code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from __future__ import annotations
  
  from math import pi, pow
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if side_length < 0:
  		raise ValueError("""vol_cube() only accepts non-negative values"""    )
  	return pow(UpperCAmelCase       ,			3    )
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if height < 0 or radius < 0:
  		raise ValueError("""vol_spherical_cap() only accepts non-negative values"""    )
  	# Volume is 1/3 pi * height squared * (3 * radius - height)
  	return 1 / 3 * pi * pow(UpperCAmelCase       ,			2    ) * (3 * radius - height)
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if radius_a < 0 or radius_a < 0 or centers_distance < 0:
  		raise ValueError("""vol_spheres_intersect() only accepts non-negative values"""    )
  	if centers_distance == 0:
  		return vol_sphere(min(UpperCAmelCase       ,			UpperCAmelCase    )    )
  
  	A							  =      (
  	    (radius_a - radius_a + centers_distance)
  	    * (radius_a + radius_a - centers_distance)
  	    / (2 * centers_distance)
  	)
  	A							  =      (
  	    (radius_a - radius_a + centers_distance)
  	    * (radius_a + radius_a - centers_distance)
  	    / (2 * centers_distance)
  	)
  
  	return vol_spherical_cap(UpperCAmelCase       ,			UpperCAmelCase    ) + vol_spherical_cap(UpperCAmelCase       ,			UpperCAmelCase    )
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
  		raise ValueError(
  		    """vol_spheres_union() only accepts non-negative values, non-zero radius"""    )
  
  	if centers_distance == 0:
  		return vol_sphere(max(UpperCAmelCase       ,			UpperCAmelCase    )    )
  
  	return (
  	    vol_sphere(UpperCAmelCase    )
  	    + vol_sphere(UpperCAmelCase    )
  	    - vol_spheres_intersect(UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )
  	)
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if width < 0 or height < 0 or length < 0:
  		raise ValueError("""vol_cuboid() only accepts non-negative values"""    )
  	return float(width * height * length    )
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if height < 0 or area_of_base < 0:
  		raise ValueError("""vol_cone() only accepts non-negative values"""    )
  	return area_of_base * height / 3.0
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if height < 0 or radius < 0:
  		raise ValueError("""vol_right_circ_cone() only accepts non-negative values"""    )
  	return pi * pow(UpperCAmelCase       ,			2    ) * height / 3.0
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if height < 0 or area_of_base < 0:
  		raise ValueError("""vol_prism() only accepts non-negative values"""    )
  	return float(area_of_base * height    )
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if height < 0 or area_of_base < 0:
  		raise ValueError("""vol_pyramid() only accepts non-negative values"""    )
  	return area_of_base * height / 3.0
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if radius < 0:
  		raise ValueError("""vol_sphere() only accepts non-negative values"""    )
  	# Volume is 4/3 * pi * radius cubed
  	return 4 / 3 * pi * pow(UpperCAmelCase       ,			3    )
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if radius < 0:
  		raise ValueError("""vol_hemisphere() only accepts non-negative values"""    )
  	# Volume is radius cubed * pi * 2/3
  	return pow(UpperCAmelCase       ,			3    ) * pi * 2 / 3
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if height < 0 or radius < 0:
  		raise ValueError("""vol_circular_cylinder() only accepts non-negative values"""    )
  	# Volume is radius squared * height * pi
  	return pow(UpperCAmelCase       ,			2    ) * height * pi
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if inner_radius < 0 or outer_radius < 0 or height < 0:
  		raise ValueError(
  		    """vol_hollow_circular_cylinder() only accepts non-negative values"""    )
  	if outer_radius <= inner_radius:
  		raise ValueError("""outer_radius must be greater than inner_radius"""    )
  	return pi * (pow(UpperCAmelCase       ,			2    ) - pow(UpperCAmelCase       ,			2    )) * height
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if radius_a < 0 or radius_a < 0 or height < 0:
  		raise ValueError("""vol_conical_frustum() only accepts non-negative values"""    )
  	return (
  	    1
  	    / 3
  	    * pi
  	    * height
  	    * (pow(UpperCAmelCase       ,			2    ) + pow(UpperCAmelCase       ,			2    ) + radius_a * radius_a)
  	)
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->float:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if torus_radius < 0 or tube_radius < 0:
  		raise ValueError("""vol_torus() only accepts non-negative values"""    )
  	return 2 * pow(UpperCAmelCase       ,			2    ) * torus_radius * pow(UpperCAmelCase       ,			2    )
  
