





"""simple docstring"""





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
						from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image





class        _lowerCamelCase      :


						def __init__(self   ,							__a   ,							__a=13   ,							__a=64   ,							__a=2   ,							__a=3   ,							__a=True   ,							__a=True   ,							__a=32   ,							__a=5   ,							__a=4   ,							__a=37   ,							__a="gelu"   ,							__a=0.1   ,							__a=0.1   ,							__a=10   ,							__a=0.02   ,							__a=[1, 16, 4, 4]   ,							__a=None   ,							) ->   Tuple:
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	image_size
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	is_training
								UpperCamelCase   =	use_labels
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	num_hidden_layers
								UpperCamelCase   =	num_attention_heads
								UpperCamelCase   =	intermediate_size
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	type_sequence_label_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	scope
								UpperCamelCase   =	backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								UpperCamelCase   =	(self.image_size // 32) ** 2
								UpperCamelCase   =	num_patches + 1


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								UpperCamelCase   =	None
								if self.use_labels:
										UpperCamelCase   =	ids_tensor([self.batch_size]   ,							self.type_sequence_label_size		)

								UpperCamelCase   =	self.get_config()

								return config, pixel_values, labels


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	{
								    "global_padding": "same",
								    "layer_type": "bottleneck",
								    "depths": [3, 4, 9],
								    "out_features": ["stage1", "stage2", "stage3"],
								    "embedding_dynamic_padding": True,
								    "hidden_sizes": [4, 8, 16, 32],
								    "num_groups": 2,
								}

								return ViTHybridConfig(
								    image_size=self.image_size   ,							patch_size=self.patch_size   ,							num_channels=self.num_channels   ,							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   ,							is_decoder=__a   ,							initializer_range=self.initializer_range   ,							backbone_featmap_shape=self.backbone_featmap_shape   ,							backbone_config=__a   ,							)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	ViTHybridModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   int:
								UpperCamelCase   =	self.type_sequence_label_size
								UpperCamelCase   =	ViTHybridForImageClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size)		)




						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs
								UpperCamelCase   =	{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
						UpperCAmelCase_ =			(
						    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	ViTHybridModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							has_text_modality=__a   ,							hidden_size=37		)


						def     snake_case_   (self		) ->   Optional[int]:
								self.config_tester.run_common_tests()


						@unittest.skip(reason="ViT does not use inputs_embeds"		)
						def     snake_case_   (self		) ->   Optional[int]:
								pass


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										UpperCamelCase   =	model_class(__a		)
										self.assertIsInstance(model.get_input_embeddings()   ,							(nn.Module)		)
										UpperCamelCase   =	model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__a   ,							nn.Linear		)		)


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										UpperCamelCase   =	model_class(__a		)
										UpperCamelCase   =	inspect.signature(model.forward		)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										UpperCamelCase   =	[*signature.parameters.keys()]

										UpperCamelCase   =	["pixel_values"]
										self.assertListEqual(arg_names[:1]   ,							__a		)


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a		)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

								UpperCamelCase   =	_config_zero_init(__a		)
								for model_class in self.all_model_classes:
										UpperCamelCase   =	model_class(config=__a		)
										# Skip the check for the backbone
										for name, module in model.named_modules():
												if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
														UpperCamelCase   =	[F"{name}.{key}" for key in module.state_dict().keys()]
														break

										for name, param in model.named_parameters():
												if param.requires_grad:
														if name in backbone_params:
																continue
														self.assertIn(
														    ((param.data.mean() * 1e9).round() / 1e9).item()   ,							[0.0, 1.0]   ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,							)




						@slow
						def     snake_case_   (self		) ->   Tuple:
								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										UpperCamelCase   =	ViTHybridModel.from_pretrained(__a		)
										self.assertIsNotNone(__a		)



def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image






@require_torch
@require_vision
class        _lowerCamelCase      (       unittest.TestCase ):


						@cached_property
						def     snake_case_   (self		) ->   Union[str, Any]:
								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
								    if is_vision_available()
								    else None
								)


						@slow
						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
								    __a		)

								UpperCamelCase   =	self.default_image_processor
								UpperCamelCase   =	prepare_img()
								UpperCamelCase   =	image_processor(images=__a   ,							return_tensors="pt"		).to(__a		)

								# forward pass
								with torch.no_grad():
										UpperCamelCase   =	model(**__a		)

								# verify the logits
								UpperCamelCase   =	torch.Size((1, 10_00)		)
								self.assertEqual(outputs.logits.shape   ,							__a		)

