




'''simple docstring'''


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int							):




     """simple docstring"""


     if "model" in orig_key:
          a_    :       int    	=     orig_key.replace("""model."""   ,						""""""							)
     if "norm1" in orig_key:
          a_    :       Union[str, Any]    	=     orig_key.replace("""norm1"""   ,						"""attention.output.LayerNorm"""							)
     if "norm2" in orig_key:
          a_    :       Tuple    	=     orig_key.replace("""norm2"""   ,						"""output.LayerNorm"""							)
     if "norm" in orig_key:
          a_    :       str    	=     orig_key.replace("""norm"""   ,						"""LayerNorm"""							)
     if "transformer" in orig_key:
          a_    :       List[Any]    	=     orig_key.split("""."""							)[0].split("""_"""							)[-1]
          a_    :       List[Any]    	=     orig_key.replace(F"transformer_{layer_num}"   ,						F"encoder.layer.{layer_num}"							)
     if "mha.attn" in orig_key:
          a_    :       Union[str, Any]    	=     orig_key.replace("""mha.attn"""   ,						"""attention.self"""							)
     if "mha" in orig_key:
          a_    :       Any    	=     orig_key.replace("""mha"""   ,						"""attention"""							)
     if "W_q" in orig_key:
          a_    :       List[Any]    	=     orig_key.replace("""W_q"""   ,						"""self.query"""							)
     if "W_k" in orig_key:
          a_    :       List[Any]    	=     orig_key.replace("""W_k"""   ,						"""self.key"""							)
     if "W_v" in orig_key:
          a_    :       Optional[int]    	=     orig_key.replace("""W_v"""   ,						"""self.value"""							)
     if "ff1" in orig_key:
          a_    :       Dict    	=     orig_key.replace("""ff1"""   ,						"""intermediate.dense"""							)
     if "ff2" in orig_key:
          a_    :       Optional[Any]    	=     orig_key.replace("""ff2"""   ,						"""output.dense"""							)
     if "ff" in orig_key:
          a_    :       Tuple    	=     orig_key.replace("""ff"""   ,						"""output.dense"""							)
     if "mlm_class" in orig_key:
          a_    :       Any    	=     orig_key.replace("""mlm.mlm_class"""   ,						"""cls.predictions.decoder"""							)
     if "mlm" in orig_key:
          a_    :       Any    	=     orig_key.replace("""mlm"""   ,						"""cls.predictions.transform"""							)
     if "cls" not in orig_key:
          a_    :       Dict    	=     "nystromformer." + orig_key

     return orig_key



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				str							):




     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          a_    :       Dict    	=     orig_state_dict.pop(UpperCamelCase__							)

          if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
               continue
          else:
               a_    :       Dict    	=     val

     a_    :       List[str]    	=     orig_state_dict["cls.predictions.decoder.bias"]
     a_    :       int    	=     (
         torch.arange(config.max_position_embeddings							).expand((1, -1)							) + 2
     )

     return orig_state_dict



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int   ,						UpperCamelCase__						:				Optional[int]   ,						UpperCamelCase__						:				Union[str, Any]							):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     torch.load(UpperCamelCase__   ,						map_location="""cpu"""							)["model_state_dict"]
     a_    :       Dict    	=     NystromformerConfig.from_json_file(UpperCamelCase__							)
     a_    :       Optional[Any]    	=     NystromformerForMaskedLM(UpperCamelCase__							)

     a_    :       Optional[int]    	=     convert_checkpoint_helper(UpperCamelCase__   ,						UpperCamelCase__							)

     model.load_state_dict(UpperCamelCase__							)
     model.eval()
     model.save_pretrained(UpperCamelCase__							)

     print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)


if __name__ == "__main__":
       lowerCAmelCase_			:     Optional[Any]							    =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help='The json file for Nystromformer model config.',
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowerCAmelCase_			:     Optional[Any]							    =     parser.parse_args()
       convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''


import shutil

import requests



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				bool = False   ,						UpperCamelCase__						:				str = "."							):




     """simple docstring"""


     a_    :       Any    	=     """https://api.nasa.gov/planetary/apod"""
     return requests.get(UpperCamelCase__   ,						params={"""api_key""": api_key}							).json()



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				str = "."							):




     """simple docstring"""


     a_    :       Tuple    	=     get_apod_data(UpperCamelCase__							)
     a_    :       Tuple    	=     apod_data["""url"""]
     a_    :       Any    	=     img_url.split("""/"""							)[-1]
     a_    :       Tuple    	=     requests.get(UpperCamelCase__   ,						stream=UpperCamelCase__							)

     with open(F"{path}/{img_name}"   ,						"""wb+"""							) as img_file:
          shutil.copyfileobj(response.raw   ,						UpperCamelCase__							)
     del response
     return apod_data



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       str    	=     """https://images-api.nasa.gov/search"""
     return requests.get(UpperCamelCase__   ,						params={"""q""": query}							).json()


if __name__ == "__main__":
 print(save_apod('YOUR API KEY'))
 lowerCAmelCase_			:     int							    =     get_archive_data('apollo 2011')['collection']['items']
 print(apollo_aaaa_items[0]['data'][0]['description'])
