import requests

_SCREAMING_SNAKE_CASE										=						''  # <-- Put your OpenWeatherMap appid here!
_SCREAMING_SNAKE_CASE										=						'https://api.openweathermap.org/data/2.5/'






def  snake_case  (       snake_case__			:str = "Chicago"   ,	snake_case__			:str = APPID)						->   dict:
       return requests.get(URL_BASE + """weather"""   ,	params=locals()).json()






def  snake_case  (       snake_case__			:str = "Kolkata, India"   ,	snake_case__			:str = APPID)						->   dict:
       return requests.get(URL_BASE + """forecast"""   ,	params=locals()).json()






def  snake_case  (       snake_case__			:float = 55.68   ,	snake_case__			:float = 12.57   ,	snake_case__			:str = APPID)						->   dict:
       return requests.get(URL_BASE + """onecall"""   ,	params=locals()).json()


if __name__ == "__main__":
    from pprint import pprint

    while True:
        _SCREAMING_SNAKE_CASE										=						input('Enter a location:').strip()
        if location:
            pprint(current_weather(location))
        else:
            break





import torch
from torch import nn



class 				a							(			nn.Module       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=1		,				lowerCAmelCase_=False							)  ->					Any:
        super().__init__()

        _A        =  n_token
        _A        =  d_embed
        _A        =  d_proj

        _A        =  cutoffs + [n_token]
        _A        =  [0] + self.cutoffs
        _A        =  div_val

        _A        =  self.cutoffs[0]
        _A        =  len(self.cutoffs							) - 1
        _A        =  self.shortlist_size + self.n_clusters

        if self.n_clusters > 0:
               _A        =  nn.Parameter(torch.zeros(self.n_clusters		,				self.d_embed							)							)
               _A        =  nn.Parameter(torch.zeros(self.n_clusters							)							)

        _A        =  nn.ModuleList()
        _A        =  nn.ParameterList()

        if div_val == 1:
               for i in range(len(self.cutoffs							)							):
                      if d_proj != d_embed:
                             self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCAmelCase_		,				lowerCAmelCase_							)							)							)
                      else:
                             self.out_projs.append(lowerCAmelCase_							)

               self.out_layers.append(nn.Linear(lowerCAmelCase_		,				lowerCAmelCase_							)							)
        else:
               for i in range(len(self.cutoffs							)							):
                      _A   ,  _A        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                      _A        =  d_embed // (div_val**i)

                      self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCAmelCase_		,				lowerCAmelCase_							)							)							)

                      self.out_layers.append(nn.Linear(lowerCAmelCase_		,				r_idx - l_idx							)							)

        _A        =  keep_order



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        if proj is None:
               _A        =  nn.functional.linear(lowerCAmelCase_		,				lowerCAmelCase_		,				bias=lowerCAmelCase_							)
        else:
               # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
               _A        =  nn.functional.linear(lowerCAmelCase_		,				proj.t().contiguous()							)
               _A        =  nn.functional.linear(lowerCAmelCase_		,				lowerCAmelCase_		,				bias=lowerCAmelCase_							)
               # else:
               #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
               #     if bias is not None:
               #         logit = logit + bias

        return logit



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=None		,				lowerCAmelCase_=False							)  ->					List[Any]:

        if labels is not None:
               # Shift so that tokens < n predict n
               _A        =  hidden[..., :-1, :].contiguous()
               _A        =  labels[..., 1:].contiguous()
               _A        =  hidden.view(-1		,				hidden.size(-1							)							)
               _A        =  labels.view(-1							)
               if hidden.size(0							) != labels.size(0							):
                      raise RuntimeError("""Input and labels should have the same size in the batch dimension."""							)
        else:
               _A        =  hidden.view(-1		,				hidden.size(-1							)							)

        if self.n_clusters == 0:
               _A        =  self._compute_logit(lowerCAmelCase_		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]							)
               if labels is not None:
                      _A        =  labels != -1_00
                      _A        =  torch.zeros_like(lowerCAmelCase_		,				dtype=hidden.dtype		,				device=hidden.device							)
                      _A        =  (
                          -nn.functional.log_softmax(lowerCAmelCase_		,				dim=-1							)[mask].gather(1		,				labels[mask].unsqueeze(1							)							).squeeze(1							)
                      )
               else:
                      _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=-1							)
        else:
               # construct weights and biases
               _A   ,  _A        =  [], []
               for i in range(len(self.cutoffs							)							):
                      if self.div_val == 1:
                             _A   ,  _A        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                             _A        =  self.out_layers[0].weight[l_idx:r_idx]
                             _A        =  self.out_layers[0].bias[l_idx:r_idx]
                      else:
                             _A        =  self.out_layers[i].weight
                             _A        =  self.out_layers[i].bias

