'''simple docstring'''







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						# FIXME: add fast tests
						pass




@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				List[Any]:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)




						@property
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = ort.SessionOptions()
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = False
													return options




						def      __lowerCamelCase       (    self :       Tuple      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    'runwayml/stable-diffusion-inpainting'							,							revision='onnx'							,							safety_checker=UpperCamelCase_							,							feature_extractor=UpperCamelCase_							,							provider=self.gpu_provider							,							sess_options=self.gpu_options							,							)
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = 'A red cat sitting on a park bench'

													SCREAMING_SNAKE_CASE__     :Tuple						    = np.random.RandomState(0      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = pipe(
													    prompt=UpperCamelCase_							,							image=UpperCamelCase_							,							mask_image=UpperCamelCase_							,							guidance_scale=7.5							,							num_inference_steps=10							,							generator=UpperCamelCase_							,							output_type='np'							,							)
													SCREAMING_SNAKE_CASE__     :List[str]						    = output.images
													SCREAMING_SNAKE_CASE__     :int						    = images[0, 2_55:2_58, 2_55:2_58, -1]

													assert images.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :str						    = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       str      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'      )
													SCREAMING_SNAKE_CASE__     :Dict						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = LMSDiscreteScheduler.from_pretrained(
													    'runwayml/stable-diffusion-inpainting'							,							subfolder='scheduler'							,							revision='onnx'      )
													SCREAMING_SNAKE_CASE__     :str						    = OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    'runwayml/stable-diffusion-inpainting'							,							revision='onnx'							,							scheduler=UpperCamelCase_							,							safety_checker=UpperCamelCase_							,							feature_extractor=UpperCamelCase_							,							provider=self.gpu_provider							,							sess_options=self.gpu_options							,							)
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 'A red cat sitting on a park bench'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.random.RandomState(0      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = pipe(
													    prompt=UpperCamelCase_							,							image=UpperCamelCase_							,							mask_image=UpperCamelCase_							,							guidance_scale=7.5							,							num_inference_steps=20							,							generator=UpperCamelCase_							,							output_type='np'							,							)
													SCREAMING_SNAKE_CASE__     :str						    = output.images
													SCREAMING_SNAKE_CASE__     :str						    = images[0, 2_55:2_58, 2_55:2_58, -1]

													assert images.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :Tuple						    = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3







'''simple docstring'''







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


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''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 _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= 'gpt_neox'




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       int=5_04_32							,							UpperCamelCase_ :       Dict=61_44							,							UpperCamelCase_ :       int=44							,							UpperCamelCase_ :       Any=64							,							UpperCamelCase_ :       Optional[int]=2_45_76							,							UpperCamelCase_ :       str="gelu"							,							UpperCamelCase_ :       Tuple=0.25							,							UpperCamelCase_ :       Tuple=1_00_00							,							UpperCamelCase_ :       Tuple=0.0							,							UpperCamelCase_ :       Optional[int]=0.0							,							UpperCamelCase_ :       str=0.1							,							UpperCamelCase_ :       List[Any]=20_48							,							UpperCamelCase_ :       List[str]=0.02							,							UpperCamelCase_ :       Optional[int]=1e-5							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       Optional[Any]=0							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       str=False							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       Tuple=None							,							**UpperCamelCase_ :       Any							,							)     ->				int:
													super().__init__(bos_token_id=UpperCamelCase_							,							eos_token_id=UpperCamelCase_							,							**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Dict						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Dict						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rotary_pct
													SCREAMING_SNAKE_CASE__     :Any						    = rotary_emb_base
													SCREAMING_SNAKE_CASE__     :Dict						    = attention_dropout
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_dropout
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = classifier_dropout
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[str]						    = layer_norm_eps
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_cache
													SCREAMING_SNAKE_CASE__     :Any						    = tie_word_embeddings
													SCREAMING_SNAKE_CASE__     :List[str]						    = use_parallel_residual
													SCREAMING_SNAKE_CASE__     :int						    = 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      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Union[str, Any]:
													if self.rope_scaling is None:
																				return

													if not isinstance(self.rope_scaling							,							UpperCamelCase_      ) 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}'''      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.rope_scaling.get('type'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.rope_scaling.get('factor'							,							UpperCamelCase_      )
													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(UpperCamelCase_							,							UpperCamelCase_      ) or rope_scaling_factor <= 1.0:
																				raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )






