"""simple docstring"""


import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  [
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

UpperCAmelCase__     						=  [
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					torch.load(lowercase      ,map_location="""cpu"""						)
  return sd






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase=rename_keys_prefix						):







  """simple docstring"""
  _UpperCAmelCase         =					OrderedDict()
  _UpperCAmelCase         =					torch.arange(config.max_position_embeddings						).expand((1, -1)						)
  # detector_d = OrderedDict()
  for key in d:
    if "detector" in key:
      # detector_d[key.replace('detector.','')] = d[key]
      continue
    _UpperCAmelCase         =					key
    for name_pair in rename_keys_prefix:
      _UpperCAmelCase         =					new_key.replace(name_pair[0]      ,name_pair[1]						)
    _UpperCAmelCase         =					d[key]
    if key == "bert.cls.predictions.decoder.weight":
      # Old bert code didn't have `decoder.bias`, but was added separately
      _UpperCAmelCase         =					new_d["""cls.predictions.bias"""]
  return new_d






@torch.no_grad()
def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  assert (
      checkpoint_path.split("""/"""						)[-1] in ACCEPTABLE_CHECKPOINTS
  ), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

  # Get Config
  if "pre" in checkpoint_path:
    _UpperCAmelCase         =					"""pretraining"""
    if "vcr" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 5_12}
    elif "vqa_advanced" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 20_48}
    elif "vqa" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 20_48}
    elif "nlvr" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 10_24}
    else:
      raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''						)
  else:
    if "vcr" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 5_12}
      _UpperCAmelCase         =					"""multichoice"""
    elif "vqa_advanced" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 20_48}
      _UpperCAmelCase         =					"""vqa_advanced"""
    elif "vqa" in checkpoint_path:
      _UpperCAmelCase         =					{"""visual_embedding_dim""": 20_48, """num_labels""": 31_29}
      _UpperCAmelCase         =					"""vqa"""
    elif "nlvr" in checkpoint_path:
      _UpperCAmelCase         =					{
          """visual_embedding_dim""": 10_24,
          """num_labels""": 2,
      }
      _UpperCAmelCase         =					"""nlvr"""

  _UpperCAmelCase         =					VisualBertConfig(**lowercase						)

  # Load State Dict
  _UpperCAmelCase         =					load_state_dict(lowercase						)

  _UpperCAmelCase         =					get_new_dict(lowercase      ,lowercase						)

  if model_type == "pretraining":
    _UpperCAmelCase         =					VisualBertForPreTraining(lowercase						)
  elif model_type == "vqa":
    _UpperCAmelCase         =					VisualBertForQuestionAnswering(lowercase						)
  elif model_type == "nlvr":
    _UpperCAmelCase         =					VisualBertForVisualReasoning(lowercase						)
  elif model_type == "multichoice":
    _UpperCAmelCase         =					VisualBertForMultipleChoice(lowercase						)

  model.load_state_dict(lowercase						)
  # Save Checkpoints
  Path(lowercase						).mkdir(exist_ok=lowercase						)
  model.save_pretrained(lowercase						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
    parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
    UpperCAmelCase__     						=  parser.parse_args()
    convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""


import requests

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






def 			__UpperCAmelCase    (	lowercase = "Chicago"      ,lowercase = APPID						):







  """simple docstring"""
  return requests.get(URL_BASE + """weather"""      ,params=locals()						).json()






def 			__UpperCAmelCase    (	lowercase = "Kolkata, India"      ,lowercase = APPID						):







  """simple docstring"""
  return requests.get(URL_BASE + """forecast"""      ,params=locals()						).json()






def 			__UpperCAmelCase    (	lowercase = 55.68      ,lowercase = 12.57      ,lowercase = APPID						):







  """simple docstring"""
  return requests.get(URL_BASE + """onecall"""      ,params=locals()						).json()


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

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