTransfoXLForSequenceClassification(_UpperCAmelCase		)
											SCREAMING_SNAKE_CASE_ 						=     model(_UpperCAmelCase		)
											self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)		)





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






@require_tf
class 			__magic_name__    (	_UpperCamelCase     ,	_UpperCamelCase     ,	unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				Optional[int]						   =       (
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				SCREAMING_SNAKE_CASE__       :				Tuple						   =       () if is_tf_available() else ()
				SCREAMING_SNAKE_CASE__       :				int						   =       (
				    {
				        '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__       :				Dict						   =       False
				SCREAMING_SNAKE_CASE__       :				Dict						   =       False
				SCREAMING_SNAKE_CASE__       :				str						   =       False
				SCREAMING_SNAKE_CASE__       :				List[str]						   =       False


				def      _A				(				self: Any		,       _lowerCamelCase: Union[str, Any]		,       _lowerCamelCase: List[Any]		,       _lowerCamelCase: Union[str, Any]		,       _lowerCamelCase: List[str]		,       _lowerCamelCase: Any		):
											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: List[str]		):
											SCREAMING_SNAKE_CASE_ 						=     TFTransfoXLModelTester(self		)
											SCREAMING_SNAKE_CASE_ 						=     ConfigTester(self		,       config_class=_UpperCAmelCase		,       d_embed=37		)


				def      _A				(				self: Optional[Any]		):
											self.config_tester.run_common_tests()


				def      _A				(				self: Optional[int]		):
											self.model_tester.set_seed()
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_model(*_UpperCAmelCase		)


				def      _A				(				self: Dict		):
											self.model_tester.set_seed()
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_lm_head(*_UpperCAmelCase		)


				def      _A				(				self: List[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_UpperCAmelCase		)


				def      _A				(				self: Any		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE_ 						=     [TFTransfoXLForSequenceClassification]

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


				def      _A				(				self: Optional[Any]		):
											# TODO JP: Make TransfoXL XLA compliant
											pass


				@slow
				def      _A				(				self: Any		):
											for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE_ 						=     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      _A				(				self: Optional[int]		):
											pass






@require_tf
class 			__magic_name__    (	unittest.TestCase):
				'''simple docstring'''







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

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







'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


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




