




'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_SCREAMING_SNAKE_CASE							:     Dict   	=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:     List[Any]   	=				{
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Dict		 =					"vit-hybrid"




			def __init__(      self       ,							a__=None       ,							a__=768       ,							a__=12       ,							a__=12       ,							a__=3_072       ,							a__="gelu"       ,							a__=0.0       ,							a__=0.0       ,							a__=0.0_2       ,							a__=1e-12       ,							a__=224       ,							a__=1       ,							a__=3       ,							a__=[1, 1_024, 24, 24]       ,							a__=True       ,							**a__       ,							)       ->       List[Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										if backbone_config is None:
																	logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."							)
																	snake_case_			      =   {
																	    "global_padding": "same",
																	    "layer_type": "bottleneck",
																	    "depths": [3, 4, 9],
																	    "out_features": ["stage3"],
																	    "embedding_dynamic_padding": True,
																	}

										if isinstance(a__       ,							a__							):
																	if "model_type" in backbone_config:
																								snake_case_			      =   CONFIG_MAPPING[backbone_config["model_type"]]
																	else:
																								logger.info(
																								    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."							)
																								snake_case_			      =   BitConfig
																	snake_case_			      =   backbone_config_class(**a__							)

										snake_case_			      =   backbone_featmap_shape
										snake_case_			      =   backbone_config
										snake_case_			      =   hidden_size
										snake_case_			      =   num_hidden_layers
										snake_case_			      =   num_attention_heads
										snake_case_			      =   intermediate_size
										snake_case_			      =   hidden_act
										snake_case_			      =   hidden_dropout_prob
										snake_case_			      =   attention_probs_dropout_prob
										snake_case_			      =   initializer_range
										snake_case_			      =   layer_norm_eps
										snake_case_			      =   image_size
										snake_case_			      =   patch_size
										snake_case_			      =   num_channels
										snake_case_			      =   qkv_bias




			def      lowerCAmelCase__       (      self							)       ->       Dict[str, any]:

										'''simple docstring'''




										snake_case_			      =   copy.deepcopy(self.__dict__							)
										snake_case_			      =   self.backbone_config.to_dict()
										snake_case_			      =   self.__class__.model_type
										return output










'''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							: int							,       snake_case							: bool = False  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: Optional[RNGType] = None							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							if rng_type == RNGType.TORCH:
														snake_case_			      =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														snake_case_			      =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														snake_case_			      =   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."
														snake_case_			      =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														snake_case_			      =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														snake_case_			      =   generator.get_state()

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

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




def   UpperCamelCase_(			snake_case							: List[Union[str, RNGType]]							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






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




