"""simple docstring"""



import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=14   ,      snake_case_=7   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=99   ,      snake_case_=32   ,      snake_case_=5   ,      snake_case_=4   ,      snake_case_=37   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=512   ,      snake_case_=16   ,      snake_case_=2   ,      snake_case_=0.02   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=None   ,      ):







										'''simple docstring'''


										A__       :     Tuple								=			parent
										A__       :     List[str]								=			batch_size
										A__       :     Dict								=			seq_length
										A__       :     Optional[int]								=			is_training
										A__       :     Optional[int]								=			use_token_type_ids
										A__       :     Dict								=			use_input_mask
										A__       :     List[Any]								=			use_labels
										A__       :     Tuple								=			use_mc_token_ids
										A__       :     List[str]								=			vocab_size
										A__       :     List[str]								=			hidden_size
										A__       :     Optional[Any]								=			num_hidden_layers
										A__       :     Dict								=			num_attention_heads
										A__       :     int								=			intermediate_size
										A__       :     Optional[int]								=			hidden_act
										A__       :     Tuple								=			hidden_dropout_prob
										A__       :     Union[str, Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			max_position_embeddings
										A__       :     int								=			type_vocab_size
										A__       :     Optional[Any]								=			type_sequence_label_size
										A__       :     List[str]								=			initializer_range
										A__       :     str								=			num_labels
										A__       :     Optional[int]								=			num_choices
										A__       :     Any								=			scope
										A__       :     Union[str, Any]								=			self.vocab_size - 1





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size							)

										A__       :     str								=			None
										if self.use_input_mask:
																	A__       :     List[Any]								=			random_attention_mask([self.batch_size, self.seq_length]							)

										A__       :     List[str]								=			None
										if self.use_token_type_ids:
																	A__       :     str								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size							)

										A__       :     Tuple								=			None
										if self.use_mc_token_ids:
																	A__       :     List[Any]								=			ids_tensor([self.batch_size, self.num_choices]   ,      self.seq_length							)

										A__       :     Any								=			None
										A__       :     Optional[int]								=			None
										A__       :     Dict								=			None
										if self.use_labels:
																	A__       :     int								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size							)
																	A__       :     Any								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels							)
																	A__       :     List[Any]								=			ids_tensor([self.batch_size]   ,      self.num_choices							)

										A__       :     Union[str, Any]								=			self.get_config()

										A__       :     Tuple								=			ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,      2							)

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										    token_type_ids,
										    mc_token_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return CTRLConfig(
										    vocab_size=self.vocab_size   ,      n_embd=self.hidden_size   ,      n_layer=self.num_hidden_layers   ,      n_head=self.num_attention_heads   ,      n_positions=self.max_position_embeddings   ,      pad_token_id=self.pad_token_id   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      *snake_case_							):







										'''simple docstring'''


										A__       :     int								=			CTRLModel(config=__UpperCAmelCase							)
										model.to(__UpperCAmelCase							)
										model.eval()

										model(__UpperCAmelCase   ,      token_type_ids=__UpperCAmelCase   ,      head_mask=__UpperCAmelCase							)
										model(__UpperCAmelCase   ,      token_type_ids=__UpperCAmelCase							)
										A__       :     Any								=			model(__UpperCAmelCase							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)							)
										self.parent.assertEqual(len(result.past_key_values							)   ,      config.n_layer							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      *snake_case_							):







										'''simple docstring'''


										A__       :     str								=			CTRLLMHeadModel(__UpperCAmelCase							)
										model.to(__UpperCAmelCase							)
										model.eval()

										A__       :     Optional[Any]								=			model(__UpperCAmelCase   ,      token_type_ids=__UpperCAmelCase   ,      labels=__UpperCAmelCase							)
										self.parent.assertEqual(result.loss.shape   ,      ()							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.prepare_config_and_inputs()

										(
										    (
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,							(
										    A__		
										)				,									
										)       :     int								=			config_and_inputs

										A__       :     List[str]								=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

										return config, inputs_dict





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      *snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.num_labels
										A__       :     Optional[Any]								=			CTRLForSequenceClassification(__UpperCAmelCase							)
										model.to(__UpperCAmelCase							)
										model.eval()
										A__       :     List[Any]								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size							)
										A__       :     List[str]								=			model(__UpperCAmelCase   ,      token_type_ids=__UpperCAmelCase   ,      labels=__UpperCAmelCase							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)							)





@require_torch
class 						__UpperCAmelCase    (A__							,   A__							,   A__							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Union[str, Any]					     =				(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
			_UpperCamelCase  :						Tuple					     =				(CTRLLMHeadModel,) if is_torch_available() else ()
			_UpperCamelCase  :						Any					     =				(
			    {
			        'feature-extraction': CTRLModel,
			        'text-classification': CTRLForSequenceClassification,
			        'text-generation': CTRLLMHeadModel,
			        'zero-shot': CTRLForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			_UpperCamelCase  :						Tuple					     =				True
			_UpperCamelCase  :						Optional[int]					     =				False
			_UpperCamelCase  :						Dict					     =				False





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

										return False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			CTRLModelTester(self							)
										A__       :     List[Any]								=			ConfigTester(self   ,      config_class=__UpperCAmelCase   ,      n_embd=37							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().tearDown()
										# clean-up as much as possible GPU memory occupied by PyTorch
										gc.collect()
										torch.cuda.empty_cache()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.config_tester.run_common_tests()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_model(*__UpperCAmelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*__UpperCAmelCase							)





			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__       :     List[str]								=			CTRLModel.from_pretrained(__UpperCAmelCase							)
																	self.assertIsNotNone(__UpperCAmelCase							)





			@unittest.skip("""The model doesn't support left padding"""							)  # and it's not used enough to be worth fixing :)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





@require_torch
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().tearDown()
										# clean-up as much as possible GPU memory occupied by PyTorch
										gc.collect()
										torch.cuda.empty_cache()





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			CTRLLMHeadModel.from_pretrained("""ctrl"""							)
										model.to(__UpperCAmelCase							)
										A__       :     List[Any]								=			torch.tensor(
										    [[11_859, 0, 1_611, 8]]   ,      dtype=torch.long   ,      device=__UpperCAmelCase							)  # Legal the president is
										A__       :     Optional[Any]								=			[
										    11_859,
										    0,
										    1_611,
										    8,
										    5,
										    150,
										    26_449,
										    2,
										    19,
										    348,
										    469,
										    3,
										    2_595,
										    48,
										    20_740,
										    246_533,
										    246_533,
										    19,
										    30,
										    5,
										]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

										A__       :     List[Any]								=			model.generate(__UpperCAmelCase   ,      do_sample=__UpperCAmelCase							)
										self.assertListEqual(output_ids[0].tolist()   ,      __UpperCAmelCase							)








"""simple docstring"""




def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   2
			while i * i <= n:
						SCREAMING_SNAKE_CASE__										=   0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   1

			while True:
						i += 1
						t_num += i

						if count_divisors(snake_case__     ) > 5_00:
									break

			return t_num


if __name__ == "__main__":
						print(solution())


