
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  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_3       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=7       ,							SCREAMING_SNAKE_CASE_  :			Tuple=True       ,							SCREAMING_SNAKE_CASE_  :			str=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=9_9       ,							SCREAMING_SNAKE_CASE_  :			Any=6_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=5       ,							SCREAMING_SNAKE_CASE_  :			Any=4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_7       ,							SCREAMING_SNAKE_CASE_  :			Tuple="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Any=0.1       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Any=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=1_6       ,							SCREAMING_SNAKE_CASE_  :			List[str]=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Dict=3       ,							SCREAMING_SNAKE_CASE_  :			int=4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=None       ,							)       ->     Any:
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					seq_length
					lowercase_       =					is_training
					lowercase_       =					use_input_mask
					lowercase_       =					use_token_type_ids
					lowercase_       =					use_labels
					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					type_sequence_label_size
					lowercase_       =					initializer_range
					lowercase_       =					num_labels
					lowercase_       =					num_choices
					lowercase_       =					scope
					lowercase_       =					vocab_size - 1
	def 						_lowercase  (       self  :			int       )       ->     int:
					lowercase_       =					ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

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

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

					lowercase_       =					self.get_config()

					return config, input_ids, input_mask, token_labels
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Optional[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=_snake_case       ,							initializer_range=self.initializer_range       ,							pad_token_id=self.pad_token_id       ,							)
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[Any]:
					lowercase_       =					self.prepare_config_and_inputs()

					lowercase_       =					True

					return config, input_ids, input_mask, token_labels
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     List[str]:
					lowercase_       =					GPTNeoXModel(config=_snake_case       )
					model.to(_snake_case       )
					model.eval()
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       )
					lowercase_       =					model(_snake_case       )
					self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)       )
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Optional[Any]:
					lowercase_       =					True
					lowercase_       =					GPTNeoXModel(_snake_case       )
					model.to(_snake_case       )
					model.eval()
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       )
					self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Tuple:
					lowercase_       =					GPTNeoXForCausalLM(config=_snake_case       )
					model.to(_snake_case       )
					model.eval()
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       ,							labels=_snake_case       )
					self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.vocab_size)       )
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     List[str]:
					lowercase_       =					self.num_labels
					lowercase_       =					GPTNeoXForQuestionAnswering(_snake_case       )
					model.to(_snake_case       )
					model.eval()
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       )
					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  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Union[str, Any]:
					lowercase_       =					self.num_labels
					lowercase_       =					GPTNeoXForSequenceClassification(_snake_case       )
					model.to(_snake_case       )
					model.eval()
					lowercase_       =					ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       ,							labels=_snake_case       )
					self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     str:
					lowercase_       =					self.num_labels
					lowercase_       =					GPTNeoXForTokenClassification(_snake_case       )
					model.to(_snake_case       )
					model.eval()
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       ,							labels=_snake_case       )
					self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     str:
					lowercase_       =					True
					lowercase_       =					GPTNeoXForCausalLM(config=_snake_case       )
					model.to(_snake_case       )
					model.eval()

					# first forward pass
					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       ,							use_cache=_snake_case       )
					lowercase_       =					outputs.past_key_values

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

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

					lowercase_       =					model(_snake_case       ,							attention_mask=_snake_case       ,							output_hidden_states=_snake_case       )
					lowercase_       =					output_from_no_past['hidden_states'][0]
					lowercase_       =					model(
					    _snake_case       ,							attention_mask=_snake_case       ,							past_key_values=_snake_case       ,							output_hidden_states=_snake_case       ,							)['hidden_states'][0]

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




	def 						_lowercase  (       self  :			str       )       ->     Union[str, Any]:
					lowercase_       =					self.prepare_config_and_inputs()
					lowercase_       =					config_and_inputs
					lowercase_       =					{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict

@require_torch
class  lowercase__(				lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						unittest.TestCase							):



	"""simple docstring"""




	a			:int         =	(
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a			:str         =	(GPTNeoXForCausalLM,) if is_torch_available() else ()
	a			:Dict         =	(
	    {
	        'feature-extraction': GPTNeoXModel,
	        'question-answering': GPTNeoXForQuestionAnswering,
	        'text-classification': GPTNeoXForSequenceClassification,
	        'text-generation': GPTNeoXForCausalLM,
	        'token-classification': GPTNeoXForTokenClassification,
	        'zero-shot': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a			:Any         =	False
	a			:int         =	False
	a			:Any         =	False
	a			:Dict         =	False
	def 						_lowercase  (       self  :			Optional[int]       )       ->     List[Any]:
					lowercase_       =					GPTNeoXModelTester(self       )
					lowercase_       =					ConfigTester(self       ,							config_class=_snake_case       ,							hidden_size=6_4       ,							num_attention_heads=8       )
	def 						_lowercase  (       self  :			List[str]       )       ->     Any:
					self.config_tester.run_common_tests()
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[str]:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(_snake_case       ,							_snake_case       ,							_snake_case       )
	def 						_lowercase  (       self  :			Tuple       )       ->     int:
					lowercase_       =					self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(_snake_case       ,							_snake_case       ,							_snake_case       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     str:
					# This regression test was failing with PyTorch < 1.3
					lowercase_       =					self.model_tester.prepare_config_and_inputs_for_decoder()

