



'''simple docstring'''



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a__  :	Any								=	logging.get_logger(__name__)

a__  :	Optional[int]								=	{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
a__  :	int								=	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]







def _lowercase      (     __A							,__A							,__A							,__A							,__A				):



 '''simple docstring'''



 for attribute in key.split("""."""				):
  __UpperCamelCase 					=     getattr(_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE				)

 if weight_type is not None:
  __UpperCamelCase 					=     getattr(_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE				).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 _lowercase      (     __A							,__A				):



 '''simple docstring'''



 __UpperCamelCase 					=     []
 __UpperCamelCase 					=     fairseq_model.state_dict()

 __UpperCamelCase 					=     hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  __UpperCamelCase 					=     False
  if "conv_layers" in name:
   load_conv_layer(
       _SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE							,hf_model.config.feat_extract_norm == """group"""							,)
   __UpperCamelCase 					=     True
  else:
   for key, mapped_key in MAPPING.items():
    __UpperCamelCase 					=     'unispeech_sat.' + 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]:
     if "layer_norm_for_extract" in name and (".".join(name.split("""."""				)[:-1]				) != key):
      # special case since naming is very similar
      continue
     __UpperCamelCase 					=     True
     if "*" in mapped_key:
      __UpperCamelCase 					=     name.split(_SCREAMING_SNAKE_CASE				)[0].split("""."""				)[-2]
      __UpperCamelCase 					=     mapped_key.replace("""*"""							,_SCREAMING_SNAKE_CASE				)
     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(_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE				)
    continue
  if not is_used:
   unused_weights.append(_SCREAMING_SNAKE_CASE				)

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







def _lowercase      (     __A							,__A							,__A							,__A							,__A				):



 '''simple docstring'''



 __UpperCamelCase 					=     full_name.split("""conv_layers."""				)[-1]
 __UpperCamelCase 					=     name.split("""."""				)
 __UpperCamelCase 					=     int(items[0]				)
 __UpperCamelCase 					=     int(items[1]				)

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."				)
   __UpperCamelCase 					=     value
   logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."				)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."				)
   __UpperCamelCase 					=     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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."				)
   __UpperCamelCase 					=     value
   logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."				)
   __UpperCamelCase 					=     value
   logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
 else:
  unused_weights.append(_SCREAMING_SNAKE_CASE				)







@torch.no_grad()
def _lowercase      (     __A							,__A							,__A=None							,__A=None							,__A=True				):



 '''simple docstring'''



 if config_path is not None:
  __UpperCamelCase 					=     UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
 else:
  __UpperCamelCase 					=     UniSpeechSatConfig()

 __UpperCamelCase 					=     ''

 if is_finetuned:
  __UpperCamelCase 					=     UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE				)
 else:
  __UpperCamelCase 					=     UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE				)

 __UpperCamelCase 					=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]							,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
 __UpperCamelCase 					=     model[0].eval()

 recursively_load_weights(_SCREAMING_SNAKE_CASE							,_SCREAMING_SNAKE_CASE				)

 hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
 a__  :	str								=	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__  :	List[Any]								=	parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )






"""simple docstring"""
from math import pi, sqrt, tan







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('surface_area_cube() only accepts non-negative values'   )
  return 6 * side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or breadth < 0 or height < 0:
    raise ValueError('surface_area_cuboid() only accepts non-negative values'   )
  return 2 * ((length * breadth) + (breadth * height) + (length * height))







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_sphere() only accepts non-negative values'   )
  return 4 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_hemisphere() only accepts non-negative values'   )
  return 3 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cone() only accepts non-negative values'   )
  return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_a < 0 or radius_a < 0 or height < 0:
    raise ValueError(
        'surface_area_conical_frustum() only accepts non-negative values'   )
  a__:     List[Any]										=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
  return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cylinder() only accepts non-negative values'   )
  return 2 * pi * radius * (height + radius)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if torus_radius < 0 or tube_radius < 0:
    raise ValueError('surface_area_torus() only accepts non-negative values'   )
  if torus_radius < tube_radius:
    raise ValueError(
        'surface_area_torus() does not support spindle or self intersecting tori'   )
  return 4 * pow(_SCREAMING_SNAKE_CASE		,     2   ) * torus_radius * tube_radius







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or width < 0:
    raise ValueError('area_rectangle() only accepts non-negative values'   )
  return length * width







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('area_square() only accepts non-negative values'   )
  return side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_triangle() only accepts non-negative values'   )
  return (base * height) / 2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if sidea < 0 or sidea < 0 or sidea < 0:
    raise ValueError('area_triangle_three_sides() only accepts non-negative values'   )
  elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
    raise ValueError('Given three sides do not form a triangle'   )
  a__:     int										=	(sidea + sidea + sidea) / 2
  a__:     Tuple										=	sqrt(
      semi_perimeter
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)   )
  return area







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_parallelogram() only accepts non-negative values'   )
  return base * height







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if basea < 0 or basea < 0 or height < 0:
    raise ValueError('area_trapezium() only accepts non-negative values'   )
  return 1 / 2 * (basea + basea) * height







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('area_circle() only accepts non-negative values'   )
  return pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_x < 0 or radius_y < 0:
    raise ValueError('area_ellipse() only accepts non-negative values'   )
  return pi * radius_x * radius_y







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if diagonal_a < 0 or diagonal_a < 0:
    raise ValueError('area_rhombus() only accepts non-negative values'   )
  return 1 / 2 * diagonal_a * diagonal_a







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) or sides < 3:
    raise ValueError(
        'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'   )
  elif length < 0:
    raise ValueError(
        'area_reg_polygon() only accepts non-negative values as \
length of a side'   )
  return (sides * length**2) / (4 * tan(pi / sides   ))
  return (sides * length**2) / (4 * tan(pi / sides   ))


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

 print('[DEMO] Areas of various geometric shapes: \n')
 print(f"Rectangle: {area_rectangle(10, 20) = }")
 print(f"Square: {area_square(10) = }")
 print(f"Triangle: {area_triangle(10, 10) = }")
 print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
 print(f"Parallelogram: {area_parallelogram(10, 20) = }")
 print(f"Rhombus: {area_rhombus(10, 20) = }")
 print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
 print(f"Circle: {area_circle(20) = }")
 print(f"Ellipse: {area_ellipse(10, 20) = }")
 print('\nSurface Areas of various geometric shapes: \n')
 print(f"Cube: {surface_area_cube(20) = }")
 print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
 print(f"Sphere: {surface_area_sphere(20) = }")
 print(f"Hemisphere: {surface_area_hemisphere(20) = }")
 print(f"Cone: {surface_area_cone(10, 20) = }")
 print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
 print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
 print(f"Torus: {surface_area_torus(20, 10) = }")
 print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
 print(f"Square: {area_reg_polygon(4, 10) = }")
 print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")