                      if i == 0:
                             _A        =  torch.cat([weight_i, self.cluster_weight]		,				dim=0							)
                             _A        =  torch.cat([bias_i, self.cluster_bias]		,				dim=0							)

                      weights.append(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

               _A   ,  _A   ,  _A        =  weights[0], biases[0], self.out_projs[0]

               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
               _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

               if labels is None:
                      _A        =  hidden.new_empty((head_logit.size(0							), self.n_token)							)
               else:
                      _A        =  torch.zeros_like(lowerCAmelCase_		,				dtype=hidden.dtype		,				device=hidden.device							)

               _A        =  0
               _A        =  [0] + self.cutoffs
               for i in range(len(lowerCAmelCase_							) - 1							):
                      _A   ,  _A        =  cutoff_values[i], cutoff_values[i + 1]

                      if labels is not None:
                             _A        =  (labels >= l_idx) & (labels < r_idx)
                             _A        =  mask_i.nonzero().squeeze()

                             if indices_i.numel() == 0:
                                    continue

                             _A        =  labels.index_select(0		,				lowerCAmelCase_							) - l_idx
                             _A        =  head_logprob.index_select(0		,				lowerCAmelCase_							)
                             _A        =  hidden.index_select(0		,				lowerCAmelCase_							)
                      else:
                             _A        =  hidden

                      if i == 0:
                             if labels is not None:
                                    _A        =  head_logprob_i.gather(1		,				target_i[:, None]							).squeeze(1							)
                             else:
                                    _A        =  head_logprob[:, : self.cutoffs[0]]
                      else:
                             _A   ,  _A   ,  _A        =  weights[i], biases[i], self.out_projs[i]

                             _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)
                             _A        =  self.cutoffs[0] + i - 1  # No probability for the head cluster
                             if labels is not None:
                                    _A        =  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                                        1		,				target_i[:, None]							).squeeze(1							)
                             else:
                                    _A        =  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                                    _A        =  logprob_i

                      if labels is not None:
                             if (hasattr(self		,				"""keep_order"""							) and self.keep_order) or keep_order:
                                    out.index_copy_(0		,				lowerCAmelCase_		,				-logprob_i							)
                             else:
                                    out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
                             offset += logprob_i.size(0							)

        return out



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					int:
        if self.n_clusters == 0:
               _A        =  self._compute_logit(lowerCAmelCase_		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]							)
               return nn.functional.log_softmax(lowerCAmelCase_		,				dim=-1							)
        else:
               # construct weights and biases
               _A   ,  _A        =  [], []
               for i in range(len(self.cutoffs							)							):
                      if self.div_val == 1:
                             _A   ,  _A        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                             _A        =  self.out_layers[0].weight[l_idx:r_idx]
                             _A        =  self.out_layers[0].bias[l_idx:r_idx]
                      else:
                             _A        =  self.out_layers[i].weight
                             _A        =  self.out_layers[i].bias

                      if i == 0:
                             _A        =  torch.cat([weight_i, self.cluster_weight]		,				dim=0							)
                             _A        =  torch.cat([bias_i, self.cluster_bias]		,				dim=0							)

                      weights.append(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

               _A   ,  _A   ,  _A        =  weights[0], biases[0], self.out_projs[0]
               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

               _A        =  hidden.new_empty((head_logit.size(0							), self.n_token)							)
               _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

               _A        =  [0] + self.cutoffs
               for i in range(len(lowerCAmelCase_							) - 1							):
                      _A   ,  _A        =  cutoff_values[i], cutoff_values[i + 1]

                      if i == 0:
                             _A        =  head_logprob[:, : self.cutoffs[0]]
                      else:
                             _A   ,  _A   ,  _A        =  weights[i], biases[i], self.out_projs[i]

                             _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

                             _A        =  head_logprob[:, -i] + tail_logprob_i
                             _A        =  logprob_i

               return out