					lowercase_       =					None

					self.model_tester.create_and_check_model_as_decoder(_snake_case       ,							_snake_case       ,							_snake_case       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Tuple:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(_snake_case       ,							_snake_case       ,							_snake_case       )
	def 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_causal_lm(*_snake_case       )
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_snake_case       )
	def 						_lowercase  (       self  :			Tuple       )       ->     Dict:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_snake_case       )
	def 						_lowercase  (       self  :			List[str]       )       ->     int:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_snake_case       )
	@unittest.skip(reason='''Feed forward chunking is not implemented'''       )
	def 						_lowercase  (       self  :			Any       )       ->     List[str]:
					pass




	@parameterized.expand([('''linear''',), ('''dynamic''',)]       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     List[Any]:
					lowercase_       =					self.model_tester.prepare_config_and_inputs_for_common()
					lowercase_       =					ids_tensor([1, 1_0]       ,							config.vocab_size       )
					lowercase_       =					ids_tensor([1, int(config.max_position_embeddings * 1.5       )]       ,							config.vocab_size       )

					set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
					lowercase_       =					GPTNeoXModel(_snake_case       )
					original_model.to(_snake_case       )
					original_model.eval()
					lowercase_       =					original_model(_snake_case       ).last_hidden_state
					lowercase_       =					original_model(_snake_case       ).last_hidden_state

					set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
					lowercase_       =					{'type': scaling_type, 'factor': 1_0.0}
					lowercase_       =					GPTNeoXModel(_snake_case       )
					scaled_model.to(_snake_case       )
					scaled_model.eval()
					lowercase_       =					scaled_model(_snake_case       ).last_hidden_state
					lowercase_       =					scaled_model(_snake_case       ).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(_snake_case       ,							_snake_case       ,							atol=1e-5       )       )
					else:
									self.assertFalse(torch.allclose(_snake_case       ,							_snake_case       ,							atol=1e-5       )       )

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

@require_torch
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




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

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

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

									lowercase_       =					model.generate(**_snake_case       ,							do_sample=_snake_case       ,							max_new_tokens=2_0       )
									lowercase_       =					tokenizer.batch_decode(_snake_case       )[0]

									self.assertEqual(_snake_case       ,							_snake_case       )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :Optional[int]        =      logging.get_logger(__name__)

a_   :Dict        =      {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""openai-gpt"""
     _SCREAMING_SNAKE_CASE						=	{
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(       self  :  Optional[int], _snake_case  :  Dict=4_0_4_7_8, _snake_case  :  str=5_1_2, _snake_case  :  int=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Any=1_2, _snake_case  :  str="gelu", _snake_case  :  List[str]=0.1, _snake_case  :  Any=0.1, _snake_case  :  Dict=0.1, _snake_case  :  int=1e-5, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[Any]="cls_index", _snake_case  :  Any=True, _snake_case  :  Any=None, _snake_case  :  int=True, _snake_case  :  Optional[Any]=0.1, **_snake_case  :  List[Any], )   ->Optional[int]:
            snake_case__							:	int   	=	vocab_size
            snake_case__							:	Dict   	=	n_positions
            snake_case__							:	str   	=	n_embd
            snake_case__							:	str   	=	n_layer
            snake_case__							:	List[Any]   	=	n_head
            snake_case__							:	List[Any]   	=	afn
            snake_case__							:	Optional[Any]   	=	resid_pdrop
            snake_case__							:	List[str]   	=	embd_pdrop
            snake_case__							:	List[Any]   	=	attn_pdrop
            snake_case__							:	Optional[int]   	=	layer_norm_epsilon
            snake_case__							:	str   	=	initializer_range
            snake_case__							:	List[str]   	=	summary_type
            snake_case__							:	Optional[int]   	=	summary_use_proj
            snake_case__							:	List[str]   	=	summary_activation
            snake_case__							:	Optional[Any]   	=	summary_first_dropout
            snake_case__							:	int   	=	summary_proj_to_labels
            super().__init__(**_snake_case					)






