



'''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      __magic_name__						:







							def __init__(							self :	List[str]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Tuple=13     ,_UpperCAmelCase :	str=7     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	int=True     ,_UpperCAmelCase :	Any=True     ,_UpperCAmelCase :	Optional[int]=True     ,_UpperCAmelCase :	List[str]=99     ,_UpperCAmelCase :	str=64     ,_UpperCAmelCase :	Dict=5     ,_UpperCAmelCase :	str=4     ,_UpperCAmelCase :	str=37     ,_UpperCAmelCase :	int="gelu"     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	List[Any]=512     ,_UpperCAmelCase :	Dict=16     ,_UpperCAmelCase :	int=2     ,_UpperCAmelCase :	Tuple=0.02     ,_UpperCAmelCase :	Dict=3     ,_UpperCAmelCase :	Dict=4     ,_UpperCAmelCase :	Tuple=None     ,):
									_a					:				str				=							parent
									_a					:				Tuple				=							batch_size
									_a					:				Union[str, Any]				=							seq_length
									_a					:				str				=							is_training
									_a					:				Dict				=							use_input_mask
									_a					:				Any				=							use_token_type_ids
									_a					:				Union[str, Any]				=							use_labels
									_a					:				Union[str, Any]				=							vocab_size
									_a					:				Dict				=							hidden_size
									_a					:				Optional[int]				=							num_hidden_layers
									_a					:				Any				=							num_attention_heads
									_a					:				Tuple				=							intermediate_size
									_a					:				List[Any]				=							hidden_act
									_a					:				Any				=							hidden_dropout_prob
									_a					:				Optional[Any]				=							attention_probs_dropout_prob
									_a					:				str				=							max_position_embeddings
									_a					:				Optional[int]				=							type_vocab_size
									_a					:				Optional[Any]				=							type_sequence_label_size
									_a					:				Dict				=							initializer_range
									_a					:				List[str]				=							num_labels
									_a					:				Dict				=							num_choices
									_a					:				Union[str, Any]				=							scope
									_a					:				Optional[int]				=							vocab_size - 1







							def 							__lowercase      (							self :	str		):
									_a					:				Dict				=							ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size		)

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

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

									_a					:				Optional[int]				=							self.get_config()

									return config, input_ids, input_mask, token_labels







							def 							__lowercase      (							self :	str		):
									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=_UpperCAmelCase     ,initializer_range=self.initializer_range     ,pad_token_id=self.pad_token_id     ,)







							def 							__lowercase      (							self :	Tuple		):
									_a	, _a	, _a	, _a					:				Dict				=							self.prepare_config_and_inputs()

									_a					:				Any				=							True

									return config, input_ids, input_mask, token_labels







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							GPTNeoXModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Dict				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)
									_a					:				Dict				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str		):
									_a					:				Any				=							True
									_a					:				List[str]				=							GPTNeoXModel(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Tuple				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int		):
									_a					:				Optional[Any]				=							GPTNeoXForCausalLM(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[Any]				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)		)







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Any		):
									_a					:				Optional[int]				=							self.num_labels
									_a					:				Optional[int]				=							GPTNeoXForQuestionAnswering(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[Any]				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)
									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 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Dict		):
									_a					:				Tuple				=							self.num_labels
									_a					:				Any				=							GPTNeoXForSequenceClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[Any]				=							ids_tensor([self.batch_size]     ,self.type_sequence_label_size		)
									_a					:				List[str]				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Any		):
									_a					:				Any				=							self.num_labels
									_a					:				Any				=							GPTNeoXForTokenClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[Any]				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels)		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							True
									_a					:				Union[str, Any]				=							GPTNeoXForCausalLM(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()

									# first forward pass
									_a					:				Any				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,use_cache=_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							outputs.past_key_values

