



"""simple docstring"""






import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_lowercase : str      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'linear'
					_a   			=							'cosine'
					_a   			=							'cosine_with_restarts'
					_a   			=							'polynomial'
					_a   			=							'constant'
					_a   			=							'constant_with_warmup'
					_a   			=							'piecewise_constant'






def      snake_case__   (		__lowerCamelCase				:    Optimizer							,		__lowerCamelCase				:    int = -1	):
			"""simple docstring"""
			return LambdaLR(__lowerCamelCase							,		lambda __lowerCamelCase	: 1							,		last_epoch=__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optimizer							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int = -1	):
			"""simple docstring"""

			def lr_lambda(__lowerCamelCase				:    int	):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase	) / float(max(1.0							,		__lowerCamelCase	)	)
						return 1.0

			return LambdaLR(__lowerCamelCase							,		__lowerCamelCase							,		last_epoch=__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optimizer							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    int = -1	):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			={}
			lowerCamelCase__		:		Tuple			=step_rules.split(''','''	)
			for rule_str in rule_list[:-1]:
						lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=rule_str.split(''':'''	)
						lowerCamelCase__		:		Any			=int(__lowerCamelCase	)
						lowerCamelCase__		:		int			=float(__lowerCamelCase	)
						lowerCamelCase__		:		Tuple			=value
			lowerCamelCase__		:		Union[str, Any]			=float(rule_list[-1]	)

			def create_rules_function(__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    List[str]	):
						def rule_func(__lowerCamelCase				:    int	) -> float:
									lowerCamelCase__		:		Optional[int]			=sorted(rules_dict.keys()	)
									for i, sorted_step in enumerate(__lowerCamelCase	):
												if steps < sorted_step:
															return rules_dict[sorted_steps[i]]
									return last_lr_multiple

						return rule_func

			lowerCamelCase__		:		Any			=create_rules_function(__lowerCamelCase							,		__lowerCamelCase	)

			return LambdaLR(__lowerCamelCase							,		__lowerCamelCase							,		last_epoch=__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Union[str, Any]=-1	):
			"""simple docstring"""

			def lr_lambda(__lowerCamelCase				:    int	):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase	) / float(max(1							,		__lowerCamelCase	)	)
						return max(
						    0.0							,		float(num_training_steps - current_step	) / float(max(1							,		num_training_steps - num_warmup_steps	)	)	)

			return LambdaLR(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optimizer							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    float = 0.5							,		__lowerCamelCase				:    int = -1	):
			"""simple docstring"""

			def lr_lambda(__lowerCamelCase				:    Dict	):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase	) / float(max(1							,		__lowerCamelCase	)	)
						lowerCamelCase__		:		Tuple			=float(current_step - num_warmup_steps	) / float(max(1							,		num_training_steps - num_warmup_steps	)	)
						return max(0.0							,		0.5 * (1.0 + math.cos(math.pi * float(__lowerCamelCase	) * 2.0 * progress	))	)

			return LambdaLR(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optimizer							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int = 1							,		__lowerCamelCase				:    int = -1	):
			"""simple docstring"""

			def lr_lambda(__lowerCamelCase				:    Union[str, Any]	):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase	) / float(max(1							,		__lowerCamelCase	)	)
						lowerCamelCase__		:		Dict			=float(current_step - num_warmup_steps	) / float(max(1							,		num_training_steps - num_warmup_steps	)	)
						if progress >= 1.0:
									return 0.0
						return max(0.0							,		0.5 * (1.0 + math.cos(math.pi * ((float(__lowerCamelCase	) * progress) % 1.0)	))	)

			return LambdaLR(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    List[str]=1e-7							,		__lowerCamelCase				:    List[Any]=1.0							,		__lowerCamelCase				:    Any=-1	):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=optimizer.defaults['''lr''']
			if not (lr_init > lr_end):
						raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''	)

			def lr_lambda(__lowerCamelCase				:    int	):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase	) / float(max(1							,		__lowerCamelCase	)	)
						elif current_step > num_training_steps:
									return lr_end / lr_init  # as LambdaLR multiplies by lr_init
						else:
									lowerCamelCase__		:		Optional[int]			=lr_init - lr_end
									lowerCamelCase__		:		Union[str, Any]			=num_training_steps - num_warmup_steps
									lowerCamelCase__		:		Optional[Any]			=1 - (current_step - num_warmup_steps) / decay_steps
									lowerCamelCase__		:		str			=lr_range * pct_remaining**power + lr_end
									return decay / lr_init  # as LambdaLR multiplies by lr_init

			return LambdaLR(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)


_lowercase : Any      						=						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def      snake_case__   (		__lowerCamelCase				:    Union[str, SchedulerType]							,		__lowerCamelCase				:    Optimizer							,		__lowerCamelCase				:    Optional[str] = None							,		__lowerCamelCase				:    Optional[int] = None							,		__lowerCamelCase				:    Optional[int] = None							,		__lowerCamelCase				:    int = 1							,		__lowerCamelCase				:    float = 1.0							,		__lowerCamelCase				:    int = -1							,		):
			"""simple docstring"""
			lowerCamelCase__		:		int			=SchedulerType(__lowerCamelCase	)
			lowerCamelCase__		:		Dict			=TYPE_TO_SCHEDULER_FUNCTION[name]
			if name == SchedulerType.CONSTANT:
						return schedule_func(__lowerCamelCase							,		last_epoch=__lowerCamelCase	)

			if name == SchedulerType.PIECEWISE_CONSTANT:
						return schedule_func(__lowerCamelCase							,		step_rules=__lowerCamelCase							,		last_epoch=__lowerCamelCase	)

			# All other schedulers require `num_warmup_steps`
			if num_warmup_steps is None:
						raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.'''	)

			if name == SchedulerType.CONSTANT_WITH_WARMUP:
						return schedule_func(__lowerCamelCase							,		num_warmup_steps=__lowerCamelCase							,		last_epoch=__lowerCamelCase	)

			# All other schedulers require `num_training_steps`
			if num_training_steps is None:
						raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.'''	)

			if name == SchedulerType.COSINE_WITH_RESTARTS:
						return schedule_func(
						    __lowerCamelCase							,		num_warmup_steps=__lowerCamelCase							,		num_training_steps=__lowerCamelCase							,		num_cycles=__lowerCamelCase							,		last_epoch=__lowerCamelCase							,		)

			if name == SchedulerType.POLYNOMIAL:
						return schedule_func(
						    __lowerCamelCase							,		num_warmup_steps=__lowerCamelCase							,		num_training_steps=__lowerCamelCase							,		power=__lowerCamelCase							,		last_epoch=__lowerCamelCase							,		)

			return schedule_func(
			    __lowerCamelCase							,		num_warmup_steps=__lowerCamelCase							,		num_training_steps=__lowerCamelCase							,		last_epoch=__lowerCamelCase	)









"""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


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		int			=hidden_act
								lowerCamelCase__		:		Tuple			=hidden_dropout_prob
								lowerCamelCase__		:		Union[str, Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		int			=type_vocab_size
								lowerCamelCase__		:		Tuple			=initializer_range
								lowerCamelCase__		:		Tuple			=relative_attention
								lowerCamelCase__		:		Optional[Any]			=max_relative_positions
								lowerCamelCase__		:		List[Any]			=pad_token_id
								lowerCamelCase__		:		int			=position_biased_input

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={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 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       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,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=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




