
from __future__ import annotations

from math import pi, pow





def 					_a      (		lowerCamelCase							):
 if side_length < 0:
  raise ValueError("""vol_cube() only accepts non-negative values"""							)
 return pow(lowerCamelCase,			3							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 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(lowerCamelCase,			2							) * (3 * radius - height)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 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(lowerCamelCase,			lowerCamelCase							)							)

 lowerCamelCase	:			int           =   (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 lowerCamelCase	:			Any           =   (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(lowerCamelCase,			lowerCamelCase							) + vol_spherical_cap(lowerCamelCase,			lowerCamelCase							)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):

 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(lowerCamelCase,			lowerCamelCase							)							)

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





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





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





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





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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 # 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(lowerCamelCase,			2							) - pow(lowerCamelCase,			2							)) * height





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 # 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(lowerCamelCase,			2							) + pow(lowerCamelCase,			2							) + radius_a * radius_a)
 )





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





def 					_a      (		):
 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 json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowerCamelCase           ={
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 for attribute in key.split("""."""							):
  lowerCamelCase	:			List[str]           =   getattr(lowerCamelCase,			lowerCamelCase							)

 if weight_type is not None:
  lowerCamelCase	:			str           =   getattr(lowerCamelCase,			lowerCamelCase							).shape
 else:
  lowerCamelCase	:			Union[str, Any]           =   hf_pointer.shape

 assert hf_shape == value.shape, (
     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":
  lowerCamelCase	:			Dict           =   value
 elif weight_type == "weight_g":
  lowerCamelCase	:			List[str]           =   value
 elif weight_type == "weight_v":
  lowerCamelCase	:			Any           =   value
 elif weight_type == "bias":
  lowerCamelCase	:			Union[str, Any]           =   value
 else:
  lowerCamelCase	:			int           =   value

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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   []
 lowerCamelCase	:			Union[str, Any]           =   fairseq_model.state_dict()

 lowerCamelCase	:			Optional[int]           =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase	:			str           =   False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			hf_model.config.feat_extract_norm == """group""",			)
   lowerCamelCase	:			int           =   True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase	:			str           =   """sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

    if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
     lowerCamelCase	:			int           =   True
     if "*" in mapped_key:
      lowerCamelCase	:			Any           =   name.split(lowerCamelCase							)[0].split("""."""							)[-2]
      if not layer_index.isnumeric():
       continue
      lowerCamelCase	:			Union[str, Any]           =   mapped_key.replace("""*""",			lowerCamelCase							)
     if "weight_g" in name:
      lowerCamelCase	:			Any           =   """weight_g"""
     elif "weight_v" in name:
      lowerCamelCase	:			int           =   """weight_v"""
     elif "weight" in name:
      lowerCamelCase	:			int           =   """weight"""
     elif "bias" in name:
      lowerCamelCase	:			int           =   """bias"""
     else:
      lowerCamelCase	:			Dict           =   None
     set_recursively(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)
    continue
  if not is_used:
   unused_weights.append(lowerCamelCase							)

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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   full_name.split("""conv_layers."""							)[-1]
 lowerCamelCase	:			Optional[int]           =   name.split("""."""							)
 lowerCamelCase	:			str           =   int(items[0]							)
 lowerCamelCase	:			Optional[int]           =   int(items[1]							)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   lowerCamelCase	:			Union[str, Any]           =   value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   lowerCamelCase	:			int           =   value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   lowerCamelCase	:			Dict           =   value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   lowerCamelCase	:			Tuple           =   value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
 else:
  unused_weights.append(lowerCamelCase							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			List[Any]           =   SEWDConfig()
 if is_finetuned:
  lowerCamelCase	:			int           =   model.wav_encoder.wav_model.cfg
 else:
  lowerCamelCase	:			Optional[Any]           =   model.cfg

 lowerCamelCase	:			List[str]           =   fs_config.conv_bias
 lowerCamelCase	:			Tuple           =   eval(fs_config.conv_feature_layers							)
 lowerCamelCase	:			List[str]           =   [x[0] for x in conv_layers]
 lowerCamelCase	:			List[Any]           =   [x[1] for x in conv_layers]
 lowerCamelCase	:			int           =   [x[2] for x in conv_layers]
 lowerCamelCase	:			List[str]           =   """gelu"""
 lowerCamelCase	:			Union[str, Any]           =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
 lowerCamelCase	:			List[str]           =   0.0
 lowerCamelCase	:			Optional[int]           =   fs_config.activation_fn.name
 lowerCamelCase	:			Union[str, Any]           =   fs_config.encoder_embed_dim
 lowerCamelCase	:			Tuple           =   0.0_2
 lowerCamelCase	:			str           =   fs_config.encoder_ffn_embed_dim
 lowerCamelCase	:			Dict           =   1e-5
 lowerCamelCase	:			str           =   fs_config.encoder_layerdrop
 lowerCamelCase	:			Optional[Any]           =   fs_config.encoder_attention_heads
 lowerCamelCase	:			List[Any]           =   fs_config.conv_pos_groups
 lowerCamelCase	:			Optional[int]           =   fs_config.conv_pos
 lowerCamelCase	:			Dict           =   len(lowerCamelCase							)
 lowerCamelCase	:			List[Any]           =   fs_config.encoder_layers
 lowerCamelCase	:			Optional[int]           =   fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 lowerCamelCase	:			Optional[int]           =   fs_config.max_position_embeddings
 lowerCamelCase	:			List[str]           =   fs_config.position_buckets
 lowerCamelCase	:			List[Any]           =   fs_config.share_att_key
 lowerCamelCase	:			Dict           =   fs_config.relative_attention
 lowerCamelCase	:			List[Any]           =   fs_config.position_biased_input
 lowerCamelCase	:			Dict           =   tuple(fs_config.pos_att_type.split("""|"""							)							)
 lowerCamelCase	:			List[str]           =   fs_config.norm_rel_ebd

