


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_a   :   int												=   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 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	"convbert"
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=3_0522       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=768       ,   SCREAMING_SNAKE_CASE_       :      Any=12       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=12       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=3072       ,   SCREAMING_SNAKE_CASE_       :      Dict="gelu"       ,   SCREAMING_SNAKE_CASE_       :      int=0.1       ,   SCREAMING_SNAKE_CASE_       :      str=0.1       ,   SCREAMING_SNAKE_CASE_       :      str=512       ,   SCREAMING_SNAKE_CASE_       :      List[str]=2       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      List[str]=1e-12       ,   SCREAMING_SNAKE_CASE_       :      Any=1       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=0       ,   SCREAMING_SNAKE_CASE_       :      str=2       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=768       ,   SCREAMING_SNAKE_CASE_       :      Tuple=2       ,   SCREAMING_SNAKE_CASE_       :      Any=9       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=1       ,   SCREAMING_SNAKE_CASE_       :      Tuple=None       ,   **SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   ) ->							int:
        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_       ,   bos_token_id=SCREAMING_SNAKE_CASE_       ,   eos_token_id=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )

        __snake_case           =						vocab_size
        __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           =						max_position_embeddings
        __snake_case           =						type_vocab_size
        __snake_case           =						initializer_range
        __snake_case           =						layer_norm_eps
        __snake_case           =						embedding_size
        __snake_case           =						head_ratio
        __snake_case           =						conv_kernel_size
        __snake_case           =						num_groups
        __snake_case           =						classifier_dropout




class 			_lowercase (	__lowercase							):
   @property
   def a  (						self       :      List[Any]      ) ->							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),
            ]      )





'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     return EnvironmentCommand()







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @staticmethod
   def 	__UpperCAmelCase   (      UpperCamelCase__      :			ArgumentParser     )  ->			List[str]:
        _A    		=  parser.add_parser('env'     )
        download_parser.set_defaults(func=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  huggingface_hub.__version__

        _A    		=  'not installed'
        _A    		=  'NA'
        if is_torch_available():
             import torch

             _A    		=  torch.__version__
             _A    		=  torch.cuda.is_available()

        _A    		=  'not installed'
        if is_transformers_available():
             import transformers

             _A    		=  transformers.__version__

        _A    		=  'not installed'
        if is_accelerate_available():
             import accelerate

             _A    		=  accelerate.__version__

        _A    		=  'not installed'
        if is_xformers_available():
             import xformers

             _A    		=  xformers.__version__

        _A    		=  {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': f'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'     )
        print(self.format_dict(UpperCamelCase__     )     )

        return info

   @staticmethod
   def 	__UpperCAmelCase   (      UpperCamelCase__      :			List[Any]     )  ->			Dict:
        return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]     ) + "\n"



