


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case_						:			Optional[Any]													=							'''scheduler_config.json'''






class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						1
						UpperCAmelCase             =						2
						UpperCAmelCase             =						3
						UpperCAmelCase             =						4
						UpperCAmelCase             =						5
						UpperCAmelCase             =						6
						UpperCAmelCase             =						7
						UpperCAmelCase             =						8
						UpperCAmelCase             =						9
						UpperCAmelCase             =						10
						UpperCAmelCase             =						11
						UpperCAmelCase             =						12
						UpperCAmelCase             =						13
						UpperCAmelCase             =						14






@dataclass
class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						42






class 						A__							:
						UpperCAmelCase             =						SCHEDULER_CONFIG_NAME
						UpperCAmelCase             =						[]
						UpperCAmelCase             =						True




						@classmethod
						def 							__UpperCamelCase				(       cls					:      List[str]				,    _a					:      Dict[str, Any] = None				,    _a					:      Optional[str] = None				,    _a					:      Optional[Any]=False				,    **_a					:      Dict				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =cls.load_config(
										    pretrained_model_name_or_path=_a				,    subfolder=_a				,    return_unused_kwargs=_a				,    return_commit_hash=_a				,    **_a				,    )
										return cls.from_config(_a				,    return_unused_kwargs=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Union[str, os.PathLike]				,    _a					:      bool = False				,    **_a					:      int  )							->     Dict:


										"""simple docstring"""





										self.save_config(save_directory=_a				,    push_to_hub=_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[Any]:


										"""simple docstring"""





										return self._get_compatibles()




						@classmethod
						def 							__UpperCamelCase				(       cls					:      List[Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =list(set([cls.__name__] + cls._compatibles  )  )
										_SCREAMING_SNAKE_CASE			  =importlib.import_module(__name__.split('''.'''  )[0]  )
										_SCREAMING_SNAKE_CASE			  =[
										    getattr(_a				,    _a  ) for c in compatible_classes_str if hasattr(_a				,    _a  )
										]
										return compatible_classes



from collections import OrderedDict
from typing import Mapping

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


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





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

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

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )