


"""simple docstring"""



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase      		=			299_792_458

# Symbols
UpperCAmelCase		,	UpperCAmelCase		,	UpperCAmelCase		,	UpperCAmelCase      		=			symbols('''ct x y z''')






def       lowerCamelCase					(a_						:float)     ->							float:
       if velocity > c:
              raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''')
       elif velocity < 1:
              # Usually the speed should be much higher than 1 (c order of magnitude)
              raise ValueError('''Speed must be greater than or equal to 1!''')

       return velocity / c






def       lowerCamelCase					(a_						:float)     ->							float:
       return 1 / sqrt(1 - beta(a_) ** 2)






def       lowerCamelCase					(a_						:float)     ->							np.ndarray:
       return np.array(
           [
               [gamma(a_), -gamma(a_) * beta(a_), 0, 0],
               [-gamma(a_) * beta(a_), gamma(a_), 0, 0],
               [0, 0, 1, 0],
               [0, 0, 0, 1],
           ])






def       lowerCamelCase					(a_						:float				,				a_						:np.ndarray | None = None)     ->							np.ndarray:
       # Ensure event is not empty
       if event is None:
              lowercase		:Optional[int]       							=				np.array([ct, x, y, z])  # Symbolic four vector
       else:
              event[0] *= c  # x0 is ct (speed of light * time)

       return transformation_matrix(a_) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 UpperCAmelCase      		=			transform(29_979_245)
 print('''Example of four vector: ''')
 print(F"""ct' = {four_vector[0]}""")
 print(F"""x' = {four_vector[1]}""")
 print(F"""y' = {four_vector[2]}""")
 print(F"""z' = {four_vector[3]}""")

 # Substitute symbols with numerical values
 UpperCAmelCase      		=			{ct: c, x: 1, y: 1, z: 1}
 UpperCAmelCase      		=			[four_vector[i].subs(sub_dict) for i in range(4)]

 print(F"""\n{numerical_vector}""")





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       lowerCamelCase					(a_						:Dict)     ->							Dict:
       lowercase		:Tuple       							=				[
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(a_				,				a_)






def       lowerCamelCase					(a_						:Union[str, Any])     ->							str:
       lowercase							,  lowercase		:Tuple       							=				emb.weight.shape
       lowercase		:List[str]       							=				nn.Linear(a_				,				a_				,				bias=a_)
       lowercase		:List[str]       							=				emb.weight.data
       return lin_layer






def       lowerCamelCase					(a_						:int				,				a_						:Union[str, Any]="facebook/mbart-large-en-ro"				,				a_						:Union[str, Any]=False				,				a_						:List[Any]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				torch.load(a_				,				map_location='''cpu''')['''model''']
       remove_ignore_keys_(a_)
       lowercase		:Dict       							=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase		:Tuple       							=				MBartConfig.from_pretrained(a_				,				vocab_size=a_)
       if mbart_aa and finetuned:
              lowercase		:List[Any]       							=				'''relu'''

       lowercase		:Optional[int]       							=				state_dict['''decoder.embed_tokens.weight''']
       lowercase		:Union[str, Any]       							=				MBartForConditionalGeneration(a_)
       model.model.load_state_dict(a_)

       if finetuned:
              lowercase		:Dict       							=				make_linear_from_emb(model.model.shared)

       return model


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
 parser.add_argument(
     '''--hf_config''',
     default='''facebook/mbart-large-cc25''',
     type=str,
     help='''Which huggingface architecture to use: mbart-large''',
 )
 parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
 parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
 UpperCAmelCase      		=			parser.parse_args()
 UpperCAmelCase      		=			convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)

