





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_		:




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=13				,						lowerCAmelCase_=7				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=99				,						lowerCAmelCase_=64				,						lowerCAmelCase_=5				,						lowerCAmelCase_=4				,						lowerCAmelCase_=37				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=512				,						lowerCAmelCase_=16				,						lowerCAmelCase_=2				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=3				,						lowerCAmelCase_=4				,						lowerCAmelCase_=None				,						)		->							int:
										_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  )		->							Dict:
										_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  )		->							Union[str, Any]:
										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=lowerCAmelCase_				,						initializer_range=self.initializer_range				,						pad_token_id=self.pad_token_id				,						)




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_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				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Any:
										_snake_case									=				GPTNeoXModel(config=lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_  )
										_snake_case									=				model(lowerCAmelCase_  )
										self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Tuple:
										_snake_case									=				True
										_snake_case									=				GPTNeoXModel(lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_  )
										self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Any:
										_snake_case									=				GPTNeoXForCausalLM(config=lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						labels=lowerCAmelCase_  )
										self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				self.num_labels
										_snake_case									=				GPTNeoXForQuestionAnswering(lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_  )
										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				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							int:
										_snake_case									=				self.num_labels
										_snake_case									=				GPTNeoXForSequenceClassification(lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				ids_tensor([self.batch_size]				,						self.type_sequence_label_size  )
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						labels=lowerCAmelCase_  )
										self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							List[str]:
										_snake_case									=				self.num_labels
										_snake_case									=				GPTNeoXForTokenClassification(lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						labels=lowerCAmelCase_  )
										self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.num_labels)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							int:
										_snake_case									=				True
										_snake_case									=				GPTNeoXForCausalLM(config=lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()

										# first forward pass
										_snake_case									=				model(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						use_cache=lowerCAmelCase_  )
										_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(lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						output_hidden_states=lowerCAmelCase_  )
										_snake_case									=				output_from_no_past['hidden_states'][0]
										_snake_case									=				model(
										    lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						past_key_values=lowerCAmelCase_				,						output_hidden_states=lowerCAmelCase_				,						)['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(lowerCAmelCase_				,						lowerCAmelCase_				,						atol=1E-3  )  )






					def 	lowerCAmelCase			(		self  )		->							str:
										_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 			UpperCamelCase_		(  lowercase__							,  lowercase__							,  lowercase__							,  unittest.TestCase					):
					lowerCAmelCase_							=	(
					    (
					        GPTNeoXModel,
					        GPTNeoXForCausalLM,
					        GPTNeoXForQuestionAnswering,
					        GPTNeoXForSequenceClassification,
					        GPTNeoXForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase_							=	(GPTNeoXForCausalLM,) if is_torch_available() else ()
					lowerCAmelCase_							=	(
					    {
					        'feature-extraction': GPTNeoXModel,
					        'question-answering': GPTNeoXForQuestionAnswering,
					        'text-classification': GPTNeoXForSequenceClassification,
					        'text-generation': GPTNeoXForCausalLM,
					        'token-classification': GPTNeoXForTokenClassification,
					        'zero-shot': GPTNeoXForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase_							=	False
					lowerCAmelCase_							=	False
					lowerCAmelCase_							=	False
					lowerCAmelCase_							=	False




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				GPTNeoXModelTester(self  )
										_snake_case									=				ConfigTester(self				,						config_class=lowerCAmelCase_				,						hidden_size=64				,						num_attention_heads=8  )




					def 	lowerCAmelCase			(		self  )		->							int:
										self.config_tester.run_common_tests()




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										_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(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							str:
										# This regression test was failing with PyTorch < 1.3
										_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(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Any:
										_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(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_  )




					@unittest.skip(reason='Feed forward chunking is not implemented'  )
					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										pass






					@parameterized.expand([('linear',), ('dynamic',)]  )
					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										_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(lowerCAmelCase_  )
										original_model.to(lowerCAmelCase_  )
										original_model.eval()
										_snake_case									=				original_model(lowerCAmelCase_  ).last_hidden_state
										_snake_case									=				original_model(lowerCAmelCase_  ).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': 10.0}
										_snake_case									=				GPTNeoXModel(lowerCAmelCase_  )
										scaled_model.to(lowerCAmelCase_  )
										scaled_model.eval()
										_snake_case									=				scaled_model(lowerCAmelCase_  ).last_hidden_state
										_snake_case									=				scaled_model(lowerCAmelCase_  ).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(lowerCAmelCase_				,						lowerCAmelCase_				,						atol=1E-5  )  )
										else:
															self.assertFalse(torch.allclose(lowerCAmelCase_				,						lowerCAmelCase_				,						atol=1E-5  )  )

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





@require_torch
class 			UpperCamelCase_		(  unittest.TestCase					):




					@slow
					def 	lowerCAmelCase			(		self  )		->							Any:
										_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(lowerCAmelCase_  )

															_snake_case									=				tokenizer('My favorite food is'				,						return_tensors='pt'  ).to(lowerCAmelCase_  )
															# 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(**lowerCAmelCase_				,						do_sample=lowerCAmelCase_				,						max_new_tokens=20  )
															_snake_case									=				tokenizer.batch_decode(lowerCAmelCase_  )[0]

															self.assertEqual(lowerCAmelCase_				,						lowerCAmelCase_  )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