								UpperCamelCase   =	torch.tensor([-1.9090, -0.4993, -0.2389]		).to(__a		)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,							__a   ,							atol=1e-4		)		)




						@slow
						@require_accelerate
						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
								UpperCamelCase   =	ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"   ,							device_map="auto"		)

								UpperCamelCase   =	prepare_img()

								UpperCamelCase   =	image_processor(images=__a   ,							return_tensors="pt"		)
								UpperCamelCase   =	model(**__a		)
								UpperCamelCase   =	outputs.logits
								# model predicts one of the 1000 ImageNet classes
								UpperCamelCase   =	logits.argmax(-1		).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]   ,							"tabby, tabby cat"		)













"""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      :


						def __init__(self   ,							__a   ,							__a=13   ,							__a=7   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=99   ,							__a=64   ,							__a=5   ,							__a=4   ,							__a=37   ,							__a="gelu"   ,							__a=0.1   ,							__a=0.1   ,							__a=5_12   ,							__a=16   ,							__a=2   ,							__a=0.02   ,							__a=3   ,							__a=4   ,							__a=None   ,							) ->   Optional[int]:
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	seq_length
								UpperCamelCase   =	is_training
								UpperCamelCase   =	use_input_mask
								UpperCamelCase   =	use_token_type_ids
								UpperCamelCase   =	use_labels
								UpperCamelCase   =	vocab_size
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	num_hidden_layers
								UpperCamelCase   =	num_attention_heads
								UpperCamelCase   =	intermediate_size
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	max_position_embeddings
								UpperCamelCase   =	type_vocab_size
								UpperCamelCase   =	type_sequence_label_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	num_labels
								UpperCamelCase   =	num_choices
								UpperCamelCase   =	scope
								UpperCamelCase   =	vocab_size - 1


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

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

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

								UpperCamelCase   =	self.get_config()

								return config, input_ids, input_mask, token_labels


						def     snake_case_   (self		) ->   int:
								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		) ->   int:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.prepare_config_and_inputs()

								UpperCamelCase   =	True

								return config, input_ids, input_mask, token_labels


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	GPTNeoXModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a		)
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	True
								UpperCamelCase   =	GPTNeoXModel(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	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   ,							__a   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	GPTNeoXForCausalLM(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	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   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForQuestionAnswering(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	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   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForSequenceClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	ids_tensor([self.batch_size]   ,							self.type_sequence_label_size		)
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForTokenClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	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   ,							__a   ,							__a   ,							__a		) ->   Any:
								UpperCamelCase   =	True
								UpperCamelCase   =	GPTNeoXForCausalLM(config=__a		)
								model.to(__a		)
								model.eval()

								# first forward pass
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							use_cache=__a		)
								UpperCamelCase   =	outputs.past_key_values

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

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

								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							output_hidden_states=__a		)
								UpperCamelCase   =	output_from_no_past["hidden_states"][0]
								UpperCamelCase   =	model(
								    __a   ,							attention_mask=__a   ,							past_key_values=__a   ,							output_hidden_states=__a   ,							)["hidden_states"][0]

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





@require_torch
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase_ =			(GPTNeoXForCausalLM,) if is_torch_available() else ()
						UpperCAmelCase_ =			(
						    {
						        "feature-extraction": GPTNeoXModel,
						        "question-answering": GPTNeoXForQuestionAnswering,
						        "text-classification": GPTNeoXForSequenceClassification,
						        "text-generation": GPTNeoXForCausalLM,
						        "token-classification": GPTNeoXForTokenClassification,
						        "zero-shot": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	GPTNeoXModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							hidden_size=64   ,							num_attention_heads=8		)


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


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__a   ,							__a   ,							__a		)


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

								UpperCamelCase   =	None

								self.model_tester.create_and_check_model_as_decoder(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	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		) ->   Any:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	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		) ->   Any:
								pass




						@parameterized.expand([("linear",), ("dynamic",)]		)
						def     snake_case_   (self   ,							__a		) ->   Dict:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCamelCase   =	ids_tensor([1, 10]   ,							config.vocab_size		)
								UpperCamelCase   =	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
								UpperCamelCase   =	GPTNeoXModel(__a		)
								original_model.to(__a		)
								original_model.eval()
								UpperCamelCase   =	original_model(__a		).last_hidden_state
								UpperCamelCase   =	original_model(__a		).last_hidden_state

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


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

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

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

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

										self.assertEqual(__a   ,							__a		)






