

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_             = logging.get_logger(__name__)





class 				__lowerCAmelCase  (      _snake_case							):
  _a		    =						'linear'
  _a		    =						'cosine'
  _a		    =						'cosine_with_restarts'
  _a		    =						'polynomial'
  _a		    =						'constant'
  _a		    =						'constant_with_warmup'
  _a		    =						'piecewise_constant'
def   a   (    A__			:     List[Any]   ,     A__			:     List[str] = -1  )					->   int:





      """simple docstring"""



      return LambdaLR(__UpperCAmelCase   ,     lambda A__  : 1   ,     last_epoch=__UpperCAmelCase  )
def   a   (    A__			:     Tuple   ,     A__			:     Any   ,     A__			:     List[str] = -1  )					->   List[Any]:





      """simple docstring"""



      def lr_lambda(A__			:     Any  ):
            if current_step < num_warmup_steps:
                  return float(__UpperCAmelCase  ) / float(max(1.0   ,     __UpperCAmelCase  )  )
            return 1.0

      return LambdaLR(__UpperCAmelCase   ,     __UpperCAmelCase   ,     last_epoch=__UpperCAmelCase  )
def   a   (    A__			:     Any   ,     A__			:     Union[str, Any]   ,     A__			:     Optional[Any] = -1  )					->   int:





      """simple docstring"""



      _lowercase			 ={}
      _lowercase			 =step_rules.split(','  )
      for rule_str in rule_list[:-1]:
            _lowercase , _lowercase			 =rule_str.split(':'  )
            _lowercase			 =int(__UpperCAmelCase  )
            _lowercase			 =float(__UpperCAmelCase  )
            _lowercase			 =value
      _lowercase			 =float(rule_list[-1]  )

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

            return rule_func

      _lowercase			 =create_rules_function(__UpperCAmelCase   ,     __UpperCAmelCase  )

      return LambdaLR(__UpperCAmelCase   ,     __UpperCAmelCase   ,     last_epoch=__UpperCAmelCase  )
def   a   (    A__			:     Optional[Any]   ,     A__			:     int   ,     A__			:     List[Any]   ,     A__			:     List[str]=-1  )					->   int:





      """simple docstring"""



      def lr_lambda(A__			:     Optional[int]  ):
            if current_step < num_warmup_steps:
                  return float(__UpperCAmelCase  ) / float(max(1   ,     __UpperCAmelCase  )  )
            return max(
                0.0   ,     float(num_training_steps - current_step  ) / float(max(1   ,     num_training_steps - num_warmup_steps  )  )  )

      return LambdaLR(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase  )
def   a   (    A__			:     Tuple   ,     A__			:     str   ,     A__			:     str   ,     A__			:     Optional[Any] = 0.5   ,     A__			:     List[str] = -1  )					->   Dict:





      """simple docstring"""



      def lr_lambda(A__			:     List[str]  ):
            if current_step < num_warmup_steps:
                  return float(__UpperCAmelCase  ) / float(max(1   ,     __UpperCAmelCase  )  )
            _lowercase			 =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(__UpperCAmelCase  ) * 2.0 * progress  ))  )

      return LambdaLR(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase  )
def   a   (    A__			:     Union[str, Any]   ,     A__			:     str   ,     A__			:     Any   ,     A__			:     Union[str, Any] = 1   ,     A__			:     Optional[Any] = -1  )					->   Tuple:





      """simple docstring"""



      def lr_lambda(A__			:     str  ):
            if current_step < num_warmup_steps:
                  return float(__UpperCAmelCase  ) / float(max(1   ,     __UpperCAmelCase  )  )
            _lowercase			 =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(__UpperCAmelCase  ) * progress) % 1.0)  ))  )

      return LambdaLR(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase  )
def   a   (    A__			:     Dict   ,     A__			:     Optional[int]   ,     A__			:     Tuple   ,     A__			:     List[str]=1e-7   ,     A__			:     Any=1.0   ,     A__			:     Optional[Any]=-1  )					->   Tuple:





      """simple docstring"""



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

      return LambdaLR(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase  )


lowercase_             = {
    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   a   (    A__			:     Tuple   ,     A__			:     Optional[Any]   ,     A__			:     int = None   ,     A__			:     Dict = None   ,     A__			:     Union[str, Any] = None   ,     A__			:     int = 1   ,     A__			:     Tuple = 1.0   ,     A__			:     List[Any] = -1   ,     )					->   str:





      """simple docstring"""



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

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

      # 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(__UpperCAmelCase   ,     num_warmup_steps=__UpperCAmelCase   ,     last_epoch=__UpperCAmelCase  )

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

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

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





import requests

lowercase_             =     'YOUR API KEY'
def   a   (    A__			:     str   ,     A__			:     str = giphy_api_key  )					->   list:





      """simple docstring"""



      _lowercase			 ='+'.join(query.split()  )
      _lowercase			 =F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
      _lowercase			 =requests.get(A__  ).json()['data']
      return [gif["url"] for gif in gifs]


if __name__ == "__main__":
  print('\n'.join(get_gifs('space ship')))


