
from __future__ import annotations

from math import pi, pow




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




def 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float						,   a__	:	float  )		->						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__  )  )

       UpperCamelCase_											=							(
           (radius_a - radius_a + centers_distance)
           * (radius_a + radius_a - centers_distance)
           / (2 * centers_distance)
       )
       UpperCamelCase_											=							(
           (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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    a__	:	float						,   a__	:	float  )		->						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 					lowerCamelCase__					(    )		->						List[Any]:
       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 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       for attribute in key.split("""."""  ):
              UpperCamelCase_											=							getattr(a__						,   a__  )

       if weight_type is not None:
              UpperCamelCase_											=							getattr(a__						,   a__  ).shape
       else:
              UpperCamelCase_											=							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":
              UpperCamelCase_											=							value
       elif weight_type == "weight_g":
              UpperCamelCase_											=							value
       elif weight_type == "weight_v":
              UpperCamelCase_											=							value
       elif weight_type == "bias":
              UpperCamelCase_											=							value
       else:
              UpperCamelCase_											=							value

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




def 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

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

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

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""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]:
                                   UpperCamelCase_											=							True
                                   if "*" in mapped_key:
                                          UpperCamelCase_											=							name.split(a__  )[0].split("""."""  )[-2]
                                          UpperCamelCase_											=							mapped_key.replace("""*"""						,   a__  )
                                   if "weight_g" in name:
                                          UpperCamelCase_											=							"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCamelCase_											=							"""weight_v"""
                                   elif "bias" in name:
                                          UpperCamelCase_											=							"""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCamelCase_											=							"""weight"""
                                   else:
                                          UpperCamelCase_											=							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 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							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.'''  )
       UpperCamelCase_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

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

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




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

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

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




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

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

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

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

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

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

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

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

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

              UpperCamelCase_											=							torch.argmax(a__						,   dim=-1  )
              UpperCamelCase_											=							processor.batch_decode(a__  )

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

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							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
      )



