






"""simple docstring"""



def        _lowerCAmelCase						(							UpperCAmelCase__    :   list[int],  UpperCAmelCase__    :   list[int] )						->None:
						A__  :					str											=					len(UpperCAmelCase__ )
						print("""The following activities are selected:""" )

						# The first activity is always selected
						A__  :					Any											=					0
						print(UpperCAmelCase__,  end=""",""" )

						# Consider rest of the activities
						for j in range(UpperCAmelCase__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(UpperCAmelCase__,  end=""",""" )
																		A__  :					Tuple											=					j


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A_      =							[1, 3, 0, 5, 8, 5]
						A_      =							[2, 4, 6, 7, 9, 9]
						print_max_activities(start, finish)














"""simple docstring"""



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 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  Optional[int]     ,      snake_case :  Optional[Any]     ,      snake_case :  Tuple=13     ,      snake_case :  Dict=7     ,      snake_case :  Optional[int]=True     ,      snake_case :  Union[str, Any]=True     ,      snake_case :  Dict=True     ,      snake_case :  Any=True     ,      snake_case :  List[str]=99     ,      snake_case :  str=64     ,      snake_case :  Optional[int]=5     ,      snake_case :  str=4     ,      snake_case :  List[Any]=37     ,      snake_case :  Optional[Any]="gelu"     ,      snake_case :  List[str]=0.1     ,      snake_case :  str=0.1     ,      snake_case :  Optional[int]=512     ,      snake_case :  Dict=16     ,      snake_case :  List[Any]=2     ,      snake_case :  Optional[int]=0.02     ,      snake_case :  Any=3     ,      snake_case :  Union[str, Any]=4     ,      snake_case :  Dict=None     ,      ):
													'''simple docstring'''





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


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





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

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

													A__  :					Union[str, Any]											=					None
													if self.use_labels:
																			A__  :					Optional[int]											=					ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels							)

													A__  :					Tuple											=					self.get_config()

													return config, input_ids, input_mask, token_labels


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													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=snake_case     ,      initializer_range=self.initializer_range     ,      pad_token_id=self.pad_token_id     ,      )


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					str											=					self.prepare_config_and_inputs()

													A__  :					Union[str, Any]											=					True

													return config, input_ids, input_mask, token_labels


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  Optional[int]     ,      snake_case :  List[str]     ,      snake_case :  int							):
													'''simple docstring'''





													A__  :					Any											=					GPTNeoXModel(config=snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					int											=					model(snake_case     ,      attention_mask=snake_case							)
													A__  :					Optional[int]											=					model(snake_case							)
													self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)							)


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  str     ,      snake_case :  Any     ,      snake_case :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					int											=					True
													A__  :					str											=					GPTNeoXModel(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case							)
													self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)							)


							def   _UpperCamelCase    (     self :  Dict     ,      snake_case :  List[Any]     ,      snake_case :  str     ,      snake_case :  Optional[Any]     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Any											=					GPTNeoXForCausalLM(config=snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case     ,      labels=snake_case							)
													self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Optional[Any]     ,      snake_case :  Optional[Any]     ,      snake_case :  Union[str, Any]     ,      snake_case :  Tuple							):
													'''simple docstring'''





													A__  :					int											=					self.num_labels
													A__  :					int											=					GPTNeoXForQuestionAnswering(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Optional[Any]											=					model(snake_case     ,      attention_mask=snake_case							)
													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 :  str     ,      snake_case :  Tuple     ,      snake_case :  int     ,      snake_case :  int     ,      snake_case :  Dict							):
													'''simple docstring'''





													A__  :					List[Any]											=					self.num_labels
													A__  :					Tuple											=					GPTNeoXForSequenceClassification(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Any											=					ids_tensor([self.batch_size]     ,      self.type_sequence_label_size							)
													A__  :					List[str]											=					model(snake_case     ,      attention_mask=snake_case     ,      labels=snake_case							)
													self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)							)


							def   _UpperCamelCase    (     self :  Any     ,      snake_case :  Union[str, Any]     ,      snake_case :  int     ,      snake_case :  Tuple     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Tuple											=					self.num_labels
													A__  :					Any											=					GPTNeoXForTokenClassification(snake_case							)
													model.to(snake_case							)
													model.eval()
													A__  :					Dict											=					model(snake_case     ,      attention_mask=snake_case     ,      labels=snake_case							)
													self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  List[str]     ,      snake_case :  Tuple     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Optional[int]											=					True
													A__  :					Any											=					GPTNeoXForCausalLM(config=snake_case							)
													model.to(snake_case							)
													model.eval()

													# first forward pass
													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case     ,      use_cache=snake_case							)
													A__  :					str											=					outputs.past_key_values

