


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


__snake_case            =   logging.get_logger(__name__)




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """linear"""
			lowercase						= """cosine"""
			lowercase						= """cosine_with_restarts"""
			lowercase						= """polynomial"""
			lowercase						= """constant"""
			lowercase						= """constant_with_warmup"""
			lowercase						= """piecewise_constant"""






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optimizer       ,  SCREAMING_SNAKE_CASE_   :		int = -1	):





			"""simple docstring"""




			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  lambda SCREAMING_SNAKE_CASE_	: 1       ,  last_epoch=SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optimizer       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int = -1	):





			"""simple docstring"""




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

			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  last_epoch=SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optimizer       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int = -1	):





			"""simple docstring"""




			UpperCamelCase         =					{}
			UpperCamelCase         =					step_rules.split(""","""	)
			for rule_str in rule_list[:-1]:
						UpperCamelCase  ,  UpperCamelCase         =					rule_str.split(""":"""	)
						UpperCamelCase         =					int(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					float(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					value
			UpperCamelCase         =					float(rule_list[-1]	)

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

						return rule_func

			UpperCamelCase         =					create_rules_function(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  last_epoch=SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=-1	):





			"""simple docstring"""




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

			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optimizer       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		float = 0.5       ,  SCREAMING_SNAKE_CASE_   :		int = -1	):





			"""simple docstring"""




			def lr_lambda(SCREAMING_SNAKE_CASE_   :		Optional[Any]	):
						if current_step < num_warmup_steps:
									return float(SCREAMING_SNAKE_CASE_	) / float(max(1       ,  SCREAMING_SNAKE_CASE_	)	)
						UpperCamelCase         =					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(SCREAMING_SNAKE_CASE_	) * 2.0 * progress	))	)

			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optimizer       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int = 1       ,  SCREAMING_SNAKE_CASE_   :		int = -1	):





			"""simple docstring"""




			def lr_lambda(SCREAMING_SNAKE_CASE_   :		Dict	):
						if current_step < num_warmup_steps:
									return float(SCREAMING_SNAKE_CASE_	) / float(max(1       ,  SCREAMING_SNAKE_CASE_	)	)
						UpperCamelCase         =					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(SCREAMING_SNAKE_CASE_	) * progress) % 1.0)	))	)

			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Any=1e-7       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]=1.0       ,  SCREAMING_SNAKE_CASE_   :		Tuple=-1	):





			"""simple docstring"""




			UpperCamelCase         =					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(SCREAMING_SNAKE_CASE_   :		int	):
						if current_step < num_warmup_steps:
									return float(SCREAMING_SNAKE_CASE_	) / float(max(1       ,  SCREAMING_SNAKE_CASE_	)	)
						elif current_step > num_training_steps:
									return lr_end / lr_init  # as LambdaLR multiplies by lr_init
						else:
									UpperCamelCase         =					lr_init - lr_end
									UpperCamelCase         =					num_training_steps - num_warmup_steps
									UpperCamelCase         =					1 - (current_step - num_warmup_steps) / decay_steps
									UpperCamelCase         =					lr_range * pct_remaining**power + lr_end
									return decay / lr_init  # as LambdaLR multiplies by lr_init

			return LambdaLR(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)


__snake_case            =   {
    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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, SchedulerType]       ,  SCREAMING_SNAKE_CASE_   :		Optimizer       ,  SCREAMING_SNAKE_CASE_   :		Optional[str] = None       ,  SCREAMING_SNAKE_CASE_   :		Optional[int] = None       ,  SCREAMING_SNAKE_CASE_   :		Optional[int] = None       ,  SCREAMING_SNAKE_CASE_   :		int = 1       ,  SCREAMING_SNAKE_CASE_   :		float = 1.0       ,  SCREAMING_SNAKE_CASE_   :		int = -1       ,  ):





			"""simple docstring"""




			UpperCamelCase         =					SchedulerType(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					TYPE_TO_SCHEDULER_FUNCTION[name]
			if name == SchedulerType.CONSTANT:
						return schedule_func(SCREAMING_SNAKE_CASE_       ,  last_epoch=SCREAMING_SNAKE_CASE_	)

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

			# 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(SCREAMING_SNAKE_CASE_       ,  num_warmup_steps=SCREAMING_SNAKE_CASE_       ,  last_epoch=SCREAMING_SNAKE_CASE_	)

			# 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(
						    SCREAMING_SNAKE_CASE_       ,  num_warmup_steps=SCREAMING_SNAKE_CASE_       ,  num_training_steps=SCREAMING_SNAKE_CASE_       ,  num_cycles=SCREAMING_SNAKE_CASE_       ,  last_epoch=SCREAMING_SNAKE_CASE_       ,  )

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

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








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




