






import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=()     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__="no"     ,   UpperCAmelCase__="29500"  )	->    List[Any]:
		UpperCamelCase_: Any   				=    False
		UpperCamelCase_: List[str]   				=    False
		if any(key.startswith('KAGGLE'  ) for key in os.environ.keys()  ):
				UpperCamelCase_: List[Any]   				=    True
		elif "IPython" in sys.modules:
				UpperCamelCase_: List[Any]   				=    'google.colab' in str(sys.modules['IPython'].get_ipython()  )

		try:
				UpperCamelCase_: Optional[int]   				=    PrecisionType(mixed_precision.lower()  )
		except ValueError:
				raise ValueError(
				    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''  )

		if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'     ,   UpperCAmelCase__  ) is not None):
				# TPU launch
				import torch_xla.distributed.xla_multiprocessing as xmp

				if len(AcceleratorState._shared_state  ) > 0:
						raise ValueError(
						    'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
						    'your training function. Restart your notebook and make sure no cells initializes an '
						    '`Accelerator`.'  )
				if num_processes is None:
						UpperCamelCase_: List[str]   				=    8

				UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   distributed_type='TPU'  )
				print(F'''Launching a training on {num_processes} TPU cores.'''  )
				xmp.spawn(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )
		elif in_colab:
				# No need for a distributed launch otherwise as it's either CPU or one GPU.
				if torch.cuda.is_available():
						print('Launching training on one GPU.'  )
				else:
						print('Launching training on one CPU.'  )
				function(*UpperCAmelCase__  )
		else:
				if num_processes is None:
						raise ValueError(
						    'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'  )

				if num_processes > 1:
						# Multi-GPU launch
						from torch.multiprocessing import start_processes
						from torch.multiprocessing.spawn import ProcessRaisedException

						if len(AcceleratorState._shared_state  ) > 0:
								raise ValueError(
								    'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
								    'inside your training function. Restart your notebook and make sure no cells initializes an '
								    '`Accelerator`.'  )

						if torch.cuda.is_initialized():
								raise ValueError(
								    'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
								    'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
								    'function.'  )

						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=UpperCAmelCase__     ,   master_addr='127.0.01'     ,   master_port=UpperCAmelCase__     ,   mixed_precision=UpperCAmelCase__  ):
								UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   distributed_type='MULTI_GPU'  )
								print(F'''Launching training on {num_processes} GPUs.'''  )
								try:
										start_processes(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )
								except ProcessRaisedException as e:
										if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
												raise RuntimeError(
												    'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
												    'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
												    'Please review your imports and test them when running the `notebook_launcher()` to identify '
												    'which one is problematic.'  ) from e

				else:
						# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
						if is_mps_available():
								UpperCamelCase_: Tuple   				=    '1'
								print('Launching training on MPS.'  )
						elif torch.cuda.is_available():
								print('Launching training on one GPU.'  )
						else:
								print('Launching training on CPU.'  )
						function(*UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=()     ,   UpperCAmelCase__=2  )	->    Optional[int]:
		from torch.multiprocessing import start_processes

		with tempfile.NamedTemporaryFile() as tmp_file:
				# torch.distributed will expect a few environment variable to be here. We set the ones common to each
				# process here (the other ones will be set be the launcher).
				with patch_environment(
				    world_size=UpperCAmelCase__     ,   master_addr='127.0.01'     ,   master_port='29500'     ,   accelerate_mixed_precision='no'     ,   accelerate_debug_rdv_file=tmp_file.name     ,   accelerate_use_cpu='yes'     ,   ):
						UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   debug=UpperCAmelCase__  )
						start_processes(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

A_		:		Optional[Any]		     =			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									='''convbert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=2  ,      _lowerCamelCase=9  ,      _lowerCamelCase=1  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: Tuple   				=    hidden_size
         UpperCamelCase_: Optional[int]   				=    num_hidden_layers
         UpperCamelCase_: Optional[int]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: Any   				=    hidden_dropout_prob
         UpperCamelCase_: Any   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: List[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    layer_norm_eps
         UpperCamelCase_: List[str]   				=    embedding_size
         UpperCamelCase_: int   				=    head_ratio
         UpperCamelCase_: Dict   				=    conv_kernel_size
         UpperCamelCase_: List[Any]   				=    num_groups
         UpperCamelCase_: Dict   				=    classifier_dropout







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @property
       def       _a   ( self							):
         if self.task == "multiple-choice":
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]							)