
from __future__ import annotations

from math import pi, pow

def    UpperCamelCase_ (	a_		)     ->float:
		if side_length < 0:
				raise ValueError("vol_cube() only accepts non-negative values"		)
		return pow(a_  ,				3		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		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(a_  ,				2		) * (3 * radius - height)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->float:
		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(a_  ,				a_		)		)

		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(a_  ,				a_		) + vol_spherical_cap(a_  ,				a_		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->float:

		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(a_  ,				a_		)		)

		return (
		    vol_sphere(a_		)
		    + vol_sphere(a_		)
		    - vol_spheres_intersect(a_  ,				a_  ,				a_		)
		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->float:
		if width < 0 or height < 0 or length < 0:
				raise ValueError("vol_cuboid() only accepts non-negative values"		)
		return float(width * height * length		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		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    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		if height < 0 or radius < 0:
				raise ValueError("vol_right_circ_cone() only accepts non-negative values"		)
		return pi * pow(a_  ,				2		) * height / 3.0

def    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		if height < 0 or area_of_base < 0:
				raise ValueError("vol_prism() only accepts non-negative values"		)
		return float(area_of_base * height		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		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    UpperCamelCase_ (	a_		)     ->float:
		if radius < 0:
				raise ValueError("vol_sphere() only accepts non-negative values"		)
		# Volume is 4/3 * pi * radius cubed
		return 4 / 3 * pi * pow(a_  ,				3		)

def    UpperCamelCase_ (	a_		)     ->float:
		if radius < 0:
				raise ValueError("vol_hemisphere() only accepts non-negative values"		)
		# Volume is radius cubed * pi * 2/3
		return pow(a_  ,				3		) * pi * 2 / 3

def    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		if height < 0 or radius < 0:
				raise ValueError("vol_circular_cylinder() only accepts non-negative values"		)
		# Volume is radius squared * height * pi
		return pow(a_  ,				2		) * height * pi

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->float:
		# Volume - (outer_radius squared - inner_radius squared) * pi * height
		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(a_  ,				2		) - pow(a_  ,				2		)) * height

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->float:
		# Volume is 1/3 * pi * height *
		#           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
		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(a_  ,				2		) + pow(a_  ,				2		) + radius_a * radius_a)
		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->float:
		if torus_radius < 0 or tube_radius < 0:
				raise ValueError("vol_torus() only accepts non-negative values"		)
		return 2 * pow(a_  ,				2		) * torus_radius * pow(a_  ,				2		)

def    UpperCamelCase_ (	)     ->Dict:
		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()






import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__a							  =						logging.get_logger(__name__)

__a							  =						{
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
__a							  =						[
    """lm_head""",
]

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_  ,				a_		)     ->Dict:
		for attribute in key.split("."		):
				A				=getattr(a_  ,				a_		)

		if weight_type is not None:
				A				=getattr(a_  ,				a_		).shape
		else:
				A				=hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''		)

		if weight_type == "weight":
				A				=value
		elif weight_type == "weight_g":
				A				=value
		elif weight_type == "weight_v":
				A				=value
		elif weight_type == "bias":
				A				=value
		else:
				A				=value

		logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->Union[str, Any]:
		A				=[]
		A				=fairseq_model.state_dict()

		if not is_headless:
				A				=hf_model.dataavec_audio.feature_extractor
				A				=hf_model.dataavec_audio.encoder.pos_conv_embed

		else:
				A				=hf_model.feature_extractor
				A				=hf_model.encoder.pos_conv_embed

		for name, value in fairseq_dict.items():
				A				=False
				if "conv_layers" in name:
						load_conv_layer(
						    a_  ,				a_  ,				a_  ,				a_  ,				)
						A				=True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    a_  ,				a_  ,				a_  ,				a_  ,				)
						A				=True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										A				="data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
										A				=True
										if "*" in mapped_key:
												A				=name.split(a_		)[0].split("."		)[-2]
												A				=mapped_key.replace("*"  ,				a_		)
										if "weight_g" in name:
												A				="weight_g"
										elif "weight_v" in name:
												A				="weight_v"
										elif "bias" in name:
												A				="bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A				="weight"
										else:
												A				=None
										set_recursively(a_  ,				a_  ,				a_  ,				a_  ,				a_		)
								continue
				if not is_used:
						unused_weights.append(a_		)

		logger.warning(f'''Unused weights: {unused_weights}'''		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->List[str]:
		A				=path.split("."		)
		return reduce(a_  ,				a_  ,				a_		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=access_by_string(a_  ,				a_		)
		A				=hf_weight.data

		if fsq_value.shape != hf_value.shape:
				raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''		)
		A				=fsq_value
		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->List[Any]:
		A				=full_name.split("conv_layers."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

