




'''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  _snake_case		:




			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__=512				,							a__=16				,							a__=2				,							a__=0.0_2				,							a__=3				,							a__=4				,							a__=None				,							)				->				List[Any]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				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=a__				,							initializer_range=self.initializer_range				,							pad_token_id=self.pad_token_id				,							)




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				str:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				List[str]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__							)				->				int:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__							)				->				List[str]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__							)				->				Dict:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__							)				->				List[Any]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				List[Any]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										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  _snake_case		(	lowercase_						,						lowercase_						,						lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      List[Any]						       =    (
			    (
			        GPTNeoXModel,
			        GPTNeoXForCausalLM,
			        GPTNeoXForQuestionAnswering,
			        GPTNeoXForSequenceClassification,
			        GPTNeoXForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCAmelCase_     :      List[Any]						       =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
			lowerCAmelCase_     :      str						       =    (
			    {
			        "feature-extraction": GPTNeoXModel,
			        "question-answering": GPTNeoXForQuestionAnswering,
			        "text-classification": GPTNeoXForSequenceClassification,
			        "text-generation": GPTNeoXForCausalLM,
			        "token-classification": GPTNeoXForTokenClassification,
			        "zero-shot": GPTNeoXForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_     :      str						       =    False
			lowerCAmelCase_     :      Optional[Any]						       =    False
			lowerCAmelCase_     :      List[str]						       =    False
			lowerCAmelCase_     :      Dict						       =    False




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      GPTNeoXModelTester(self							)
										snake_case_						=      ConfigTester(self				,							config_class=a__				,							hidden_size=64				,							num_attention_heads=8							)




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										self.config_tester.run_common_tests()




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*a__							)




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*a__							)




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*a__							)




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										pass




			@parameterized.expand([("linear",), ("dynamic",)]							)
			def    lowerCAmelCase__				(					self				,							a__							)				->				Optional[Any]:

										'''simple docstring'''




										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": 1_0.0}
										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  _snake_case		(	unittest.TestCase							):




			@slow
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										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__							)






import os
from math import logaa






def 							_lowercase							(     UpperCamelCase_ = "base_exp.txt"		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0
					for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase_		)	,	UpperCamelCase_		)		)		):
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= list(map(UpperCamelCase_	,	line.split(','		)		)		)
										if x * logaa(UpperCamelCase_		) > largest:
															SCREAMING_SNAKE_CASE__													= x * logaa(UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= i + 1
					return result


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





