


'''simple docstring'''



from math import sqrt

import numpy as np
from sympy import symbols

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

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







def       _UpperCamelCase (						UpperCamelCase__   ):
			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 (						UpperCamelCase__   ):
			return 1 / sqrt(1 - beta(UpperCamelCase__   ) ** 2   )







def       _UpperCamelCase (						UpperCamelCase__   ):
			return np.array(
			    [
			        [gamma(UpperCamelCase__   ), -gamma(UpperCamelCase__   ) * beta(UpperCamelCase__   ), 0, 0],
			        [-gamma(UpperCamelCase__   ) * beta(UpperCamelCase__   ), gamma(UpperCamelCase__   ), 0, 0],
			        [0, 0, 1, 0],
			        [0, 0, 0, 1],
			    ]   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None   ):
			# Ensure event is not empty
			if event is None:
						UpperCAmelCase__					:   Union[str, Any]       				=       np.array([ct, x, y, z]   )  # Symbolic four vector
			else:
						event[0] *= c  # x0 is ct (speed of light * time)

			return transformation_matrix(UpperCamelCase__   ) @ event


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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

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



'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       Encoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							down_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							double_z=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)