






def 						__UpperCAmelCase			(     a_							,     a_):
							assert x is not None
							assert y is not None

							snake_case_				=							len(a_)
							snake_case_				=							len(a_)

							# declaring the array for storing the dp values
							snake_case_				=							[[0] * (n + 1) for _ in range(m + 1)]  # noqa: E741

							for i in range(1							,     m + 1):
														for j in range(1							,     n + 1):
																					snake_case_				=							1 if x[i - 1] == y[j - 1] else 0

																					snake_case_				=							max(l[i - 1][j]							,     l[i][j - 1]							,     l[i - 1][j - 1] + match)

							snake_case_				=							''
							snake_case_			,		snake_case_				=							m, n
							while i > 0 and j > 0:
														snake_case_				=							1 if x[i - 1] == y[j - 1] else 0

														if l[i][j] == l[i - 1][j - 1] + match:
																					if match == 1:
																												snake_case_				=							x[i - 1] + seq
																					i -= 1
																					j -= 1
														elif l[i][j] == l[i - 1][j]:
																					i -= 1
														else:
																					j -= 1

							return l[m][n], seq


if __name__ == "__main__":
	lowercase  							=					"AGGTAB"
	lowercase  							=					"GXTXAYB"
	lowercase  							=					4
	lowercase  							=					"GTAB"

	lowercase							,       lowercase  							=					longest_common_subsequence(a, b)
	print("len =", ln, ", sub-sequence =", subseq)
	import doctest

	doctest.testmod()













import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
	    GPTNeoXForCausalLM,
	    GPTNeoXForQuestionAnswering,
	    GPTNeoXForSequenceClassification,
	    GPTNeoXForTokenClassification,
	    GPTNeoXModel,
	)




class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=64       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=3       ,			a=4       ,			a=None       ,			)       ->    str:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_input_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_labels
								snake_case_				=							num_choices
								snake_case_				=							scope
								snake_case_				=							vocab_size - 1




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

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

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

								snake_case_				=							self.get_config()

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self					)       ->    int:
								return GPTNeoXConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			pad_token_id=self.pad_token_id       ,			)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.prepare_config_and_inputs()

								snake_case_				=							True

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Optional[int]:
								snake_case_				=							GPTNeoXModel(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								snake_case_				=							model(a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    List[str]:
								snake_case_				=							True
								snake_case_				=							GPTNeoXModel(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							GPTNeoXForCausalLM(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    Optional[int]:
								snake_case_				=							self.num_labels
								snake_case_				=							GPTNeoXForQuestionAnswering(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								self.parent.assertEqual(result.start_logits.shape       ,			(self.batch_size, self.seq_length)					)
								self.parent.assertEqual(result.end_logits.shape       ,			(self.batch_size, self.seq_length)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    List[str]:
								snake_case_				=							self.num_labels
								snake_case_				=							GPTNeoXForSequenceClassification(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							ids_tensor([self.batch_size]       ,			self.type_sequence_label_size					)
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    str:
								snake_case_				=							self.num_labels
								snake_case_				=							GPTNeoXForTokenClassification(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    List[Any]:
								snake_case_				=							True
								snake_case_				=							GPTNeoXForCausalLM(config=a					)
								model.to(a					)
								model.eval()

								# first forward pass
								snake_case_				=							model(a       ,			attention_mask=a       ,			use_cache=a					)
								snake_case_				=							outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								snake_case_				=							ids_tensor((self.batch_size, 3)       ,			config.vocab_size					)
								snake_case_				=							ids_tensor((self.batch_size, 3)       ,			vocab_size=2					)

								# append to next input_ids and
								snake_case_				=							torch.cat([input_ids, next_tokens]       ,			dim=-1					)
								snake_case_				=							torch.cat([input_mask, next_mask]       ,			dim=-1					)

								snake_case_				=							model(a       ,			attention_mask=a       ,			output_hidden_states=a					)
								snake_case_				=							output_from_no_past['hidden_states'][0]
								snake_case_				=							model(
								    a       ,			attention_mask=a       ,			past_key_values=a       ,			output_hidden_states=a       ,			)['hidden_states'][0]

								# select random slice
								snake_case_				=							ids_tensor((1,)       ,			output_from_past.shape[-1]					).item()
								snake_case_				=							output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case_				=							output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(a       ,			a       ,			atol=1E-3					)					)






	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict






@require_torch
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= (
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase													= (GPTNeoXForCausalLM,) if is_torch_available() else ()
	lowerCAmelCase													= (
	    {
	        '''feature-extraction''': GPTNeoXModel,
	        '''question-answering''': GPTNeoXForQuestionAnswering,
	        '''text-classification''': GPTNeoXForSequenceClassification,
	        '''text-generation''': GPTNeoXForCausalLM,
	        '''token-classification''': GPTNeoXForTokenClassification,
	        '''zero-shot''': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							GPTNeoXModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=64       ,			num_attention_heads=8					)




	def 							_UpperCamelCase			(       self					)       ->    Any:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								# This regression test was failing with PyTorch < 1.3
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case_				=							None

								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*a					)




	@unittest.skip(reason='Feed forward chunking is not implemented'					)
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								pass






	@parameterized.expand([('linear',), ('dynamic',)]					)
	def 							_UpperCamelCase			(       self       ,			a					)       ->    int:
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				=							ids_tensor([1, 10]       ,			config.vocab_size					)
								snake_case_				=							ids_tensor([1, int(config.max_position_embeddings * 1.5					)]       ,			config.vocab_size					)

								set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
								snake_case_				=							GPTNeoXModel(a					)
								original_model.to(a					)
								original_model.eval()
								snake_case_				=							original_model(a					).last_hidden_state
								snake_case_				=							original_model(a					).last_hidden_state

								set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
								snake_case_				=							{'type': scaling_type, 'factor': 10.0}
								snake_case_				=							GPTNeoXModel(a					)
								scaled_model.to(a					)
								scaled_model.eval()
								snake_case_				=							scaled_model(a					).last_hidden_state
								snake_case_				=							scaled_model(a					).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
															self.assertTrue(torch.allclose(a       ,			a       ,			atol=1E-5					)					)
								else:
															self.assertFalse(torch.allclose(a       ,			a       ,			atol=1E-5					)					)

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(a       ,			a       ,			atol=1E-5					)					)






@require_torch
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'					)
								for checkpointing in [True, False]:
															snake_case_				=							GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'					)

															if checkpointing:
																						model.gradient_checkpointing_enable()
															else:
																						model.gradient_checkpointing_disable()
															model.to(a					)

															snake_case_				=							tokenizer('My favorite food is'       ,			return_tensors='pt'					).to(a					)
															# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
															# See: https://github.com/huggingface/transformers/pull/24193
															snake_case_				=							'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

															snake_case_				=							model.generate(**a       ,			do_sample=a       ,			max_new_tokens=20					)
															snake_case_				=							tokenizer.batch_decode(a					)[0]

															self.assertEqual(a       ,			a					)





