

import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand






def     __lowercase					(   _A	)   ->  List[Any]:
      return EnvironmentCommand()







class 				a__				(      UpperCAmelCase				):







  """simple docstring"""




  @staticmethod
  def        _lowercase					(	UpperCAmelCase__			:       Any						)    ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[Any]        =      parser.add_parser("""env"""						)
        download_parser.set_defaults(func=__A						)


  def        _lowercase					(	self			:       Any						)    ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Dict        =      huggingface_hub.__version__

        SCREAMING_SNAKE_CASE       :					str        =      "not installed"
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      "NA"
        if is_torch_available():
              import torch

              SCREAMING_SNAKE_CASE       :					Tuple        =      torch.__version__
              SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      torch.cuda.is_available()

        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      "not installed"
        if is_transformers_available():
              import transformers

              SCREAMING_SNAKE_CASE       :					Tuple        =      transformers.__version__

        SCREAMING_SNAKE_CASE       :					List[Any]        =      "not installed"
        if is_accelerate_available():
              import accelerate

              SCREAMING_SNAKE_CASE       :					List[Any]        =      accelerate.__version__

        SCREAMING_SNAKE_CASE       :					Optional[int]        =      "not installed"
        if is_xformers_available():
              import xformers

              SCREAMING_SNAKE_CASE       :					Any        =      xformers.__version__

        SCREAMING_SNAKE_CASE       :					Optional[int]        =      {
            "`diffusers` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
            "Huggingface_hub version": hub_version,
            "Transformers version": transformers_version,
            "Accelerate version": accelerate_version,
            "xFormers version": xformers_version,
            "Using GPU in script?": "<fill in>",
            "Using distributed or parallel set-up in script?": "<fill in>",
        }

        print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""						)
        print(self.format_dict(__A						)						)

        return info




  @staticmethod
  def        _lowercase					(	UpperCAmelCase__			:       List[str]						)    ->List[Any]:
        """simple docstring"""







        return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]						) + "\n"








from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''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 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   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
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