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

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

									_a					:				Any				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,output_hidden_states=_UpperCAmelCase		)
									_a					:				List[str]				=							output_from_no_past['hidden_states'][0]
									_a					:				str				=							model(
									    _UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,past_key_values=_UpperCAmelCase     ,output_hidden_states=_UpperCAmelCase     ,)['hidden_states'][0]

									# select random slice
									_a					:				int				=							ids_tensor((1,)     ,output_from_past.shape[-1]		).item()
									_a					:				List[Any]				=							output_from_no_past[:, -3:, random_slice_idx].detach()
									_a					:				Union[str, 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(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=1E-3		)		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Union[str, Any]				=							self.prepare_config_and_inputs()
									_a	, _a	, _a	, _a					:				Dict				=							config_and_inputs
									_a					:				List[Any]				=							{'input_ids': input_ids, 'attention_mask': input_mask}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[Any]           = (
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase	:   List[str]           = (GPTNeoXForCausalLM,) if is_torch_available() else ()
							lowerCAmelCase	:   Optional[int]           = (
							    {
							        'feature-extraction': GPTNeoXModel,
							        'question-answering': GPTNeoXForQuestionAnswering,
							        'text-classification': GPTNeoXForSequenceClassification,
							        'text-generation': GPTNeoXForCausalLM,
							        'token-classification': GPTNeoXForTokenClassification,
							        'zero-shot': GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCAmelCase	:   Tuple           = False
							lowerCAmelCase	:   str           = False
							lowerCAmelCase	:   Optional[Any]           = False
							lowerCAmelCase	:   Tuple           = False







							def 							__lowercase      (							self :	Dict		):
									_a					:				int				=							GPTNeoXModelTester(self		)
									_a					:				Tuple				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,hidden_size=64     ,num_attention_heads=8		)







							def 							__lowercase      (							self :	int		):
									self.config_tester.run_common_tests()







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a	, _a	, _a	, _a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a	, _a	, _a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[Any]		):
									# This regression test was failing with PyTorch < 1.3
									_a	, _a	, _a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs_for_decoder()

									_a					:				Union[str, Any]				=							None

									self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a	, _a	, _a	, _a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				str				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]		):
									_a					:				str				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[str]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase		)







							@unittest.skip(reason='Feed forward chunking is not implemented'		)
							def 							__lowercase      (							self :	Dict		):
									pass







							@parameterized.expand([('linear',), ('dynamic',)]		)
							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Tuple		):
									_a	, _a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()
									_a					:				Optional[Any]				=							ids_tensor([1, 10]     ,config.vocab_size		)
									_a					:				Any				=							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					:				Dict				=							GPTNeoXModel(_UpperCAmelCase		)
									original_model.to(_UpperCAmelCase		)
									original_model.eval()
									_a					:				Optional[int]				=							original_model(_UpperCAmelCase		).last_hidden_state
									_a					:				str				=							original_model(_UpperCAmelCase		).last_hidden_state

									set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
									_a					:				Dict				=							{'type': scaling_type, 'factor': 10.0}
									_a					:				int				=							GPTNeoXModel(_UpperCAmelCase		)
									scaled_model.to(_UpperCAmelCase		)
									scaled_model.eval()
									_a					:				Dict				=							scaled_model(_UpperCAmelCase		).last_hidden_state
									_a					:				Tuple				=							scaled_model(_UpperCAmelCase		).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(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=1E-5		)		)
									else:
											self.assertFalse(torch.allclose(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=1E-5		)		)

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

@require_torch
class      __magic_name__						(   unittest.TestCase       ):







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

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

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

											_a					:				int				=							model.generate(**_UpperCAmelCase     ,do_sample=_UpperCAmelCase     ,max_new_tokens=20		)
											_a					:				int				=							tokenizer.batch_decode(_UpperCAmelCase		)[0]

											self.assertEqual(_UpperCAmelCase     ,_UpperCAmelCase		)









