





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 : List[Any]						,				_A : Optional[int]						,				_A : Tuple=13						,				_A : Union[str, Any]=7						,				_A : str=True						,				_A : Dict=True						,				_A : str=True						,				_A : Union[str, Any]=True						,				_A : str=99						,				_A : List[Any]=64						,				_A : List[str]=5						,				_A : Any=4						,				_A : List[Any]=37						,				_A : Optional[Any]="gelu"						,				_A : Union[str, Any]=0.1						,				_A : Tuple=0.1						,				_A : Optional[Any]=512						,				_A : Dict=16						,				_A : Tuple=2						,				_A : int=0.0_2						,				_A : int=3						,				_A : Tuple=4						,				_A : Optional[int]=None						,				)							->      Optional[int]:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : List[Any]		)							->      Union[str, Any]:







							"""simple docstring"""


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

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

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

							snake_case_						:     str     =		self.get_config()

							return config, input_ids, input_mask, token_labels






						def UpperCAmelCase_			( self : int		)							->      str:







							"""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 UpperCAmelCase_			( self : List[Any]		)							->      List[Any]:







							"""simple docstring"""


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

							snake_case_						:     Tuple     =		True

							return config, input_ids, input_mask, token_labels






						def UpperCAmelCase_			( self : str						,				_A : Any						,				_A : List[str]						,				_A : Optional[Any]		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Any     =		GPTNeoXModel(config=_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     List[Any]     =		model(_A						,				attention_mask=_A		)
							snake_case_						:     List[str]     =		model(_A		)
							self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Dict						,				_A : List[Any]						,				_A : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		True
							snake_case_						:     Optional[Any]     =		GPTNeoXModel(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Optional[int]     =		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 : str						,				_A : Optional[int]						,				_A : Any						,				_A : Optional[Any]						,				_A : Optional[int]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		GPTNeoXForCausalLM(config=_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     int     =		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 : str						,				_A : int						,				_A : List[Any]						,				_A : List[Any]						,				_A : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							snake_case_						:     int     =		self.num_labels
							snake_case_						:     int     =		GPTNeoXForQuestionAnswering(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     List[Any]     =		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 : List[str]						,				_A : Optional[Any]						,				_A : Dict						,				_A : Dict						,				_A : Any		)							->      int:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : Dict						,				_A : List[str]						,				_A : Optional[Any]						,				_A : Union[str, Any]						,				_A : Any		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.num_labels
							snake_case_						:     Tuple     =		GPTNeoXForTokenClassification(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     List[str]     =		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 : List[Any]						,				_A : List[Any]						,				_A : Optional[Any]						,				_A : Union[str, Any]		)							->      int:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		True
							snake_case_						:     Dict     =		GPTNeoXForCausalLM(config=_A		)
							model.to(_A		)
							model.eval()

							# first forward pass
							snake_case_						:     List[str]     =		model(_A						,				attention_mask=_A						,				use_cache=_A		)
							snake_case_						:     Optional[int]     =		outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							snake_case_						:     str     =		ids_tensor((self.batch_size, 3)						,				config.vocab_size		)
							snake_case_						:     Union[str, Any]     =		ids_tensor((self.batch_size, 3)						,				vocab_size=2		)

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

							snake_case_						:     Any     =		model(_A						,				attention_mask=_A						,				output_hidden_states=_A		)
							snake_case_						:     Union[str, Any]     =		output_from_no_past['hidden_states'][0]
							snake_case_						:     str     =		model(
							    _A						,				attention_mask=_A						,				past_key_values=_A						,				output_hidden_states=_A						,				)['hidden_states'][0]