  
  
  
  def    __a (     )      ->Dict:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	print("""Volumes:"""    )
  	print(f"""Cube: {vol_cube(2    ) = }"""    )  # = 8
  	print(f"""Cuboid: {vol_cuboid(2       ,			2       ,			2    ) = }"""    )  # = 8
  	print(f"""Cone: {vol_cone(2       ,			2    ) = }"""    )  # ~= 1.33
  	print(f"""Right Circular Cone: {vol_right_circ_cone(2       ,			2    ) = }"""    )  # ~= 8.38
  	print(f"""Prism: {vol_prism(2       ,			2    ) = }"""    )  # = 4
  	print(f"""Pyramid: {vol_pyramid(2       ,			2    ) = }"""    )  # ~= 1.33
  	print(f"""Sphere: {vol_sphere(2    ) = }"""    )  # ~= 33.5
  	print(f"""Hemisphere: {vol_hemisphere(2    ) = }"""    )  # ~= 16.75
  	print(f"""Circular Cylinder: {vol_circular_cylinder(2       ,			2    ) = }"""    )  # ~= 25.1
  	print(f"""Torus: {vol_torus(2       ,			2    ) = }"""    )  # ~= 157.9
  	print(f"""Conical Frustum: {vol_conical_frustum(2       ,			2       ,			4    ) = }"""    )  # ~= 58.6
  	print(f"""Spherical cap: {vol_spherical_cap(1       ,			2    ) = }"""    )  # ~= 5.24
  	print(f"""Spheres intersetion: {vol_spheres_intersect(2       ,			2       ,			1    ) = }"""    )  # ~= 21.21
  	print(f"""Spheres union: {vol_spheres_union(2       ,			2       ,			1    ) = }"""    )  # ~= 45.81
  	print(
  	    f"""Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1       ,			2       ,			3    ) = }"""    )  # ~= 28.3
  
  
  if __name__ == "__main__":
  				main()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
  	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
  	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )
  
  	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
  	# Encoder
  	for layer_index in range(config.num_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_attention_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  		A							  =      tax_mlp_layer_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_encoder_rel_embedding
  
  	# Assigning
  	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  	A							  =      tax_encoder_norm
  
  	# Decoder
  	for layer_index in range(config.num_decoder_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
  		    """scale"""
  		]
  
  		# Encoder-Decoder-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
  		    """kernel"""
  		]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
  		# MLP
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_pre_attention_layer_norm
  
  		A							  =      tax_enc_dec_attention_key
  		A							  =      tax_enc_dec_attention_out
  		A							  =      tax_enc_dec_attention_query
  		A							  =      tax_enc_dec_attention_value
  
  		A							  =      tax_cross_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  
  		A							  =      txa_mlp_layer_norm
  
  	# Decoder Normalization
  	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  	A							  =      txa_decoder_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_decoder_rel_embedding
  
  	# Token Embeddings
  	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  	A							  =      txa_token_embeddings
  
  	# LM Head (only in v1.1 checkpoints)
  	if "logits_dense" in tax_model["target"]["decoder"]:
  		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
  	flax_model.save_pretrained(UpperCAmelCase    )
  	print("""T5X Model was sucessfully converted!"""    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Tuple				    =       argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
  				)
  				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
  				parser.add_argument(
  				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  				)
  				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
  				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  