




'''simple docstring'''


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort







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






      @property
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )






      @property
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  ort.SessionOptions()
           a_     :							Tuple     						=  False
           return options






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
           a_     :							Union[str, Any]     						=  load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
           a_     :							Union[str, Any]     						=  load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

           # using the PNDM scheduler by default
           a_     :							int     						=  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
               """CompVis/stable-diffusion-v1-4"""						,						revision="""onnx"""						,						safety_checker=_lowercase						,						feature_extractor=_lowercase						,						provider=self.gpu_provider						,						sess_options=self.gpu_options						,						)
           pipe.set_progress_bar_config(disable=_lowercase       )

           a_     :							str     						=  """A red cat sitting on a park bench"""

           a_     :							List[str]     						=  np.random.RandomState(0       )
           a_     :							Union[str, Any]     						=  pipe(
               prompt=_lowercase						,						image=_lowercase						,						mask_image=_lowercase						,						strength=0.75						,						guidance_scale=7.5						,						num_inference_steps=15						,						generator=_lowercase						,						output_type="""np"""						,						)

           a_     :							str     						=  output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image       ).max() < 1e-2



'''simple docstring'''

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				data
											SCREAMING_SNAKE_CASE_				=				[0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]



				@staticmethod
				def   a__					(    _lowercase,     _lowercase       )     ->							List[str]:
											return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				B'\x80' + B'\x00' * (63 - (len(self.data       ) + 8) % 64)
											SCREAMING_SNAKE_CASE_				=				self.data + padding + struct.pack('>Q',     8 * len(self.data       )       )
											return padded_data



				def   a__					(    self       )     ->							Optional[Any]:
											return [
											    self.padded_data[i : i + 64] for i in range(0,     len(self.padded_data       ),     64       )
											]



				def   a__					(    self,     _lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				list(struct.unpack('>16L',     _lowercase       )       ) + [0] * 64
											for i in range(16,     80       ):
																		SCREAMING_SNAKE_CASE_				=				self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),     1       )
											return w





				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.padding()
											SCREAMING_SNAKE_CASE_				=				self.split_blocks()
											for block in self.blocks:
																		SCREAMING_SNAKE_CASE_				=				self.expand_block(_lowercase       )
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.h
																		for i in range(0,     80       ):
																									if 0 <= i < 20:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | ((~b) & d)
																																SCREAMING_SNAKE_CASE_				=				0x5_a_8_2_7_9_9_9
																									elif 20 <= i < 40:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0x6_e_d_9_e_b_a_1
																									elif 40 <= i < 60:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | (b & d) | (c & d)
																																SCREAMING_SNAKE_CASE_				=				0x8_f_1_b_b_c_d_c
																									elif 60 <= i < 80:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0xc_a_6_2_c_1_d_6
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				(
																									    self.rotate(_lowercase,     5       ) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																									    a,
																									    self.rotate(_lowercase,     30       ),
																									    c,
																									    d,
																									)
																		SCREAMING_SNAKE_CASE_				=				(
																		    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
																		    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
																		    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
																		    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
																		    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
																		)
											return ("{:08x}" * 5).format(*self.h       )




def 							_UpperCamelCase				(   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				B'Test String'
							assert SHAaHash(lowerCAmelCase__   ).final_hash() == hashlib.shaa(lowerCAmelCase__   ).hexdigest()  # noqa: S324




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser(description='Process some strings or files'   )
							parser.add_argument(
							    '--string'		,dest='input_string'		,default='Hello World!! Welcome to Cryptography'		,help='Hash the string'		,)
							parser.add_argument('--file'		,dest='input_file'		,help='Hash contents of a file'   )
							SCREAMING_SNAKE_CASE_				=				parser.parse_args()
							SCREAMING_SNAKE_CASE_				=				args.input_string
							# In any case hash input should be a bytestring
							if args.input_file:
														with open(args.input_file		,'rb'   ) as f:
																					SCREAMING_SNAKE_CASE_				=				f.read()
							else:
														SCREAMING_SNAKE_CASE_				=				bytes(lowerCAmelCase__		,'utf-8'   )
							print(SHAaHash(lowerCAmelCase__   ).final_hash()   )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod()