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

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

													A__  :					Tuple											=					model(snake_case     ,      attention_mask=snake_case     ,      output_hidden_states=snake_case							)
													A__  :					List[Any]											=					output_from_no_past["""hidden_states"""][0]
													A__  :					List[str]											=					model(
													    snake_case     ,      attention_mask=snake_case     ,      past_key_values=snake_case     ,      output_hidden_states=snake_case     ,      )["""hidden_states"""][0]

													# select random slice
													A__  :					Tuple											=					ids_tensor((1,)     ,      output_from_past.shape[-1]							).item()
													A__  :					List[Any]											=					output_from_no_past[:, -3:, random_slice_idx].detach()
													A__  :					Any											=					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(snake_case     ,      snake_case     ,      atol=1e-3							)							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													A__  :					str											=					self.prepare_config_and_inputs()
													A__					,					A__					,					A__					,					A__  :					Dict											=					config_and_inputs
													A__  :					Optional[Any]											=					{"""input_ids""": input_ids, """attention_mask""": input_mask}
													return config, inputs_dict






@require_torch
class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase						):
							snake_case_       =							(
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case_       =							(GPTNeoXForCausalLM,) if is_torch_available() else ()
							snake_case_       =							(
							    {
							        'feature-extraction': GPTNeoXModel,
							        'question-answering': GPTNeoXForQuestionAnswering,
							        'text-classification': GPTNeoXForSequenceClassification,
							        'text-generation': GPTNeoXForCausalLM,
							        'token-classification': GPTNeoXForTokenClassification,
							        'zero-shot': GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							snake_case_       =							False
							snake_case_       =							False
							snake_case_       =							False
							snake_case_       =							False


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					Any											=					GPTNeoXModelTester(self							)
													A__  :					Any											=					ConfigTester(self     ,      config_class=snake_case     ,      hidden_size=64     ,      num_attention_heads=8							)


							def   _UpperCamelCase    (     self :  Union[str, Any]							):
													'''simple docstring'''





													self.config_tester.run_common_tests()


							def   _UpperCamelCase    (     self :  Any							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					Any											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					Dict											=					self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													A__					,					A__					,					A__					,					A__  :					List[str]											=					self.model_tester.prepare_config_and_inputs_for_decoder()

													A__  :					Optional[Any]											=					None

													self.model_tester.create_and_check_model_as_decoder(snake_case     ,      snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





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


							def   _UpperCamelCase    (     self :  Optional[Any]							):
													'''simple docstring'''





													A__  :					Optional[Any]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_causal_lm(*snake_case							)


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__  :					Any											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													A__  :					Optional[Any]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*snake_case							)


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													A__  :					List[str]											=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*snake_case							)


							@unittest.skip(reason="""Feed forward chunking is not implemented"""							)
							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													pass


							@parameterized.expand([("""linear""",), ("""dynamic""",)]							)
							def   _UpperCamelCase    (     self :  Optional[Any]     ,      snake_case :  Optional[Any]							):
													'''simple docstring'''





													A__					,					A__  :					int											=					self.model_tester.prepare_config_and_inputs_for_common()
													A__  :					List[Any]											=					ids_tensor([1, 10]     ,      config.vocab_size							)
													A__  :					str											=					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
													A__  :					Union[str, Any]											=					GPTNeoXModel(snake_case							)
													original_model.to(snake_case							)
													original_model.eval()
													A__  :					Optional[int]											=					original_model(snake_case							).last_hidden_state
													A__  :					List[str]											=					original_model(snake_case							).last_hidden_state

													set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
													A__  :					Optional[int]											=					{"""type""": scaling_type, """factor""": 10.0}
													A__  :					Optional[int]											=					GPTNeoXModel(snake_case							)
													scaled_model.to(snake_case							)
													scaled_model.eval()
													A__  :					List[str]											=					scaled_model(snake_case							).last_hidden_state
													A__  :					Tuple											=					scaled_model(snake_case							).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(snake_case     ,      snake_case     ,      atol=1e-5							)							)
													else:
																			self.assertFalse(torch.allclose(snake_case     ,      snake_case     ,      atol=1e-5							)							)

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






@require_torch
class 							__SCREAMING_SNAKE_CASE  (			unittest.TestCase						):


							@slow
							def   _UpperCamelCase    (     self :  Tuple							):
													'''simple docstring'''





													A__  :					Any											=					AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)
													for checkpointing in [True, False]:
																			A__  :					Optional[Any]											=					GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)

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

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

																			A__  :					Tuple											=					model.generate(**snake_case     ,      do_sample=snake_case     ,      max_new_tokens=20							)
																			A__  :					Tuple											=					tokenizer.batch_decode(snake_case							)[0]

																			self.assertEqual(snake_case     ,      snake_case							)






