"""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__:





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=7		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=99		,__UpperCAmelCase=64		,__UpperCAmelCase=5		,__UpperCAmelCase=4		,__UpperCAmelCase=37		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=5_12		,__UpperCAmelCase=16		,__UpperCAmelCase=2		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=4		,__UpperCAmelCase=None		,)					->				str:
													A__	     =							parent
													A__	     =							batch_size
													A__	     =							seq_length
													A__	     =							is_training
													A__	     =							use_input_mask
													A__	     =							use_token_type_ids
													A__	     =							use_labels
													A__	     =							vocab_size
													A__	     =							hidden_size
													A__	     =							num_hidden_layers
													A__	     =							num_attention_heads
													A__	     =							intermediate_size
													A__	     =							hidden_act
													A__	     =							hidden_dropout_prob
													A__	     =							attention_probs_dropout_prob
													A__	     =							max_position_embeddings
													A__	     =							type_vocab_size
													A__	     =							type_sequence_label_size
													A__	     =							initializer_range
													A__	     =							num_labels
													A__	     =							num_choices
													A__	     =							scope
													A__	     =							vocab_size - 1





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size     )

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

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

													A__	     =							self.get_config()

													return config, input_ids, input_mask, token_labels





						def 				snake_case__	(       self     )					->				List[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=A_		,initializer_range=self.initializer_range		,pad_token_id=self.pad_token_id		,)





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.prepare_config_and_inputs()

													A__	     =							True

													return config, input_ids, input_mask, token_labels





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[Any]:
													A__	     =							GPTNeoXModel(config=A_     )
													model.to(A_     )
													model.eval()
													A__	     =							model(A_		,attention_mask=A_     )
													A__	     =							model(A_     )
													self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Dict:
													A__	     =							True
													A__	     =							GPTNeoXModel(A_     )
													model.to(A_     )
													model.eval()
													A__	     =							model(A_		,attention_mask=A_     )
													self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							GPTNeoXForCausalLM(config=A_     )
													model.to(A_     )
													model.eval()
													A__	     =							model(A_		,attention_mask=A_		,labels=A_     )
													self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				int:
													A__	     =							self.num_labels
													A__	     =							GPTNeoXForQuestionAnswering(A_     )
													model.to(A_     )
													model.eval()
													A__	     =							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 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							self.num_labels
													A__	     =							GPTNeoXForSequenceClassification(A_     )
													model.to(A_     )
													model.eval()
													A__	     =							ids_tensor([self.batch_size]		,self.type_sequence_label_size     )
													A__	     =							model(A_		,attention_mask=A_		,labels=A_     )
													self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							self.num_labels
													A__	     =							GPTNeoXForTokenClassification(A_     )
													model.to(A_     )
													model.eval()
													A__	     =							model(A_		,attention_mask=A_		,labels=A_     )
													self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							True
													A__	     =							GPTNeoXForCausalLM(config=A_     )
													model.to(A_     )
													model.eval()

													# first forward pass
													A__	     =							model(A_		,attention_mask=A_		,use_cache=A_     )
													A__	     =							outputs.past_key_values

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

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

													A__	     =							model(A_		,attention_mask=A_		,output_hidden_states=A_     )
													A__	     =							output_from_no_past["hidden_states"][0]
													A__	     =							model(
													    A_		,attention_mask=A_		,past_key_values=A_		,output_hidden_states=A_		,)["hidden_states"][0]

													# select random slice
													A__	     =							ids_tensor((1,)		,output_from_past.shape[-1]     ).item()
													A__	     =							output_from_no_past[:, -3:, random_slice_idx].detach()
													A__	     =							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 				snake_case__	(       self     )					->				int:
													A__	     =							self.prepare_config_and_inputs()
													A__	     =							config_and_inputs
													A__	     =							{"input_ids": input_ids, "attention_mask": input_mask}
													return config, inputs_dict






@require_torch
class     UpperCamelCase__(			__snake_case		,						__snake_case		,						__snake_case		,						unittest.TestCase ):
						lowerCAmelCase__  :						str													=		(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase__  :						str													=		(GPTNeoXForCausalLM,) if is_torch_available() else ()
						lowerCAmelCase__  :						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 {}
						)
						lowerCAmelCase__  :						Any													=		False
						lowerCAmelCase__  :						List[str]													=		False
						lowerCAmelCase__  :						Union[str, Any]													=		False
						lowerCAmelCase__  :						Dict													=		False





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							GPTNeoXModelTester(self     )
													A__	     =							ConfigTester(self		,config_class=A_		,hidden_size=64		,num_attention_heads=8     )





						def 				snake_case__	(       self     )					->				Tuple:
													self.config_tester.run_common_tests()





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(A_		,A_		,A_     )





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(A_		,A_		,A_     )





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.model_tester.prepare_config_and_inputs_for_decoder()

													A__	     =							None

													self.model_tester.create_and_check_model_as_decoder(A_		,A_		,A_     )





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past_large_inputs(A_		,A_		,A_     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_causal_lm(*A_     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*A_     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*A_     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							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 				snake_case__	(       self     )					->				int:
													pass





						@parameterized.expand([('linear',), ('dynamic',)]     )
						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				List[str]:
													A__	     =							self.model_tester.prepare_config_and_inputs_for_common()
													A__	     =							ids_tensor([1, 10]		,config.vocab_size     )
													A__	     =							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__	     =							GPTNeoXModel(A_     )
													original_model.to(A_     )
													original_model.eval()
													A__	     =							original_model(A_     ).last_hidden_state
													A__	     =							original_model(A_     ).last_hidden_state

													set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
													A__	     =							{"type": scaling_type, "factor": 1_0.0}
													A__	     =							GPTNeoXModel(A_     )
													scaled_model.to(A_     )
													scaled_model.eval()
													A__	     =							scaled_model(A_     ).last_hidden_state
													A__	     =							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     UpperCamelCase__(			unittest.TestCase ):





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

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

																				A__	     =							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
																				A__	     =							"My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

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

																				self.assertEqual(A_		,A_     )






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				None:
													warnings.warn(
													    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
													    ' Please use ImageGPTImageProcessor instead.'		,__UpperCAmelCase		,)
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





