




'''simple docstring'''


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    AutoencoderKL
						__UpperCAmelCase			     =    """sample"""
						__UpperCAmelCase			     =    1e-2







						@property
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  4
								snake_case__						:       Optional[Any]       						=  3
								snake_case__						:       List[Any]       						=  (3_2, 3_2)

								snake_case__						:       Dict       						=  floats_tensor((batch_size, num_channels) + sizes  ).to(snake_case_  )

								return {"sample": image}







						@property
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								return (3, 3_2, 3_2)







						@property
						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								return (3, 3_2, 3_2)







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								snake_case__						:       Dict       						=  self.dummy_input
								return init_dict, inputs_dict







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						@unittest.skipIf(torch_device == '''mps'''			,			'''Gradient checkpointing skipped on MPS'''  )
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.prepare_init_args_and_inputs_for_common()
								snake_case__						:       Any       						=  self.model_class(**snake_case_  )
								model.to(snake_case_  )

								assert not model.is_gradient_checkpointing and model.training

								snake_case__						:       Dict       						=  model(**snake_case_  ).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model.zero_grad()

								snake_case__						:       List[Any]       						=  torch.randn_like(snake_case_  )
								snake_case__						:       int       						=  (out - labels).mean()
								loss.backward()

								# re-instantiate the model now enabling gradient checkpointing
								snake_case__						:       Optional[int]       						=  self.model_class(**snake_case_  )
								# clone model
								model_a.load_state_dict(model.state_dict()  )
								model_a.to(snake_case_  )
								model_a.enable_gradient_checkpointing()

								assert model_a.is_gradient_checkpointing and model_a.training

								snake_case__						:       Optional[Any]       						=  model_a(**snake_case_  ).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model_a.zero_grad()
								snake_case__						:       str       						=  (out_a - labels).mean()
								loss_a.backward()

								# compare the output and parameters gradients
								self.assertTrue((loss - loss_a).abs() < 1e-5  )
								snake_case__						:       Optional[int]       						=  dict(model.named_parameters()  )
								snake_case__						:       Dict       						=  dict(model_a.named_parameters()  )
								for name, param in named_params.items():
										self.assertTrue(torch_all_close(param.grad.data			,			named_params_a[name].grad.data			,			atol=5e-5  )  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''			,			output_loading_info=snake_case_  )
								self.assertIsNotNone(snake_case_  )
								self.assertEqual(len(loading_info['''missing_keys''']  )			,			0  )

								model.to(snake_case_  )
								snake_case__						:       Optional[int]       						=  model(**self.dummy_input  )

								assert image is not None, "Make sure output is not None"







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''  )
								snake_case__						:       List[Any]       						=  model.to(snake_case_  )
								model.eval()

								if torch_device == "mps":
										snake_case__						:       List[str]       						=  torch.manual_seed(0  )
								else:
										snake_case__						:       int       						=  torch.Generator(device=snake_case_  ).manual_seed(0  )

								snake_case__						:       Union[str, Any]       						=  torch.randn(
								    1			,			model.config.in_channels			,			model.config.sample_size			,			model.config.sample_size			,			generator=torch.manual_seed(0  )			,			)
								snake_case__						:       Union[str, Any]       						=  image.to(snake_case_  )
								with torch.no_grad():
										snake_case__						:       Optional[Any]       						=  model(snake_case_			,			sample_posterior=snake_case_			,			generator=snake_case_  ).sample

								snake_case__						:       Optional[Any]       						=  output[0, -1, -3:, -3:].flatten().cpu()

								# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
								# the expected output slices are not the same for CPU and GPU.
								if torch_device == "mps":
										snake_case__						:       Dict       						=  torch.tensor(
										    [
										        -4.0_078e-01,
										        -3.8_323e-04,
										        -1.2_681e-01,
										        -1.1_462e-01,
										        2.0_095e-01,
										        1.0_893e-01,
										        -8.8_247e-02,
										        -3.0_361e-01,
										        -9.8_644e-03,
										    ]  )
								elif torch_device == "cpu":
										snake_case__						:       Tuple       						=  torch.tensor(
										    [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]  )
								else:
										snake_case__						:       List[str]       						=  torch.tensor(
										    [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]  )

								self.assertTrue(torch_all_close(snake_case_			,			snake_case_			,			rtol=1e-2  )  )





@slow
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      List[Any]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								return F"""gaussian_noise_s={seed}_shape={'_'.join([str(snake_case_  ) for s in shape]  )}.npy"""







