"""simple docstring"""
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			Optional[Any]:





      """simple docstring"""






      snake_case        =     [
          'decoder.version',
          'decoder.output_projection.weight',
          '_float_tensor',
          'decoder.embed_positions._float_tensor',
      ]
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase	,     _UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			Union[str, Any]:





      """simple docstring"""






      snake_case   ,snake_case        =     emb.weight.shape
      snake_case        =     nn.Linear(_UpperCamelCase	,     _UpperCamelCase	,     bias=_UpperCamelCase   )
      snake_case        =     emb.weight.data
      return lin_layer





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]   )   ->			List[Any]:





      """simple docstring"""






      snake_case        =     torch.load(_UpperCamelCase	,     map_location='cpu'   )
      snake_case        =     Namespace(**checkpoint['cfg']['model']   )
      snake_case        =     checkpoint['model']
      remove_ignore_keys_(_UpperCamelCase   )
      snake_case        =     state_dict['decoder.embed_tokens.weight'].shape[0]

      snake_case        =     {key.replace('decoder'	,     'model'   ): val for key, val in state_dict.items()}

      snake_case        =     XGLMConfig(
          vocab_size=_UpperCamelCase	,     max_position_embeddings=args.max_target_positions	,     num_layers=args.decoder_layers	,     attention_heads=args.decoder_attention_heads	,     ffn_dim=args.decoder_ffn_embed_dim	,     d_model=args.decoder_embed_dim	,     layerdrop=args.decoder_layerdrop	,     dropout=args.dropout	,     attention_dropout=args.attention_dropout	,     activation_dropout=args.activation_dropout	,     activation_function='gelu'	,     scale_embedding=not args.no_scale_embedding	,     tie_word_embeddings=args.share_decoder_input_output_embed	,     )

      snake_case        =     XGLMForCausalLM(_UpperCamelCase   )
      snake_case        =     model.load_state_dict(_UpperCamelCase	,     strict=_UpperCamelCase   )
      print(_UpperCamelCase   )
      snake_case        =     make_linear_from_emb(model.model.embed_tokens   )

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
     parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     SCREAMING_SNAKE_CASE__   =					parser.parse_args()
     SCREAMING_SNAKE_CASE__   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
     model.save_pretrained(args.pytorch_dump_folder_path)



"""simple docstring"""
import cmath
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float   )   ->			complex:





						"""simple docstring"""






						snake_case        =     math.radians(_UpperCamelCase   )
						snake_case        =     math.radians(_UpperCamelCase   )

						# Convert voltage and current to rectangular form
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )

						# Calculate apparent power
						return voltage_rect * current_rect


if __name__ == "__main__":
					import doctest

					doctest.testmod()


