



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"gpt_neox"






							def __init__(   self			,     a_=5_0_4_3_2			,     a_=6_1_4_4			,     a_=4_4			,     a_=6_4			,     a_=2_4_5_7_6			,     a_="gelu"			,     a_=0.25			,     a_=1_0_0_0_0			,     a_=0.0			,     a_=0.0			,     a_=0.1			,     a_=2_0_4_8			,     a_=0.02			,     a_=1e-5			,     a_=True			,     a_=0			,     a_=2			,     a_=False			,     a_=True			,     a_=None			,     **a_			,     ):
									super().__init__(bos_token_id=a_			,     eos_token_id=a_			,     **a_      )
									a_     :      List[Any] 						=     vocab_size
									a_     :      Dict 						=     max_position_embeddings
									a_     :      List[Any] 						=     hidden_size
									a_     :      Tuple 						=     num_hidden_layers
									a_     :      int 						=     num_attention_heads
									a_     :      int 						=     intermediate_size
									a_     :      int 						=     hidden_act
									a_     :      str 						=     rotary_pct
									a_     :      int 						=     rotary_emb_base
									a_     :      Optional[int] 						=     attention_dropout
									a_     :      Union[str, Any] 						=     hidden_dropout
									a_     :      Union[str, Any] 						=     classifier_dropout
									a_     :      Union[str, Any] 						=     initializer_range
									a_     :      List[str] 						=     layer_norm_eps
									a_     :      Optional[Any] 						=     use_cache
									a_     :      Optional[Any] 						=     tie_word_embeddings
									a_     :      Union[str, Any] 						=     use_parallel_residual
									a_     :      List[Any] 						=     rope_scaling
									self._rope_scaling_validation()

									if self.hidden_size % self.num_attention_heads != 0:
											raise ValueError(
											    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"      )






							def snake_case_  (   self      ):
									if self.rope_scaling is None:
											return

									if not isinstance(self.rope_scaling			,     a_      ) or len(self.rope_scaling      ) != 2:
											raise ValueError(
											    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
											    F"""got {self.rope_scaling}"""      )
									a_     :      Optional[int] 						=     self.rope_scaling.get("type"			,     a_      )
									a_     :      Tuple 						=     self.rope_scaling.get("factor"			,     a_      )
									if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
											raise ValueError(
											    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""      )
									if rope_scaling_factor is None or not isinstance(a_			,     a_      ) or rope_scaling_factor <= 1.0:
											raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""      )




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2