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  lowerCamelCase	:			Tuple           =   model.cfg
  lowerCamelCase	:			Any           =   fs_config.final_dropout
  lowerCamelCase	:			Tuple           =   fs_config.layerdrop
 lowerCamelCase	:			Dict           =   fs_config.activation_dropout
 lowerCamelCase	:			Union[str, Any]           =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 lowerCamelCase	:			List[str]           =   fs_config.attention_dropout
 lowerCamelCase	:			Tuple           =   fs_config.dropout_input
 lowerCamelCase	:			Dict           =   fs_config.dropout
 lowerCamelCase	:			Dict           =   fs_config.mask_channel_length
 lowerCamelCase	:			str           =   fs_config.mask_channel_prob
 lowerCamelCase	:			Optional[Any]           =   fs_config.mask_length
 lowerCamelCase	:			List[str]           =   fs_config.mask_prob

 lowerCamelCase	:			Dict           =   """Wav2Vec2FeatureExtractor"""
 lowerCamelCase	:			int           =   """Wav2Vec2CTCTokenizer"""

 return config





@torch.no_grad()
def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=True							):

 if is_finetuned:
  lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			Any           =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path],			arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
 else:
  lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			List[str]           =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

 if config_path is not None:
  lowerCamelCase	:			List[str]           =   SEWDConfig.from_pretrained(lowerCamelCase							)
 else:
  lowerCamelCase	:			Optional[int]           =   convert_config(model[0],			lowerCamelCase							)
 lowerCamelCase	:			Dict           =   model[0].eval()

 lowerCamelCase	:			Dict           =   True if config.feat_extract_norm == """layer""" else False
 lowerCamelCase	:			List[str]           =   WavaVecaFeatureExtractor(
     feature_size=1,			sampling_rate=1_6000,			padding_value=0,			do_normalize=lowerCamelCase,			return_attention_mask=lowerCamelCase,			)

 if is_finetuned:
  if dict_path:
   lowerCamelCase	:			List[Any]           =   Dictionary.load(lowerCamelCase							)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase	:			Union[str, Any]           =   target_dict.pad_index
   lowerCamelCase	:			List[Any]           =   target_dict.bos_index
   lowerCamelCase	:			List[str]           =   target_dict.pad_index
   lowerCamelCase	:			Optional[Any]           =   target_dict.bos_index
   lowerCamelCase	:			int           =   target_dict.eos_index
   lowerCamelCase	:			Optional[int]           =   len(target_dict.symbols							)
   lowerCamelCase	:			str           =   os.path.join(lowerCamelCase,			"""vocab.json"""							)
   if not os.path.isdir(lowerCamelCase							):
    logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase							)							)
    return
   os.makedirs(lowerCamelCase,			exist_ok=lowerCamelCase							)
   with open(lowerCamelCase,			"""w""",			encoding="""utf-8"""							) as vocab_handle:
    json.dump(target_dict.indices,			lowerCamelCase							)
   lowerCamelCase	:			Union[str, Any]           =   WavaVecaCTCTokenizer(
       lowerCamelCase,			unk_token=target_dict.unk_word,			pad_token=target_dict.pad_word,			bos_token=target_dict.bos_word,			eos_token=target_dict.eos_word,			word_delimiter_token="""|""",			do_lower_case=lowerCamelCase,			)
   lowerCamelCase	:			Dict           =   WavaVecaProcessor(feature_extractor=lowerCamelCase,			tokenizer=lowerCamelCase							)
   processor.save_pretrained(lowerCamelCase							)

  lowerCamelCase	:			Union[str, Any]           =   SEWDForCTC(lowerCamelCase							)
 else:
  lowerCamelCase	:			Dict           =   SEWDModel(lowerCamelCase							)
  feature_extractor.save_pretrained(lowerCamelCase							)

 recursively_load_weights(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 hf_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase           =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(
          """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      _lowerCamelCase           =parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )




