
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    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=14				,			__UpperCamelCase=7				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=99				,			__UpperCamelCase=32				,			__UpperCamelCase=5				,			__UpperCamelCase=4				,			__UpperCamelCase=37				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.1				,			__UpperCamelCase=512				,			__UpperCamelCase=16				,			__UpperCamelCase=2				,			__UpperCamelCase=0.02				,			__UpperCamelCase=3				,			__UpperCamelCase=4				,			__UpperCamelCase=None				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					seq_length
									_lowercase					       =					is_training
									_lowercase					       =					use_token_type_ids
									_lowercase					       =					use_input_mask
									_lowercase					       =					use_labels
									_lowercase					       =					use_mc_token_ids
									_lowercase					       =					vocab_size
									_lowercase					       =					hidden_size
									_lowercase					       =					num_hidden_layers
									_lowercase					       =					num_attention_heads
									_lowercase					       =					intermediate_size
									_lowercase					       =					hidden_act
									_lowercase					       =					hidden_dropout_prob
									_lowercase					       =					attention_probs_dropout_prob
									_lowercase					       =					max_position_embeddings
									_lowercase					       =					type_vocab_size
									_lowercase					       =					type_sequence_label_size
									_lowercase					       =					initializer_range
									_lowercase					       =					num_labels
									_lowercase					       =					num_choices
									_lowercase					       =					scope
									_lowercase					       =					self.vocab_size - 1






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size  )

									_lowercase					       =					None
									if self.use_input_mask:
															_lowercase					       =					random_attention_mask([self.batch_size, self.seq_length]  )

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

									_lowercase					       =					None
									if self.use_mc_token_ids:
															_lowercase					       =					ids_tensor([self.batch_size, self.num_choices]				,			self.seq_length  )

									_lowercase					       =					None
									_lowercase					       =					None
									_lowercase					       =					None
									if self.use_labels:
															_lowercase					       =					ids_tensor([self.batch_size]				,			self.type_sequence_label_size  )
															_lowercase					       =					ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels  )
															_lowercase					       =					ids_tensor([self.batch_size]				,			self.num_choices  )

									_lowercase					       =					self.get_config()

									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					CTRLModel(config=__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

									model(__UpperCamelCase				,			token_type_ids=__UpperCamelCase				,			head_mask=__UpperCamelCase  )
									model(__UpperCamelCase				,			token_type_ids=__UpperCamelCase  )
									_lowercase					       =					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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					CTRLLMHeadModel(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.prepare_config_and_inputs()

									(
									    (
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						
									)					       =					config_and_inputs

									_lowercase					       =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

									return config, inputs_dict






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					self.num_labels
									_lowercase					       =					CTRLForSequenceClassification(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()
									_lowercase					       =					ids_tensor([self.batch_size]				,			self.type_sequence_label_size  )
									_lowercase					       =					model(__UpperCamelCase				,			token_type_ids=__UpperCamelCase				,			labels=__UpperCamelCase  )
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)  )







@require_torch
class    a_ (			_a					,			_a					,			_a					,			unittest.TestCase    ):
			a	:			Tuple					   =							(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
			a	:			List[Any]					   =							(CTRLLMHeadModel,) if is_torch_available() else ()
			a	:			Tuple					   =							(
			    {
			        '''feature-extraction''': CTRLModel,
			        '''text-classification''': CTRLForSequenceClassification,
			        '''text-generation''': CTRLLMHeadModel,
			        '''zero-shot''': CTRLForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			a	:			List[str]					   =							True
			a	:			Union[str, Any]					   =							False
			a	:			List[Any]					   =							False






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									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    UpperCamelCase_				(      self  ):
									_lowercase					       =					CTRLModelTester(self  )
									_lowercase					       =					ConfigTester(self				,			config_class=__UpperCamelCase				,			n_embd=37  )






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






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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_model(*__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									pass






			@slow
			def    UpperCamelCase_				(      self  ):
									for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase					       =					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    UpperCamelCase_				(      self  ):
									pass







@require_torch
class    a_ (			unittest.TestCase    ):






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






			@slow
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					CTRLLMHeadModel.from_pretrained("""ctrl"""  )
									model.to(__UpperCamelCase  )
									_lowercase					       =					torch.tensor(
									    [[11_859, 0, 1_611, 8]]				,			dtype=torch.long				,			device=__UpperCamelCase  )  # Legal the president is
									_lowercase					       =					[
									    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

									_lowercase					       =					model.generate(__UpperCamelCase				,			do_sample=__UpperCamelCase  )
									self.assertListEqual(output_ids[0].tolist()				,			__UpperCamelCase  )

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     bool:
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_lowercase					       =					True

						# sum is not zero and set is empty then false
						for i in range(1		,   required_sum + 1       ):
												_lowercase					       =					False

						for i in range(1		,   arr_len + 1       ):
												for j in range(1		,   required_sum + 1       ):
																		if arr[i - 1] > j:
																								_lowercase					       =					subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_lowercase					       =					subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()