"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 	a__   (		UpperCAmelCase__							:      List[str]      )->						Any:
						if "model" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""model."""		,      """"""      )
						if "norm1" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""norm1"""		,      """attention.output.LayerNorm"""      )
						if "norm2" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""norm2"""		,      """output.LayerNorm"""      )
						if "norm" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""norm"""		,      """LayerNorm"""      )
						if "transformer" in orig_key:
												UpperCAmelCase__       						=		orig_key.split("""."""      )[0].split("""_"""      )[-1]
												UpperCAmelCase__       						=		orig_key.replace(f'transformer_{layer_num}'		,      f'encoder.layer.{layer_num}'      )
						if "mha.attn" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""mha.attn"""		,      """attention.self"""      )
						if "mha" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""mha"""		,      """attention"""      )
						if "W_q" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""W_q"""		,      """self.query"""      )
						if "W_k" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""W_k"""		,      """self.key"""      )
						if "W_v" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""W_v"""		,      """self.value"""      )
						if "ff.0" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""ff.0"""		,      """intermediate.dense"""      )
						if "ff.2" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""ff.2"""		,      """output.dense"""      )
						if "ff" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""ff"""		,      """output.dense"""      )
						if "mlm_class" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""mlm.mlm_class"""		,      """cls.predictions.decoder"""      )
						if "mlm" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""mlm"""		,      """cls.predictions.transform"""      )
						if "backbone.backbone.encoders" in orig_key:
												UpperCAmelCase__       						=		orig_key.replace("""backbone.backbone.encoders"""		,      """encoder.layer"""      )
						if "cls" not in orig_key:
												UpperCAmelCase__       						=		"""mra.""" + orig_key

						return orig_key


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      List[Any]      )->						List[str]:
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase__       						=		orig_state_dict.pop(UpperCAmelCase__      )

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		UpperCAmelCase__       						=		val

						UpperCAmelCase__       						=		orig_state_dict["""cls.predictions.decoder.bias"""]
						UpperCAmelCase__       						=		torch.arange(UpperCAmelCase__      ).expand((1, -1)      ) + 2

						return orig_state_dict


def 	a__   (		UpperCAmelCase__							:      Union[str, Any]		,      UpperCAmelCase__							:      Union[str, Any]		,      UpperCAmelCase__							:      Optional[int]      )->						Optional[Any]:
						UpperCAmelCase__       						=		torch.load(UpperCAmelCase__		,      map_location="""cpu"""      )["""model_state_dict"""]
						UpperCAmelCase__       						=		MraConfig.from_json_file(UpperCAmelCase__      )
						UpperCAmelCase__       						=		MraForMaskedLM(UpperCAmelCase__      )

						UpperCAmelCase__       						=		convert_checkpoint_helper(config.max_position_embeddings		,      UpperCAmelCase__      )

						print(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__":
						UpperCamelCase_							:							List[Any]				 =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help="""The json file for Mra model config.""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						UpperCamelCase_							:							List[str]				 =		parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


"""simple docstring"""







import shutil

import requests


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      bool = False		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		"""https://api.nasa.gov/planetary/apod"""
						return requests.get(UpperCAmelCase__		,      params={"""api_key""": api_key}      ).json()


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		get_apod_data(UpperCAmelCase__      )
						UpperCAmelCase__       						=		apod_data["""url"""]
						UpperCAmelCase__       						=		img_url.split("""/"""      )[-1]
						UpperCAmelCase__       						=		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 	a__   (		UpperCAmelCase__							:      str      )->						dict:
						UpperCAmelCase__       						=		"""https://images-api.nasa.gov/search"""
						return requests.get(UpperCAmelCase__		,      params={"""q""": query}      ).json()


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