'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      __magic_name__						:







							def __init__(							self :	Dict     ,_UpperCAmelCase :	Any		):
									_a					:				Any				=							data
									_a					:				Node | None				=							None

class      __magic_name__						:







							def __init__(							self :	Any		):
									_a					:				int				=							None
									_a					:				Optional[int]				=							None







							def __iter__(							self :	Optional[int]		):
									_a					:				List[Any]				=							self.head
									while self.head:
											yield node.data
											_a					:				str				=							node.next
											if node == self.head:
													break







							def __len__(							self :	Any		):
									return sum(1 for _ in self		)







							def __repr__(							self :	int		):
									return "->".join(str(_UpperCAmelCase		) for item in iter(self		)		)







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Any		):
									self.insert_nth(len(self		)     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Any		):
									self.insert_nth(0     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Any		):
									if index < 0 or index > len(self		):
											raise IndexError('list index out of range.'		)
									_a					:				List[str]				=							Node(_UpperCAmelCase		)
									if self.head is None:
											_a					:				Tuple				=							new_node  # first node points itself
											_a					:				int				=							new_node
									elif index == 0:  # insert at head
											_a					:				Any				=							self.head
											_a					:				Tuple				=							new_node
									else:
											_a					:				Any				=							self.head
											for _ in range(index - 1		):
													_a					:				int				=							temp.next
											_a					:				Optional[int]				=							temp.next
											_a					:				int				=							new_node
											if index == len(self		) - 1:  # insert at tail
													_a					:				Optional[int]				=							new_node







							def 							__lowercase      (							self :	List[Any]		):
									return self.delete_nth(0		)







							def 							__lowercase      (							self :	Dict		):
									return self.delete_nth(len(self		) - 1		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	int = 0		):
									if not 0 <= index < len(self		):
											raise IndexError('list index out of range.'		)
									_a					:				Optional[int]				=							self.head
									if self.head == self.tail:  # just one node
											_a					:				Optional[int]				=							None
									elif index == 0:  # delete head node
											_a					:				Dict				=							self.tail.next.next
											_a					:				Dict				=							self.head.next
									else:
											_a					:				List[Any]				=							self.head
											for _ in range(index - 1		):
													_a					:				Union[str, Any]				=							temp.next
											_a					:				Optional[int]				=							temp.next
											_a					:				List[str]				=							temp.next.next
											if index == len(self		) - 1:  # delete at tail
													_a					:				int				=							temp
									return delete_node.data







							def 							__lowercase      (							self :	int		):
									return len(self		) == 0



def 					__lowerCamelCase					(				)      ->				None:
		_a					:				int				=							CircularLinkedList()
		assert len(lowerCAmelCase_  ) == 0
		assert circular_linked_list.is_empty() is True
		assert str(lowerCAmelCase_  ) == ""

		try:
				circular_linked_list.delete_front()
				raise AssertionError  # This should not happen
		except IndexError:
				assert True  # This should happen

		try:
				circular_linked_list.delete_tail()
				raise AssertionError  # This should not happen
		except IndexError:
				assert True  # This should happen

		try:
				circular_linked_list.delete_nth(-1  )
				raise AssertionError
		except IndexError:
				assert True

		try:
				circular_linked_list.delete_nth(0  )
				raise AssertionError
		except IndexError:
				assert True

		assert circular_linked_list.is_empty() is True
		for i in range(5  ):
				assert len(lowerCAmelCase_  ) == i
				circular_linked_list.insert_nth(lowerCAmelCase_						,      i + 1  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      6  )  )

		circular_linked_list.insert_tail(6  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      7  )  )
		circular_linked_list.insert_head(0  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(0						,      7  )  )

		assert circular_linked_list.delete_front() == 0
		assert circular_linked_list.delete_tail() == 6
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      6  )  )
		assert circular_linked_list.delete_nth(2  ) == 3

		circular_linked_list.insert_nth(2						,      3  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      6  )  )

		assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
							import doctest

							doctest.testmod()