		A				=name.split("."		)[-1]
		if type_id == 0:
				A				="conv"
		elif type_id == 2:
				A				="layer_norm"
		else:
				unused_weights.append(a_		)
				return

		set_weights(a_  ,				a_  ,				a_  ,				f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=full_name.split("pos_conv."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

		A				=name.split("."		)[-1]
		if type_id != 0:
				unused_weights.append(a_		)
				return
		else:
				A				="conv"

		set_weights(a_  ,				a_  ,				a_  ,				f'''layers.{layer_id}.{layer_type}.{weight_type}'''		)

@torch.no_grad()
def    UpperCamelCase_ (	a_  ,				a_  ,				a_=None  ,				a_=None  ,				a_=True		)     ->Optional[int]:
		if config_path is not None:
				A				=DataaVecAudioConfig.from_pretrained(a_		)
		else:
				A				=DataaVecAudioConfig()

		if not is_finetuned:
				# Modify final_proj layer name
				A				=DataaVecAudioModel(a_		)
				A				=os.path.dirname(a_		)

				A				=torch.load(a_		)
				A				=state_dict["model"].pop("final_proj.0.weight"		)
				A				=state_dict["model"].pop("final_proj.0.bias"		)
				A				=os.path.join(a_  ,				"converted.pt"		)
				torch.save(a_  ,				a_		)
		else:
				A				=DataaVecAudioForCTC(a_		)
				A				=checkpoint_path

		def load_dataavec(a_		):
				A			,    A			,    A				=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
				return model[0].eval()

		A				=load_dataavec(a_		)

		recursively_load_weights(a_  ,				a_  ,				not is_finetuned		)

		A				=WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"		)

		A				=load_dataset("patrickvonplaten/librispeech_asr_dummy"  ,				"clean"  ,				split="validation"		)
		A				=[x["array"] for x in ds[:4]["audio"]]

		A				=processor(a_  ,				return_tensors="pt"  ,				padding=a_		)

		A				=inputs.input_values
		A				=inputs.attention_mask
		#    input_values = inputs.input_values[:, :-1]
		#    attention_mask = inputs.attention_mask[:, :-1]

		hf_wavavec.eval()
		model.eval()
		if is_finetuned:
				A				=model(source=a_  ,				padding_mask=(1 - attention_mask)  ,				mask=a_  ,				features_only=a_		)[
				    "encoder_out"
				].transpose(0  ,				1		)
				A				=hf_wavavec(a_  ,				attention_mask=a_		)["logits"]

				A				=torch.argmax(a_  ,				dim=-1		)
				A				=processor.batch_decode(a_		)

				print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''		)
		else:
				A				=model(source=a_  ,				padding_mask=(1 - attention_mask)  ,				mask=a_  ,				features_only=a_		)[
				    "layer_results"
				][-1][0].transpose(0  ,				1		)
				A				=hf_wavavec(a_  ,				attention_mask=a_		)["last_hidden_state"]

		print(our_output.shape  ,				their_output.shape		)
		A				=torch.max(torch.abs(our_output - their_output		)		).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
		A				=torch.allclose(a_  ,				a_  ,				atol=1E-3		)
		print("Do both models output the same tensors?"  ,				"🔥" if success else "💩"		)
		if not success:
				raise Exception("Something went wRoNg"		)

		hf_wavavec.save_pretrained(a_		)

		if is_finetuned:
				processor.save_pretrained(a_		)
		else:
				processor.feature_extractor.save_pretrained(a_		)


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	__a							  =						parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)




