


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def     UpperCamelCase    (      _a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :int     =       [
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''decoder.output_projection.weight''',
         '''_float_tensor''',
         '''encoder.embed_positions._float_tensor''',
         '''decoder.embed_positions._float_tensor''',
     ]
     for k in ignore_keys:
          state_dict.pop(_a       ,		_a		)




def     UpperCamelCase    (      _a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       list(s_dict.keys()		)
     for key in keys:
          if "transformer_layers" in key:
               lowercase_       :List[str]     =       s_dict.pop(_a		)
          elif "subsample" in key:
               lowercase_       :Optional[int]     =       s_dict.pop(_a		)




def     UpperCamelCase    (      _a		)					->							Dict:
     '''simple docstring'''





     lowercase_    ,      lowercase_       :Optional[Any]     =       emb.weight.shape
     lowercase_       :Optional[int]     =       nn.Linear(_a       ,		_a       ,		bias=_a		)
     lowercase_       :Optional[Any]     =       emb.weight.data
     return lin_layer




def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     lowercase_       :List[str]     =       torch.load(_a       ,		map_location='''cpu'''		)
     lowercase_       :int     =       mam_aaa['''args''']
     lowercase_       :List[str]     =       mam_aaa['''model''']
     lowercase_       :Optional[Any]     =       state_dict['''decoder.output_projection.weight''']

     remove_ignore_keys_(_a		)
     rename_keys(_a		)

     lowercase_       :Union[str, Any]     =       state_dict['''decoder.embed_tokens.weight'''].shape[0]

     lowercase_       :Optional[Any]     =       args.share_decoder_input_output_embed

     lowercase_       :Union[str, Any]     =       [int(_a		) for i in args.conv_kernel_sizes.split(''','''		)]
     lowercase_       :Tuple     =       SpeechaTextConfig(
         vocab_size=_a       ,		max_source_positions=args.max_source_positions       ,		max_target_positions=args.max_target_positions       ,		encoder_layers=args.encoder_layers       ,		decoder_layers=args.decoder_layers       ,		encoder_attention_heads=args.encoder_attention_heads       ,		decoder_attention_heads=args.decoder_attention_heads       ,		encoder_ffn_dim=args.encoder_ffn_embed_dim       ,		decoder_ffn_dim=args.decoder_ffn_embed_dim       ,		d_model=args.encoder_embed_dim       ,		dropout=args.dropout       ,		attention_dropout=args.attention_dropout       ,		activation_dropout=args.activation_dropout       ,		activation_function='''relu'''       ,		num_conv_layers=len(_a		)       ,		conv_channels=args.conv_channels       ,		conv_kernel_sizes=_a       ,		input_feat_per_channel=args.input_feat_per_channel       ,		input_channels=args.input_channels       ,		tie_word_embeddings=_a       ,		num_beams=5       ,		max_length=2_0_0       ,		use_cache=_a       ,		decoder_start_token_id=2       ,		early_stopping=_a       ,		)

     lowercase_       :Union[str, Any]     =       SpeechaTextForConditionalGeneration(_a		)
     lowercase_    ,      lowercase_       :Optional[int]     =       model.model.load_state_dict(_a       ,		strict=_a		)
     if len(_a		) > 0 and not set(_a		) <= {
         "encoder.embed_positions.weights",
         "decoder.embed_positions.weights",
     }:
          raise ValueError(
              '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
              f" but all the following weights are missing {missing}"		)

     if tie_embeds:
          lowercase_       :Union[str, Any]     =       make_linear_from_emb(model.model.decoder.embed_tokens		)
     else:
          lowercase_       :int     =       lm_head_weights

     model.save_pretrained(_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		str         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   SCREAMING_SNAKE_CASE       :		List[str]         =				parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE       :		int         =				299_792_458

# Symbols
SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Optional[Any]         =				symbols("ct x y z")




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     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     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return 1 / sqrt(1 - beta(_a		) ** 2		)




def     UpperCamelCase    (      _a		)					->							np.ndarray:
     '''simple docstring'''





     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     UpperCamelCase    (      _a       ,		_a = None		)					->							np.ndarray:
     '''simple docstring'''





     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:
   SCREAMING_SNAKE_CASE       :		List[str]         =				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
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{ct: c, x: 1, y: 1, z: 1}
   SCREAMING_SNAKE_CASE       :		str         =				[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f"\n{numerical_vector}")

