




'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Dict:


														"""simple docstring"""


														_a      	=							inspect.getfile(accelerate.test_utils   )
														_a      	=							os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_script.py''']   )
														_a      	=							os.path.sep.join(
														    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']   )
														_a      	=							os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_ops.py''']   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Any:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices.'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices.'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
														print(F'Command: {cmd}'   )
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
														with patch_environment(omp_num_threads=1				,     cuda_visible_devices='''0,1'''   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )


if __name__ == "__main__":
				_snake_case : str  				=						Accelerator()
				_snake_case : str  				=						(accelerator.state.process_index + 2, 10)
				_snake_case : Optional[Any]  				=						torch.randint(0, 10, shape).to(accelerator.device)

				_snake_case : Dict  				=						''

				_snake_case : Optional[int]  				=						accelerator.pad_across_processes(tensor)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
								error_msg += "Padding was not done with the right value (0)."

				_snake_case : str  				=						accelerator.pad_across_processes(tensor, pad_first=True)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				_snake_case : int  				=						accelerator.state.num_processes - accelerator.state.process_index - 1
				if not torch.equal(tensora[index:], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[:index] == 0):
								error_msg += "Padding was not done with the right value (0)."

				# Raise error at the end to make sure we don't stop at the first failure.
				if len(error_msg) > 0:
								raise ValueError(error_msg)







'''simple docstring'''


from collections.abc import Generator
from math import sin
def 						snake_case_       (UpperCamelCase			:    bytes ):





							'''simple docstring'''


							if len(UpperCamelCase ) != 32:
														raise ValueError('''Input must be of length 32''' )

							_a      	=							B''''''
							for i in [3, 2, 1, 0]:
														little_endian += string_aa[8 * i : 8 * i + 8]
							return little_endian
def 						snake_case_       (UpperCamelCase			:    int ):





							'''simple docstring'''


							if i < 0:
														raise ValueError('''Input must be non-negative''' )

							_a      	=							format(UpperCamelCase					,		'''08x''' )[-8:]
							_a      	=							B''''''
							for i in [3, 2, 1, 0]:
														little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8''' )
							return little_endian_hex
def 						snake_case_       (UpperCamelCase			:    bytes ):





							'''simple docstring'''


							_a      	=							B''''''
							for char in message:
														bit_string += format(UpperCamelCase					,		'''08b''' ).encode('''utf-8''' )
							_a      	=							format(len(UpperCamelCase )					,		'''064b''' ).encode('''utf-8''' )

							# Pad bit_string to a multiple of 512 chars
							bit_string += b"1"
							while len(UpperCamelCase ) % 512 != 448:
														bit_string += b"0"
							bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

							return bit_string
def 						snake_case_       (UpperCamelCase			:    bytes ):





							'''simple docstring'''


							if len(UpperCamelCase ) % 512 != 0:
														raise ValueError('''Input must have length that\'s a multiple of 512''' )

							for pos in range(0					,		len(UpperCamelCase )					,		512 ):
														_a      	=							bit_string[pos : pos + 512]
														_a      	=							[]
														for i in range(0					,		512					,		32 ):
																					block_words.append(int(to_little_endian(block[i : i + 32] )					,		2 ) )
														yield block_words
def 						snake_case_       (UpperCamelCase			:    int ):





							'''simple docstring'''


							if i < 0:
														raise ValueError('''Input must be non-negative''' )

							_a      	=							format(UpperCamelCase					,		'''032b''' )
							_a      	=							''''''
							for c in i_str:
														new_str += "1" if c == "0" else "0"
							return int(UpperCamelCase					,		2 )
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							return (a + b) % 2**32
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							if i < 0:
														raise ValueError('''Input must be non-negative''' )
							if shift < 0:
														raise ValueError('''Shift must be non-negative''' )
							return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def 						snake_case_       (UpperCamelCase			:    bytes ):





							'''simple docstring'''


							_a      	=							preprocess(UpperCamelCase )

							_a      	=							[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

							# Starting states
							_a      	=							0X67452301
							_a      	=							0Xefcdab89
							_a      	=							0X98badcfe
							_a      	=							0X10325476

							_a      	=							[
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							]

							# Process bit string in chunks, each with 16 32-char words
							for block_words in get_block_words(UpperCamelCase ):
														_a      	=							aa
														_a      	=							ba
														_a      	=							ca
														_a      	=							da

														# Hash current chunk
														for i in range(64 ):
																					if i <= 15:
																												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																												_a      	=							d ^ (b & (c ^ d))
																												_a      	=							i
																					elif i <= 31:
																												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																												_a      	=							c ^ (d & (b ^ c))
																												_a      	=							(5 * i + 1) % 16
																					elif i <= 47:
																												_a      	=							b ^ c ^ d
																												_a      	=							(3 * i + 5) % 16
																					else:
																												_a      	=							c ^ (b | not_aa(UpperCamelCase ))
																												_a      	=							(7 * i) % 16
																					_a      	=							(f + a + added_consts[i] + block_words[g]) % 2**32
																					_a      	=							d
																					_a      	=							c
																					_a      	=							b
																					_a      	=							sum_aa(UpperCamelCase					,		left_rotate_aa(UpperCamelCase					,		shift_amounts[i] ) )

														# Add hashed chunk to running total
														_a      	=							sum_aa(UpperCamelCase					,		UpperCamelCase )
														_a      	=							sum_aa(UpperCamelCase					,		UpperCamelCase )
														_a      	=							sum_aa(UpperCamelCase					,		UpperCamelCase )
														_a      	=							sum_aa(UpperCamelCase					,		UpperCamelCase )

							_a      	=							reformat_hex(UpperCamelCase ) + reformat_hex(UpperCamelCase ) + reformat_hex(UpperCamelCase ) + reformat_hex(UpperCamelCase )
							return digest


if __name__ == "__main__":
				import doctest

				doctest.testmod()

