from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase__           =				logging.get_logger(__name__)

lowerCamelCase__           =				{
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}




class 					UpperCamelCase	(     snake_case__      ):
					__UpperCamelCase						=   """deberta"""




					def __init__( self :					Any      ,_lowerCAmelCase :					Dict=50_265      ,_lowerCAmelCase :					int=768      ,_lowerCAmelCase :					List[Any]=12      ,_lowerCAmelCase :					str=12      ,_lowerCAmelCase :					int=3_072      ,_lowerCAmelCase :					Any="gelu"      ,_lowerCAmelCase :					Optional[Any]=0.1      ,_lowerCAmelCase :					Union[str, Any]=0.1      ,_lowerCAmelCase :					List[Any]=512      ,_lowerCAmelCase :					int=0      ,_lowerCAmelCase :					Optional[int]=0.0_2      ,_lowerCAmelCase :					Dict=1E-7      ,_lowerCAmelCase :					Union[str, Any]=False      ,_lowerCAmelCase :					str=-1      ,_lowerCAmelCase :					Tuple=0      ,_lowerCAmelCase :					Union[str, Any]=True      ,_lowerCAmelCase :					Optional[Any]=None      ,_lowerCAmelCase :					Optional[int]=0      ,_lowerCAmelCase :					Any="gelu"      ,**_lowerCAmelCase :					int      ,):


							"""simple docstring"""




							super().__init__(**_lowerCAmelCase						)

							__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		       =     relative_attention
							__snake_case		       =     max_relative_positions
							__snake_case		       =     pad_token_id
							__snake_case		       =     position_biased_input

							# Backwards compatibility
							if type(_lowerCAmelCase						) == str:
									__snake_case		       =     [x.strip() for x in pos_att_type.lower().split("|"						)]

							__snake_case		       =     pos_att_type
							__snake_case		       =     vocab_size
							__snake_case		       =     layer_norm_eps

							__snake_case		       =     kwargs.get("pooler_hidden_size"      ,_lowerCAmelCase						)
							__snake_case		       =     pooler_dropout
							__snake_case		       =     pooler_hidden_act




class 					UpperCamelCase	(     snake_case__      ):




					@property
					def 							UpperCamelCase_						( self :					Union[str, Any]						):


							"""simple docstring"""




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




					@property
					def 							UpperCamelCase_						( self :					List[str]						):


							"""simple docstring"""




							return 12







					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					bool = False      ,_lowerCAmelCase :					Optional["TensorType"] = None      ,_lowerCAmelCase :					int = 3      ,_lowerCAmelCase :					int = 40      ,_lowerCAmelCase :					int = 40      ,_lowerCAmelCase :					"PreTrainedTokenizerBase" = None      ,):


							"""simple docstring"""




							__snake_case		       =     super().generate_dummy_inputs(preprocessor=_lowerCAmelCase      ,framework=_lowerCAmelCase						)
							if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
									del dummy_inputs["token_type_ids"]
							return dummy_inputs






import os
from string import ascii_letters

lowerCamelCase__           =				ascii_letters + ' \t\n'



def 							_lowerCamelCase(				)  ->   dict[str, None]:
		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
		__snake_case		       =     {}
		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
				for word in dictionary_file.read().split("\n"	):
						__snake_case		       =     None
		return english_words


lowerCamelCase__           =				load_dictionary()



def 							_lowerCamelCase(				__snake_case	)  ->   float:
		__snake_case		       =     message.upper()
		__snake_case		       =     remove_non_letters(__snake_case	)
		__snake_case		       =     message.split()
		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
		return float(__snake_case	) / len(__snake_case	)



def 							_lowerCamelCase(				__snake_case	)  ->   str:
		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)



def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
		__snake_case		       =     message_letters_percentage >= letter_percentage
		return words_match and letters_match


if __name__ == "__main__":
		import doctest

		doctest.testmod()





