
from collections import OrderedDict
from typing import Mapping

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


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							str     =	"bert"





     def __init__(self   :					Optional[Any]       ,					_A   :					Tuple=3_0_5_2_2       ,					_A   :					str=7_6_8       ,					_A   :					str=1_2       ,					_A   :					int=1_2       ,					_A   :					Any=3_0_7_2       ,					_A   :					int="gelu"       ,					_A   :					Dict=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Optional[Any]=5_1_2       ,					_A   :					List[Any]=2       ,					_A   :					Optional[int]=0.02       ,					_A   :					Optional[Any]=1E-12       ,					_A   :					List[str]=0       ,					_A   :					List[str]="absolute"       ,					_A   :					int=True       ,					_A   :					Optional[Any]=None       ,					**_A   :					Union[str, Any]       ,					)		->		Any:
         super().__init__(pad_token_id=_A       ,					**_A     )

         snake_case				  =      vocab_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      hidden_act
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      initializer_range
         snake_case				  =      layer_norm_eps
         snake_case				  =      position_embedding_type
         snake_case				  =      use_cache
         snake_case				  =      classifier_dropout







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					List[str]     )		->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case				  =      {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case				  =      {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
                 ("token_type_ids", dynamic_axis),
             ]     )



import os
import sys
import tempfile

import torch

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



def 		lowercase_    (     A__				,	A__=()				,	A__=None				,	A__="no"				,	A__="29500"	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      False
    snake_case				  =      False
    if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
        snake_case				  =      True
    elif "IPython" in sys.modules:
        snake_case				  =      "google.colab" in str(sys.modules["IPython"].get_ipython()	)

    try:
        snake_case				  =      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"				,	A__	) 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:
            snake_case				  =      8

        snake_case				  =      PrepareForLaunch(A__				,	distributed_type="TPU"	)
        print(F'Launching a training on {num_processes} TPU cores.'	)
        xmp.spawn(A__				,	args=A__				,	nprocs=A__				,	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(*A__	)
    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=A__				,	master_addr="127.0.01"				,	master_port=A__				,	mixed_precision=A__	):
                snake_case				  =      PrepareForLaunch(A__				,	distributed_type="MULTI_GPU"	)
                print(F'Launching training on {num_processes} GPUs.'	)
                try:
                    start_processes(A__				,	args=A__				,	nprocs=A__				,	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():
                snake_case				  =      "1"
                print("Launching training on MPS."	)
            elif torch.cuda.is_available():
                print("Launching training on one GPU."	)
            else:
                print("Launching training on CPU."	)
            function(*A__	)





def 		lowercase_    (     A__				,	A__=()				,	A__=2	)							->							Dict:
    """simple docstring"""

    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=A__				,	master_addr="127.0.01"				,	master_port="29500"				,	accelerate_mixed_precision="no"				,	accelerate_debug_rdv_file=tmp_file.name				,	accelerate_use_cpu="yes"				,	):
            snake_case				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

