'''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 					UpperCamelCase_						( snake_case_	:	Any						,		snake_case_	:	Optional[Any] = False				) ->					Any:






	'''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	str = None						,		snake_case_	:	List[str] = None				) ->					Dict:






	'''simple docstring'''

	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(A_						,		0				)
		__lowerCAmelCase    							=						rng_state.cpu()
	elif state.distributed_type == DistributedType.MULTI_CPU:
		torch.distributed.broadcast(A_						,		0				)

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







def 					UpperCamelCase_						( snake_case_	:	Optional[int]						,		snake_case_	:	List[Any] = None				) ->					Any:






	'''simple docstring'''

	for rng_type in rng_types:
		synchronize_rng_state(RNGType(A_				)						,		generator=A_				)







'''simple docstring'''

from unittest.mock import patch

import datasets
from datasets import Dataset







def 					UpperCamelCase_						( ) ->					Dict:






 '''simple docstring'''

 __lowerCAmelCase    							=						Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}				)

 with patch("""tqdm.auto.tqdm"""				) as mock_tqdm:
  datasets.disable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_not_called()

  mock_tqdm.reset_mock()

  datasets.enable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_called()