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







							"""simple docstring"""


							snake_case_						:     Dict     =		self.prepare_config_and_inputs()
							snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:     Union[str, Any]     =		config_and_inputs
							snake_case_						:     Tuple     =		{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    (
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						__magic_name__:     str	 =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
						__magic_name__:     Optional[Any]	 =    (
						    {
						        "feature-extraction": GPTNeoXModel,
						        "question-answering": GPTNeoXForQuestionAnswering,
						        "text-classification": GPTNeoXForSequenceClassification,
						        "text-generation": GPTNeoXForCausalLM,
						        "token-classification": GPTNeoXForTokenClassification,
						        "zero-shot": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__magic_name__:     Any	 =    False
						__magic_name__:     Union[str, Any]	 =    False
						__magic_name__:     Dict	 =    False
						__magic_name__:     Any	 =    False






						def UpperCAmelCase_			( self : Optional[Any]		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		GPTNeoXModelTester(self		)
							snake_case_						:     str     =		ConfigTester(self						,				config_class=_A						,				hidden_size=64						,				num_attention_heads=8		)






						def UpperCAmelCase_			( self : Tuple		)							->      List[Any]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:     Optional[int]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(_A						,				_A						,				_A		)






						def UpperCAmelCase_			( self : Any		)							->      str:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:     Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(_A						,				_A						,				_A		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      int:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:     str     =		self.model_tester.prepare_config_and_inputs_for_decoder()

							snake_case_						:     Any     =		None

							self.model_tester.create_and_check_model_as_decoder(_A						,				_A						,				_A		)






						def UpperCAmelCase_			( self : Optional[int]		)							->      str:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:     Tuple     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(_A						,				_A						,				_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      int:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A		)






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      str:







							"""simple docstring"""


							snake_case_						:     int     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A		)






						def UpperCAmelCase_			( self : str		)							->      str:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		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 : Union[str, Any]		)							->      Optional[int]:







							"""simple docstring"""


							pass






						@parameterized.expand([('linear',), ('dynamic',)]		)
						def UpperCAmelCase_			( self : List[Any]						,				_A : List[Any]		)							->      str:







							"""simple docstring"""


							snake_case_  ,snake_case_						:     Optional[Any]     =		self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_						:     Optional[Any]     =		ids_tensor([1, 10]						,				config.vocab_size		)
							snake_case_						:     int     =		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_						:     Optional[Any]     =		GPTNeoXModel(_A		)
							original_model.to(_A		)
							original_model.eval()
							snake_case_						:     Optional[Any]     =		original_model(_A		).last_hidden_state
							snake_case_						:     Dict     =		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_						:     Any     =		{'type': scaling_type, 'factor': 1_0.0}
							snake_case_						:     Tuple     =		GPTNeoXModel(_A		)
							scaled_model.to(_A		)
							scaled_model.eval()
							snake_case_						:     Optional[Any]     =		scaled_model(_A		).last_hidden_state
							snake_case_						:     List[str]     =		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 SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@slow
						def UpperCAmelCase_			( self : List[Any]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Dict     =		AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'		)
							for checkpointing in [True, False]:
								snake_case_						:     List[Any]     =		GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'		)

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

								snake_case_						:     List[str]     =		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_						:     Union[str, Any]     =		'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

								snake_case_						:     int     =		model.generate(**_A						,				do_sample=_A						,				max_new_tokens=20		)
								snake_case_						:     Dict     =		tokenizer.batch_decode(_A		)[0]

								self.assertEqual(_A						,				_A		)








from decimal import Decimal, getcontext
from math import ceil, factorial


def 							SCREAMING_SNAKE_CASE__  (      __a				):

	if not isinstance(__a				,	__a				):
		raise TypeError('Undefined for non-integers'				)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'				)

	snake_case_						:     Dict     =		precision
	snake_case_						:     str     =		ceil(precision / 14				)
	snake_case_						:     str     =		42_68_80 * Decimal(1_00_05				).sqrt()
	snake_case_						:     Tuple     =		1
	snake_case_						:     int     =		13_59_14_09
	snake_case_						:     Tuple     =		Decimal(__a				)
	for k in range(1				,	__a				):
		snake_case_						:     List[Any]     =		factorial(6 * k				) // (factorial(3 * k				) * factorial(__a				) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term				) / exponential_term
	return str(constant_term / partial_sum				)[:-1]


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						50
						print(F'''The first {n} digits of pi is: {pi(n)}''')