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      Union[str, Any]=0			,			snake_case_					:      str=(4, 3, 5_1_2, 5_1_2)			,			snake_case_					:      Any=False  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  torch.floataa if fpaa else torch.floataa
								snake_case__						:       Optional[int]       						=  torch.from_numpy(load_hf_numpy(self.get_file_format(snake_case_			,			snake_case_  )  )  ).to(snake_case_  ).to(snake_case_  )
								return image







						def       __magic_name__							(							self					:      str			,			snake_case_					:      Dict="CompVis/stable-diffusion-v1-4"			,			snake_case_					:      int=False  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  '''fp16''' if fpaa else None
								snake_case__						:       Optional[Any]       						=  torch.floataa if fpaa else torch.floataa

								snake_case__						:       Optional[int]       						=  AutoencoderKL.from_pretrained(
								    snake_case_			,			subfolder='''vae'''			,			torch_dtype=snake_case_			,			revision=snake_case_			,			)
								model.to(snake_case_  ).eval()

								return model







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Any=0  ):



								'''simple docstring'''



								if torch_device == "mps":
										return torch.manual_seed(snake_case_  )
								return torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )







						@parameterized.expand(
						    [
						        # fmt: off
						        [3_3, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
						        [4_7, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
						        # fmt: on
						    ]  )
						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      List[str]			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.get_sd_vae_model()
								snake_case__						:       Optional[int]       						=  self.get_sd_image(snake_case_  )
								snake_case__						:       str       						=  self.get_generator(snake_case_  )

								with torch.no_grad():
										snake_case__						:       str       						=  model(snake_case_			,			generator=snake_case_			,			sample_posterior=snake_case_  ).sample

								assert sample.shape == image.shape

								snake_case__						:       List[str]       						=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
								snake_case__						:       Optional[Any]       						=  torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice  )

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=3e-3  )







						@parameterized.expand(
						    [
						        # fmt: off
						        [3_3, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
						        [4_7, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
						        # fmt: on
						    ]  )
						@require_torch_gpu
						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.get_sd_vae_model(fpaa=snake_case_  )
								snake_case__						:       Tuple       						=  self.get_sd_image(snake_case_			,			fpaa=snake_case_  )
								snake_case__						:       int       						=  self.get_generator(snake_case_  )

								with torch.no_grad():
										snake_case__						:       Any       						=  model(snake_case_			,			generator=snake_case_			,			sample_posterior=snake_case_  ).sample

								assert sample.shape == image.shape

								snake_case__						:       Optional[Any]       						=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
								snake_case__						:       List[str]       						=  torch.tensor(snake_case_  )

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=1e-2  )







						@parameterized.expand(
						    [
						        # fmt: off
						        [3_3, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
						        [4_7, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
						        # fmt: on
						    ]  )
						def       __magic_name__							(							self					:      str			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Any			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.get_sd_vae_model()
								snake_case__						:       List[str]       						=  self.get_sd_image(snake_case_  )

								with torch.no_grad():
										snake_case__						:       List[Any]       						=  model(snake_case_  ).sample

								assert sample.shape == image.shape

								snake_case__						:       Optional[Any]       						=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
								snake_case__						:       str       						=  torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice  )

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=3e-3  )







						@parameterized.expand(
						    [
						        # fmt: off
						        [1_3, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
						        [3_7, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
						        # fmt: on
						    ]  )
						@require_torch_gpu
						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      List[str]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.get_sd_vae_model()
								snake_case__						:       int       						=  self.get_sd_image(snake_case_			,			shape=(3, 4, 6_4, 6_4)  )

								with torch.no_grad():
										snake_case__						:       Tuple       						=  model.decode(snake_case_  ).sample

								assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

								snake_case__						:       Tuple       						=  sample[-1, -2:, :2, -2:].flatten().cpu()
								snake_case__						:       List[str]       						=  torch.tensor(snake_case_  )

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=1e-3  )







						@parameterized.expand(
						    [
						        # fmt: off
						        [2_7, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
						        [1_6, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
						        # fmt: on
						    ]  )
						@require_torch_gpu
						def       __magic_name__							(							self					:      int			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.get_sd_vae_model(fpaa=snake_case_  )
								snake_case__						:       List[Any]       						=  self.get_sd_image(snake_case_			,			shape=(3, 4, 6_4, 6_4)			,			fpaa=snake_case_  )

								with torch.no_grad():
										snake_case__						:       Tuple       						=  model.decode(snake_case_  ).sample

								assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

								snake_case__						:       List[Any]       						=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
								snake_case__						:       List[str]       						=  torch.tensor(snake_case_  )

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=5e-3  )







