
'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      :       List[Any] 		=						logging.get_logger(__name__)

UpperCAmelCase__      :       List[str] 		=						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}






class       A   (		lowerCAmelCase__			):
	snake_case__					:Optional[Any]         =   "qdqbert"





	def __init__(    self	:				Union[str, Any]					,							__magic_name__	:				List[str]=30522					,							__magic_name__	:				List[Any]=768					,							__magic_name__	:				Optional[Any]=12					,							__magic_name__	:				Union[str, Any]=12					,							__magic_name__	:				List[str]=3072					,							__magic_name__	:				Union[str, Any]="gelu"					,							__magic_name__	:				int=0.1					,							__magic_name__	:				List[Any]=0.1					,							__magic_name__	:				List[str]=512					,							__magic_name__	:				Any=2					,							__magic_name__	:				List[str]=0.02					,							__magic_name__	:				Dict=1E-12					,							__magic_name__	:				Union[str, Any]=True					,							__magic_name__	:				Optional[Any]=1					,							__magic_name__	:				List[str]=0					,							__magic_name__	:				int=2					,							**__magic_name__	:				Dict					,							):


				"""simple docstring"""

				super().__init__(pad_token_id=a__					,							bos_token_id=a__					,							eos_token_id=a__					,							**a__							)

				lowerCAmelCase__									=							vocab_size
				lowerCAmelCase__									=							max_position_embeddings
				lowerCAmelCase__									=							hidden_size
				lowerCAmelCase__									=							num_hidden_layers
				lowerCAmelCase__									=							num_attention_heads
				lowerCAmelCase__									=							intermediate_size
				lowerCAmelCase__									=							hidden_act
				lowerCAmelCase__									=							hidden_dropout_prob
				lowerCAmelCase__									=							attention_probs_dropout_prob
				lowerCAmelCase__									=							initializer_range
				lowerCAmelCase__									=							type_vocab_size
				lowerCAmelCase__									=							layer_norm_eps
				lowerCAmelCase__									=							use_cache












"""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_   (       snake_case   :					int						,  snake_case   :					bool = False		)->		Any:
       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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[RNGType] = None						,  snake_case   :					Optional[torch.Generator] = None		)->		str:
       # 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(snake_case						,  0		)
              _lowerCamelCase    	=					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 SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Union[str, RNGType]]						,  snake_case   :					Optional[torch.Generator] = None		)->		int:
       for rng_type in rng_types:
              synchronize_rng_state(RNGType(snake_case		)						,  generator=snake_case		)






