





"""simple docstring"""




import shutil

import requests





def        a_				(							_lowercase       ,						_lowercase = False       ,						_lowercase = "."   ):
						_UpperCamelCase			:					Union[str, Any]     =    '''https://api.nasa.gov/planetary/apod'''
						return requests.get(_lowercase       ,						params={'''api_key''': api_key}   ).json()





def        a_				(							_lowercase       ,						_lowercase = "."   ):
						_UpperCamelCase			:					Dict     =    get_apod_data(_lowercase   )
						_UpperCamelCase			:					Optional[Any]     =    apod_data['''url''']
						_UpperCamelCase			:					Union[str, Any]     =    img_url.split('''/'''   )[-1]
						_UpperCamelCase			:					Optional[int]     =    requests.get(_lowercase       ,						stream=_lowercase   )

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





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Any     =    '''https://images-api.nasa.gov/search'''
						return requests.get(_lowercase       ,						params={'''q''': query}   ).json()


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




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



