"""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 lowerCamelCase		:






					'''simple docstring'''


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



					def 			lowerCAmelCase_					(						self			:				Any		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

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

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

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, input_ids, input_mask, token_labels



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Dict:
									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 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()

									SCREAMING_SNAKE_CASE__		  =				True

									return config, input_ids, input_mask, token_labels



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



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Optional[Any]   , _snake_case			:				List[Any]   , _snake_case			:				str		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				True
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXModel(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Tuple		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXForCausalLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXForQuestionAnswering(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				List[Any]   , _snake_case			:				List[str]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.type_sequence_label_size		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				Optional[int]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXForTokenClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				True
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXForCausalLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()

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

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

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

									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , output_hidden_states=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				output_from_no_past["hidden_states"][0]
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , past_key_values=_snake_case   , output_hidden_states=_snake_case   , )["hidden_states"][0]

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



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



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






					'''simple docstring'''


					a   	=		(
					    (
					        GPTNeoXModel,
					        GPTNeoXForCausalLM,
					        GPTNeoXForQuestionAnswering,
					        GPTNeoXForSequenceClassification,
					        GPTNeoXForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		(GPTNeoXForCausalLM,) if is_torch_available() else ()
					a   	=		(
					    {
					        "feature-extraction": GPTNeoXModel,
					        "question-answering": GPTNeoXForQuestionAnswering,
					        "text-classification": GPTNeoXForSequenceClassification,
					        "text-generation": GPTNeoXForCausalLM,
					        "token-classification": GPTNeoXForTokenClassification,
					        "zero-shot": GPTNeoXForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		False
					a   	=		False
					a   	=		False
					a   	=		False



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



					def 			lowerCAmelCase_					(						self			:				int		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(_snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       str:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[str]:
									# This regression test was failing with PyTorch < 1.3
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_decoder()

									SCREAMING_SNAKE_CASE__		  =				None

									self.model_tester.create_and_check_model_as_decoder(_snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Tuple:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				int		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_causal_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Tuple		)							->       Optional[Any]:
									pass



					@parameterized.expand([("linear",), ("dynamic",)]		)
					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict		)							->       Tuple:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([1, 10]   , config.vocab_size		)
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXModel(_snake_case		)
									original_model.to(_snake_case		)
									original_model.eval()
									SCREAMING_SNAKE_CASE__		  =				original_model(_snake_case		).last_hidden_state
									SCREAMING_SNAKE_CASE__		  =				original_model(_snake_case		).last_hidden_state

									set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
									SCREAMING_SNAKE_CASE__		  =				{"type": scaling_type, "factor": 10.0}
									SCREAMING_SNAKE_CASE__		  =				GPTNeoXModel(_snake_case		)
									scaled_model.to(_snake_case		)
									scaled_model.eval()
									SCREAMING_SNAKE_CASE__		  =				scaled_model(_snake_case		).last_hidden_state
									SCREAMING_SNAKE_CASE__		  =				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 lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"		)
									for checkpointing in [True, False]:
													SCREAMING_SNAKE_CASE__		  =				GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"		)

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

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

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

													self.assertEqual(_snake_case   , _snake_case		)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




