


"""simple docstring"""




import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm





def 			a_   (  lowercase__   :int, lowercase__   :bool = False    ):
			if device_specific:
						seed += AcceleratorState().process_index
			random.seed(lowercase__    )
			np.random.seed(lowercase__    )
			torch.manual_seed(lowercase__    )
			if is_xpu_available():
						torch.xpu.manual_seed_all(lowercase__    )
			elif is_npu_available():
						torch.npu.manual_seed_all(lowercase__    )
			else:
						torch.cuda.manual_seed_all(lowercase__    )
			# ^^ safe to call this function even if cuda is not available
			if is_tpu_available():
						xm.set_rng_state(lowercase__    )





def 			a_   (  lowercase__   :Optional[RNGType] = None, lowercase__   :Optional[torch.Generator] = None    ):
			# Get the proper rng state
			if rng_type == RNGType.TORCH:
						__lowerCamelCase        =			torch.get_rng_state()
			elif rng_type == RNGType.CUDA:
						__lowerCamelCase        =			torch.cuda.get_rng_state()
			elif rng_type == RNGType.XLA:
						assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
						__lowerCamelCase        =			torch.tensor(xm.get_rng_state()    )
			elif rng_type == RNGType.NPU:
						assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
						__lowerCamelCase        =			torch.npu.get_rng_state()
			elif rng_type == RNGType.XPU:
						assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
						__lowerCamelCase        =			torch.xpu.get_rng_state()
			elif rng_type == RNGType.GENERATOR:
						assert generator is not None, "Need a generator to synchronize its seed."
						__lowerCamelCase        =			generator.get_state()

			# Broadcast the rng state from device 0 to other devices
			__lowerCamelCase        =			AcceleratorState()
			if state.distributed_type == DistributedType.TPU:
						__lowerCamelCase        =			rng_state.to(xm.xla_device()    )
						xm.collective_broadcast([rng_state]    )
						xm.mark_step()
						__lowerCamelCase        =			rng_state.cpu()
			elif (
			    state.distributed_type in CUDA_DISTRIBUTED_TYPES
			    or state.distributed_type == DistributedType.MULTI_NPU
			    or state.distributed_type == DistributedType.MULTI_XPU
			):
						__lowerCamelCase        =			rng_state.to(state.device    )
						torch.distributed.broadcast(lowercase__, 0    )
						__lowerCamelCase        =			rng_state.cpu()
			elif state.distributed_type == DistributedType.MULTI_CPU:
						torch.distributed.broadcast(lowercase__, 0    )

			# Set the broadcast rng state
			if rng_type == RNGType.TORCH:
						torch.set_rng_state(lowercase__    )
			elif rng_type == RNGType.CUDA:
						torch.cuda.set_rng_state(lowercase__    )
			elif rng_type == RNGType.NPU:
						torch.npu.set_rng_state(lowercase__    )
			elif rng_type == RNGType.XPU:
						torch.xpu.set_rng_state(lowercase__    )
			elif rng_type == RNGType.XLA:
						xm.set_rng_state(rng_state.item()    )
			elif rng_type == RNGType.GENERATOR:
						generator.set_state(lowercase__    )





def 			a_   (  lowercase__   :List[Union[str, RNGType]], lowercase__   :Optional[torch.Generator] = None    ):
			for rng_type in rng_types:
						synchronize_rng_state(RNGType(lowercase__    ), generator=lowercase__    )








"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union





def 			a_   (  lowercase__   :Union[str, int, bytes]    ):
			if isinstance(lowercase__, lowercase__    ):
						# Data already in bytes, returns as it as
						return hash_data
			elif isinstance(lowercase__, lowercase__    ):
						# We keep the data as it as for it ot be later encoded to UTF-8
						# However replace `\\` with `/` for Windows compatibility
						__lowerCamelCase        =			hash_data.replace("""\\""", """/"""    )
			elif isinstance(lowercase__, lowercase__    ):
						__lowerCamelCase        =			str(lowercase__    )
			else:
						# If data is not of the required type, raise error
						raise InvalidKeyError(lowercase__    )

			return hash_data.encode("""utf-8"""    )




class  __snake_case      (lowerCamelCase     ):




							def __init__(    self:						Union[str, Any]    ,       A_:						Tuple      ):
										__lowerCamelCase        =			"""\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
										__lowerCamelCase        =			f'\nFound Key {hash_data} of type {type(A_      )}'
										__lowerCamelCase        =			"""\nKeys should be either str, int or bytes type"""
										super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'      )




class  __snake_case      (lowerCamelCase     ):




							def __init__(    self:						Any    ,       A_:						Dict    ,       A_:						List[Any]    ,       A_:						List[str]=""      ):
										__lowerCamelCase        =			key
										__lowerCamelCase        =			duplicate_key_indices
										__lowerCamelCase        =			fix_msg
										__lowerCamelCase        =			"""Found multiple examples generated with the same key"""
										if len(A_      ) <= 20:
													__lowerCamelCase        =			f'\nThe examples at index {", ".join(A_      )} have the key {key}'
										else:
													__lowerCamelCase        =			f'\nThe examples at index {", ".join(duplicate_key_indices[:20]      )}... ({len(A_      ) - 20} more) have the key {key}'
										__lowerCamelCase        =			"""\n""" + fix_msg if fix_msg else """"""
										super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'      )




class  __snake_case      :




							def __init__(    self:						Dict    ,       A_:						str      ):
										__lowerCamelCase        =			hashlib.mda(_as_bytes(A_      )      )




							def      __a      (    self:						int    ,       A_:						Union[str, int, bytes]      ):
										__lowerCamelCase        =			self._split_mda.copy()
										__lowerCamelCase        =			_as_bytes(A_      )
										mda.update(A_      )
										# Convert to integer with hexadecimal conversion
										return int(mda.hexdigest()    ,       16      )