						@parameterized.expand([(1_3,), (1_6,), (2_7,)]  )
						@require_torch_gpu
						@unittest.skipIf(not is_xformers_available()			,			reason='''xformers is not required when using PyTorch 2.0.'''  )
						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.get_sd_vae_model(fpaa=snake_case_  )
								snake_case__						:       List[Any]       						=  self.get_sd_image(snake_case_			,			shape=(3, 4, 6_4, 6_4)			,			fpaa=snake_case_  )

								with torch.no_grad():
										snake_case__						:       Tuple       						=  model.decode(snake_case_  ).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
										snake_case__						:       List[Any]       						=  model.decode(snake_case_  ).sample

								assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=1e-1  )







						@parameterized.expand([(1_3,), (1_6,), (3_7,)]  )
						@require_torch_gpu
						@unittest.skipIf(not is_xformers_available()			,			reason='''xformers is not required when using PyTorch 2.0.'''  )
						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.get_sd_vae_model()
								snake_case__						:       Tuple       						=  self.get_sd_image(snake_case_			,			shape=(3, 4, 6_4, 6_4)  )

								with torch.no_grad():
										snake_case__						:       Optional[Any]       						=  model.decode(snake_case_  ).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
										snake_case__						:       Optional[Any]       						=  model.decode(snake_case_  ).sample

								assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

								assert torch_all_close(snake_case_			,			snake_case_			,			atol=1e-2  )







						@parameterized.expand(
						    [
						        # fmt: off
						        [3_3, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
						        [4_7, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
						        # fmt: on
						    ]  )
						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.get_sd_vae_model()
								snake_case__						:       Optional[Any]       						=  self.get_sd_image(snake_case_  )
								snake_case__						:       str       						=  self.get_generator(snake_case_  )

								with torch.no_grad():
										snake_case__						:       List[str]       						=  model.encode(snake_case_  ).latent_dist
										snake_case__						:       List[str]       						=  dist.sample(generator=snake_case_  )

								assert list(sample.shape  ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

								snake_case__						:       Union[str, Any]       						=  sample[0, -1, -3:, -3:].flatten().cpu()
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_  )

								snake_case__						:       List[str]       						=  3e-3 if torch_device != '''mps''' else 1e-2
								assert torch_all_close(snake_case_			,			snake_case_			,			atol=snake_case_  )






'''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     a			:






						"""simple docstring"""




						def __init__(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      Optional[Any]=1_3			,			snake_case_					:      int=7			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Dict=True			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=9_9			,			snake_case_					:      int=6_4			,			snake_case_					:      Dict=5			,			snake_case_					:      List[Any]=4			,			snake_case_					:      Union[str, Any]=3_7			,			snake_case_					:      Dict="gelu"			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      Dict=0.1			,			snake_case_					:      Any=5_1_2			,			snake_case_					:      Any=1_6			,			snake_case_					:      Any=2			,			snake_case_					:      Dict=0.0_2			,			snake_case_					:      List[str]=3			,			snake_case_					:      Optional[int]=4			,			snake_case_					:      str=None			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  parent
								snake_case__						:       int       						=  batch_size
								snake_case__						:       Dict       						=  seq_length
								snake_case__						:       int       						=  is_training
								snake_case__						:       Optional[Any]       						=  use_input_mask
								snake_case__						:       Optional[Any]       						=  use_token_type_ids
								snake_case__						:       Dict       						=  use_labels
								snake_case__						:       int       						=  vocab_size
								snake_case__						:       Any       						=  hidden_size
								snake_case__						:       int       						=  num_hidden_layers
								snake_case__						:       Union[str, Any]       						=  num_attention_heads
								snake_case__						:       List[Any]       						=  intermediate_size
								snake_case__						:       int       						=  hidden_act
								snake_case__						:       int       						=  hidden_dropout_prob
								snake_case__						:       List[Any]       						=  attention_probs_dropout_prob
								snake_case__						:       Optional[int]       						=  max_position_embeddings
								snake_case__						:       Optional[int]       						=  type_vocab_size
								snake_case__						:       Any       						=  type_sequence_label_size
								snake_case__						:       str       						=  initializer_range
								snake_case__						:       List[str]       						=  num_labels
								snake_case__						:       Dict       						=  num_choices
								snake_case__						:       Union[str, Any]       						=  scope
								snake_case__						:       List[Any]       						=  vocab_size - 1







