




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       int						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Tuple							=   "deberta-v2"




			def __init__( self    ,							a__=128_100    ,							a__=1_536    ,							a__=24    ,							a__=24    ,							a__=6_144    ,							a__="gelu"    ,							a__=0.1    ,							a__=0.1    ,							a__=512    ,							a__=0    ,							a__=0.0_2    ,							a__=1e-7    ,							a__=False    ,							a__=-1    ,							a__=0    ,							a__=True    ,							a__=None    ,							a__=0    ,							a__="gelu"    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										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(a__							) == 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"    ,							a__							)
										snake_case_      =				pooler_dropout
										snake_case_      =				pooler_hidden_act




class 			_snake_case		(     lowercase_							):




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

										'''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        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										return 12




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							a__ = 3    ,							a__ = 40    ,							a__ = 40    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




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










'''simple docstring'''





import logging
import os

from .state import PartialState




class 			_snake_case		(     logging.LoggerAdapter							):




			@staticmethod
			def        lowerCAmelCase__    ( a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				PartialState()
										return not main_process_only or (main_process_only and state.is_main_process)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							*a__    ,							**a__							)    ->    List[Any]:

										'''simple docstring'''




										if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."							)
										snake_case_      =				kwargs.pop("main_process_only"    ,							a__							)
										snake_case_      =				kwargs.pop("in_order"    ,							a__							)

										if self.isEnabledFor(a__							):
																	if self._should_log(a__							):
																								snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																								self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)

																	elif in_order:
																								snake_case_      =				PartialState()
																								for i in range(state.num_processes							):
																															if i == state.process_index:
																																						snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																																						self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)
																															state.wait_for_everyone()


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str = None					):

							'''simple docstring'''






							if log_level is None:
														snake_case_      =				os.environ.get("ACCELERATE_LOG_LEVEL"							,    snake_case					)
							snake_case_      =				logging.getLogger(snake_case					)
							if log_level is not None:
														logger.setLevel(log_level.upper()					)
														logger.root.setLevel(log_level.upper()					)
							return MultiProcessAdapter(snake_case							,    {}					)




