
"""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      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = False			)  ->      List[str]:


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


def      lowerCamelCase_     (						lowerCamelCase_ = None						,					lowerCamelCase_ = None			)  ->      str:


					'''simple docstring'''
					# Get the proper rng state
					if rng_type == RNGType.TORCH:
										__a:    List[Any]		     =       torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										__a:    Any		     =       torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										__a:    Optional[Any]		     =       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."
										__a:    Tuple		     =       torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										__a:    List[Any]		     =       torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										__a:    Optional[Any]		     =       generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					__a:    Any		     =       AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										__a:    List[str]		     =       rng_state.to(xm.xla_device()			)
										xm.collective_broadcast([rng_state]			)
										xm.mark_step()
										__a:    str		     =       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
					):
										__a:    List[Any]		     =       rng_state.to(state.device			)
										torch.distributed.broadcast(lowerCamelCase_						,					0			)
										__a:    List[str]		     =       rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(lowerCamelCase_						,					0			)

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




def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = None			)  ->      Union[str, Any]:


					'''simple docstring'''
					for rng_type in rng_types:
										synchronize_rng_state(RNGType(lowerCamelCase_			)						,					generator=lowerCamelCase_			)


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			lowercase		:    Union[str, Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
