


from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser






@dataclass
class      UpperCamelCase					:


   '''simple docstring'''







   lowercase					:	str									=field(
       metadata={"""help""": """The output directory where the model will be written."""}						,      )
   lowercase					:	str									=field(
       metadata={
           """help""": (
               """The encoder model checkpoint for weights initialization."""
               """Don't set if you want to train an encoder model from scratch."""
           )
       }						,      )
   lowercase					:	str									=field(
       metadata={
           """help""": (
               """The decoder model checkpoint for weights initialization."""
               """Don't set if you want to train a decoder model from scratch."""
           )
       }						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}     )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}     )




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





     lowercase_       :Tuple     =       HfArgumentParser((ModelArguments,)		)
     (lowercase_      )       :Optional[int]     =       parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

     # Use explicit specified encoder config
     if model_args.encoder_config_name:
          lowercase_       :Optional[int]     =       AutoConfig.from_pretrained(model_args.encoder_config_name		)
     # Use pretrained encoder model's config
     else:
          lowercase_       :List[Any]     =       AutoConfig.from_pretrained(model_args.encoder_model_name_or_path		)

     # Use explicit specified decoder config
     if model_args.decoder_config_name:
          lowercase_       :List[Any]     =       AutoConfig.from_pretrained(model_args.decoder_config_name		)
     # Use pretrained decoder model's config
     else:
          lowercase_       :List[str]     =       AutoConfig.from_pretrained(model_args.decoder_model_name_or_path		)

     # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
     lowercase_       :Optional[Any]     =       True
     lowercase_       :List[Any]     =       True

     lowercase_       :List[Any]     =       FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
         encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path       ,		decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path       ,		encoder_config=_a       ,		decoder_config=_a       ,		)

     # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
     lowercase_       :int     =       decoder_config.decoder_start_token_id
     lowercase_       :List[Any]     =       decoder_config.pad_token_id
     if decoder_start_token_id is None:
          lowercase_       :Optional[int]     =       decoder_config.bos_token_id
     if pad_token_id is None:
          lowercase_       :Dict     =       decoder_config.eos_token_id

     # This is necessary to make Flax's generate() work
     lowercase_       :Any     =       decoder_config.eos_token_id
     lowercase_       :Optional[Any]     =       decoder_start_token_id
     lowercase_       :Union[str, Any]     =       pad_token_id

     lowercase_       :int     =       AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path		)

     lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path		)
     lowercase_       :Optional[int]     =       tokenizer.convert_ids_to_tokens(model.config.pad_token_id		)

     model.save_pretrained(model_args.output_dir		)
     image_processor.save_pretrained(model_args.output_dir		)
     tokenizer.save_pretrained(model_args.output_dir		)


if __name__ == "__main__":
   main()





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}")

