


'''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


lowerCamelCase							:       Optional[Any]  						=     logging.get_logger(__name__)

lowerCamelCase							:       str  						=     {
    '''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   __lowerCAmelCase						(UpperCamelCase__					):





						'''simple docstring'''






						lowerCAmelCase__ :					List[Any]   =							"""vit-hybrid"""



						def __init__(self   :   List[Any]       ,						UpperCamelCase   :   List[str]=None       ,						UpperCamelCase   :   Optional[Any]=768       ,						UpperCamelCase   :   str=12       ,						UpperCamelCase   :   Dict=12       ,						UpperCamelCase   :   Any=3072       ,						UpperCamelCase   :   List[Any]="gelu"       ,						UpperCamelCase   :   Optional[Any]=0.0       ,						UpperCamelCase   :   Optional[Any]=0.0       ,						UpperCamelCase   :   List[str]=0.02       ,						UpperCamelCase   :   Tuple=1E-12       ,						UpperCamelCase   :   Optional[Any]=224       ,						UpperCamelCase   :   int=1       ,						UpperCamelCase   :   Optional[int]=3       ,						UpperCamelCase   :   str=[1, 1024, 24, 24]       ,						UpperCamelCase   :   Optional[int]=True       ,						**UpperCamelCase   :   Tuple       ,						):

													'''simple docstring'''







													super().__init__(**__lowerCamelCase			)

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

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

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






						def 				UpperCamelCase__		(self   :   int			):

													'''simple docstring'''







													lowercase__				      =			copy.deepcopy(self.__dict__			)
													lowercase__				      =			self.backbone_config.to_dict()
													lowercase__				      =			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 _SCREAMING_SNAKE_CASE				(A  ,			A = False	)	->	Tuple:






       """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 _SCREAMING_SNAKE_CASE				(A = None  ,			A = None	)	->	Tuple:






       """simple docstring"""







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

       # Broadcast the rng state from device 0 to other devices
       lowercase__				      =			AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              lowercase__				      =			rng_state.to(xm.xla_device()	)
              xm.collective_broadcast([rng_state]	)
              xm.mark_step()
              lowercase__				      =			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
       ):
              lowercase__				      =			rng_state.to(state.device	)
              torch.distributed.broadcast(A  ,			0	)
              lowercase__				      =			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 _SCREAMING_SNAKE_CASE				(A  ,			A = None	)	->	int:






       """simple docstring"""







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




