
'''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   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=13   ,       _lowercase=7   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=99   ,       _lowercase=64   ,       _lowercase=5   ,       _lowercase=4   ,       _lowercase=37   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=16   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=3   ,       _lowercase=4   ,       _lowercase=None   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			seq_length
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			use_input_mask
												_lowerCAmelCase  						=			use_token_type_ids
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			type_sequence_label_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			num_choices
												_lowerCAmelCase  						=			scope
												_lowerCAmelCase  						=			vocab_size - 1




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size				)

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

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

												_lowerCAmelCase  						=			self.get_config()

												return config, input_ids, input_mask, token_labels




						def  _lowercase     ( self				):

												"""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=_lowercase   ,       initializer_range=self.initializer_range   ,       pad_token_id=self.pad_token_id   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			self.prepare_config_and_inputs()

												_lowerCAmelCase  						=			True

												return config, input_ids, input_mask, token_labels




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			GPTNeoXModel(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase				)
												_lowerCAmelCase  						=			model(_lowercase				)
												self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			True
												_lowerCAmelCase  						=			GPTNeoXModel(_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase				)
												self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			GPTNeoXForCausalLM(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			GPTNeoXForQuestionAnswering(_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase				)
												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   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			GPTNeoXForSequenceClassification(_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			ids_tensor([self.batch_size]   ,       self.type_sequence_label_size				)
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			GPTNeoXForTokenClassification(_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			True
												_lowerCAmelCase  						=			GPTNeoXForCausalLM(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()

												# first forward pass
												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase   ,       use_cache=_lowercase				)
												_lowerCAmelCase  						=			outputs.past_key_values

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

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

												_lowerCAmelCase  						=			model(_lowercase   ,       attention_mask=_lowercase   ,       output_hidden_states=_lowercase				)
												_lowerCAmelCase  						=			output_from_no_past["""hidden_states"""][0]
												_lowerCAmelCase  						=			model(
												    _lowercase   ,       attention_mask=_lowercase   ,       past_key_values=_lowercase   ,       output_hidden_states=_lowercase   ,       )["""hidden_states"""][0]

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.prepare_config_and_inputs()
												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			config_and_inputs
												_lowerCAmelCase  						=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
												return config, inputs_dict






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       (
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowercase		:				Tuple									=       (GPTNeoXForCausalLM,) if is_torch_available() else ()
						_lowercase		:				str									=       (
						    {
						        '''feature-extraction''': GPTNeoXModel,
						        '''question-answering''': GPTNeoXForQuestionAnswering,
						        '''text-classification''': GPTNeoXForSequenceClassification,
						        '''text-generation''': GPTNeoXForCausalLM,
						        '''token-classification''': GPTNeoXForTokenClassification,
						        '''zero-shot''': GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowercase		:				Optional[int]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				Tuple									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			GPTNeoXModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       hidden_size=64   ,       num_attention_heads=8				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_decoder()

												_lowerCAmelCase  						=			None

												self.model_tester.create_and_check_model_as_decoder(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_lowercase				)




						@unittest.skip(reason="""Feed forward chunking is not implemented"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@parameterized.expand([("""linear""",), ("""dynamic""",)]				)
						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()
												_lowerCAmelCase  						=			ids_tensor([1, 10]   ,       config.vocab_size				)
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			GPTNeoXModel(_lowercase				)
												original_model.to(_lowercase				)
												original_model.eval()
												_lowerCAmelCase  						=			original_model(_lowercase				).last_hidden_state
												_lowerCAmelCase  						=			original_model(_lowercase				).last_hidden_state

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

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






@require_torch
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



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

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

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

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

																		self.assertEqual(_lowercase   ,       _lowercase				)


'''simple docstring'''



from __future__ import annotations




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			order

												# a_{0} ... a_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order
												# b_{0} ... b_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order

												# x[n-1] ... x[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order
												# y[n-1] ... y[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												if len(_lowercase				) < self.order:
																		_lowerCAmelCase  						=			[1.0, *a_coeffs]

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected a_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected b_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												_lowerCAmelCase  						=			a_coeffs
												_lowerCAmelCase  						=			b_coeffs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			0.0

												# Start at index 1 and do index 0 at the end.
												for i in range(1   ,       self.order + 1				):
																		result += (
																		    self.b_coeffs[i] * self.input_history[i - 1]
																		    - self.a_coeffs[i] * self.output_history[i - 1]
																		)

												_lowerCAmelCase  						=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

												_lowerCAmelCase  						=			self.input_history[:-1]
												_lowerCAmelCase  						=			self.output_history[:-1]

												_lowerCAmelCase  						=			sample
												_lowerCAmelCase  						=			result

												return result