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size  )

								snake_case__						:       Optional[int]       						=  None
								if self.use_input_mask:
										snake_case__						:       int       						=  random_attention_mask([self.batch_size, self.seq_length]  )

								snake_case__						:       Union[str, Any]       						=  None
								if self.use_labels:
										snake_case__						:       Optional[Any]       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels  )

								snake_case__						:       Dict       						=  self.get_config()

								return config, input_ids, input_mask, token_labels







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								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       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       str       						=  self.prepare_config_and_inputs()

								snake_case__						:       List[str]       						=  True

								return config, input_ids, input_mask, token_labels







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Any			,			snake_case_					:      str			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  GPTNeoXModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Union[str, Any]       						=  model(snake_case_			,			attention_mask=snake_case_  )
								snake_case__						:       int       						=  model(snake_case_  )
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  True
								snake_case__						:       Tuple       						=  GPTNeoXModel(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Dict       						=  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       __magic_name__							(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  GPTNeoXForCausalLM(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Tuple       						=  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       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      str			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.num_labels
								snake_case__						:       List[Any]       						=  GPTNeoXForQuestionAnswering(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Any       						=  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       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.num_labels
								snake_case__						:       List[str]       						=  GPTNeoXForSequenceClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Tuple       						=  ids_tensor([self.batch_size]			,			self.type_sequence_label_size  )
								snake_case__						:       str       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      Dict			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.num_labels
								snake_case__						:       Any       						=  GPTNeoXForTokenClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Optional[Any]       						=  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       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  True
								snake_case__						:       Union[str, Any]       						=  GPTNeoXForCausalLM(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()

								# first forward pass
								snake_case__						:       int       						=  model(snake_case_			,			attention_mask=snake_case_			,			use_cache=snake_case_  )
								snake_case__						:       Dict       						=  outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								snake_case__						:       Tuple       						=  ids_tensor((self.batch_size, 3)			,			config.vocab_size  )
								snake_case__						:       Union[str, Any]       						=  ids_tensor((self.batch_size, 3)			,			vocab_size=2  )

								# append to next input_ids and
								snake_case__						:       Dict       						=  torch.cat([input_ids, next_tokens]			,			dim=-1  )
								snake_case__						:       Optional[int]       						=  torch.cat([input_mask, next_mask]			,			dim=-1  )

								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			attention_mask=snake_case_			,			output_hidden_states=snake_case_  )
								snake_case__						:       Union[str, Any]       						=  output_from_no_past['''hidden_states'''][0]
								snake_case__						:       str       						=  model(
								    snake_case_			,			attention_mask=snake_case_			,			past_key_values=snake_case_			,			output_hidden_states=snake_case_			,			)['''hidden_states'''][0]

								# select random slice
								snake_case__						:       str       						=  ids_tensor((1,)			,			output_from_past.shape[-1]  ).item()
								snake_case__						:       str       						=  output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case__						:       Union[str, Any]       						=  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       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Tuple       						=  config_and_inputs
								snake_case__						:       str       						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__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       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  GPTNeoXModelTester(self  )
								snake_case__						:       List[str]       						=  ConfigTester(self			,			config_class=snake_case_			,			hidden_size=6_4			,			num_attention_heads=8  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  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       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case__						:       Optional[int]       						=  None

								self.model_tester.create_and_check_model_as_decoder(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Dict       						=  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       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  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       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						@parameterized.expand([('''linear''',), ('''dynamic''',)]  )
						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       List[Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()
								snake_case__						:       Dict       						=  ids_tensor([1, 1_0]			,			config.vocab_size  )
								snake_case__						:       List[Any]       						=  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
								snake_case__						:       Tuple       						=  GPTNeoXModel(snake_case_  )
								original_model.to(snake_case_  )
								original_model.eval()
								snake_case__						:       Any       						=  original_model(snake_case_  ).last_hidden_state
								snake_case__						:       List[str]       						=  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
								snake_case__						:       Optional[Any]       						=  {'''type''': scaling_type, '''factor''': 1_0.0}
								snake_case__						:       Optional[Any]       						=  GPTNeoXModel(snake_case_  )
								scaled_model.to(snake_case_  )
								scaled_model.eval()
								snake_case__						:       Optional[int]       						=  scaled_model(snake_case_  ).last_hidden_state
								snake_case__						:       List[str]       						=  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     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@slow
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )
								for checkpointing in [True, False]:
										snake_case__						:       str       						=  GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )

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

										snake_case__						:       Tuple       						=  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
										snake_case__						:       List[str]       						=  '''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

										snake_case__						:       Optional[int]       						=  model.generate(**snake_case_			,			do_sample=snake_case_			,			max_new_tokens=2_0  )
										snake_case__						:       Tuple       						=  tokenizer.batch_decode(snake_case_  )[0]

										self.assertEqual(snake_case_			,			snake_case_  )



