


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()
snake_case_  :	Optional[Any]							  =							logging.get_logger(__name__)

snake_case_  :	List[Any]							  =							{
    '''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   lowerCamelCase(      a__		,a__		,a__		,a__		,a__):
				for attribute in key.split('''.'''):
								_SCREAMING_SNAKE_CASE	     =getattr(a__		,a__)

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

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







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =[]
				_SCREAMING_SNAKE_CASE	     =fairseq_model.state_dict()

				_SCREAMING_SNAKE_CASE	     =hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE	     =False
								if "conv_layers" in name:
												load_conv_layer(
												    a__		,a__		,a__		,a__		,hf_model.config.feat_extract_norm == '''group'''		,)
												_SCREAMING_SNAKE_CASE	     =True
								else:
												for key, mapped_key in MAPPING.items():
																_SCREAMING_SNAKE_CASE	     ='''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]:
																				_SCREAMING_SNAKE_CASE	     =True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE	     =name.split(a__)[0].split('''.''')[-2]
																								if not layer_index.isnumeric():
																												continue
																								_SCREAMING_SNAKE_CASE	     =mapped_key.replace('''*'''		,a__)
																				if "weight_g" in name:
																								_SCREAMING_SNAKE_CASE	     ='''weight_g'''
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE	     ='''weight_v'''
																				elif "weight" in name:
																								_SCREAMING_SNAKE_CASE	     ='''weight'''
																				elif "bias" in name:
																								_SCREAMING_SNAKE_CASE	     ='''bias'''
																				else:
																								_SCREAMING_SNAKE_CASE	     =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__		,a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =full_name.split('''conv_layers.''')[-1]
				_SCREAMING_SNAKE_CASE	     =name.split('''.''')
				_SCREAMING_SNAKE_CASE	     =int(items[0])
				_SCREAMING_SNAKE_CASE	     =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."
												)
												_SCREAMING_SNAKE_CASE	     =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."
												)
												_SCREAMING_SNAKE_CASE	     =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."
												)
												_SCREAMING_SNAKE_CASE	     =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."
												)
												_SCREAMING_SNAKE_CASE	     =value
												logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
				else:
								unused_weights.append(a__)







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =SEWDConfig()
				if is_finetuned:
								_SCREAMING_SNAKE_CASE	     =model.wav_encoder.wav_model.cfg
				else:
								_SCREAMING_SNAKE_CASE	     =model.cfg

				_SCREAMING_SNAKE_CASE	     =fs_config.conv_bias
				_SCREAMING_SNAKE_CASE	     =eval(fs_config.conv_feature_layers)
				_SCREAMING_SNAKE_CASE	     =[x[0] for x in conv_layers]
				_SCREAMING_SNAKE_CASE	     =[x[1] for x in conv_layers]
				_SCREAMING_SNAKE_CASE	     =[x[2] for x in conv_layers]
				_SCREAMING_SNAKE_CASE	     ='''gelu'''
				_SCREAMING_SNAKE_CASE	     ='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				_SCREAMING_SNAKE_CASE	     =0.0
				_SCREAMING_SNAKE_CASE	     =fs_config.activation_fn.name
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_embed_dim
				_SCREAMING_SNAKE_CASE	     =0.02
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_ffn_embed_dim
				_SCREAMING_SNAKE_CASE	     =1e-5
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_layerdrop
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_attention_heads
				_SCREAMING_SNAKE_CASE	     =fs_config.conv_pos_groups
				_SCREAMING_SNAKE_CASE	     =fs_config.conv_pos
				_SCREAMING_SNAKE_CASE	     =len(a__)
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_layers
				_SCREAMING_SNAKE_CASE	     =fs_config.squeeze_factor
				# DeBERTa-specific parameters:
				_SCREAMING_SNAKE_CASE	     =fs_config.max_position_embeddings
				_SCREAMING_SNAKE_CASE	     =fs_config.position_buckets
				_SCREAMING_SNAKE_CASE	     =fs_config.share_att_key
				_SCREAMING_SNAKE_CASE	     =fs_config.relative_attention
				_SCREAMING_SNAKE_CASE	     =fs_config.position_biased_input
				_SCREAMING_SNAKE_CASE	     =tuple(fs_config.pos_att_type.split('''|'''))
				_SCREAMING_SNAKE_CASE	     =fs_config.norm_rel_ebd

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								_SCREAMING_SNAKE_CASE	     =model.cfg
								_SCREAMING_SNAKE_CASE	     =fs_config.final_dropout
								_SCREAMING_SNAKE_CASE	     =fs_config.layerdrop
				_SCREAMING_SNAKE_CASE	     =fs_config.activation_dropout
				_SCREAMING_SNAKE_CASE	     =fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				_SCREAMING_SNAKE_CASE	     =fs_config.attention_dropout
				_SCREAMING_SNAKE_CASE	     =fs_config.dropout_input
				_SCREAMING_SNAKE_CASE	     =fs_config.dropout
				_SCREAMING_SNAKE_CASE	     =fs_config.mask_channel_length
				_SCREAMING_SNAKE_CASE	     =fs_config.mask_channel_prob
				_SCREAMING_SNAKE_CASE	     =fs_config.mask_length
				_SCREAMING_SNAKE_CASE	     =fs_config.mask_prob

				_SCREAMING_SNAKE_CASE	     ='''Wav2Vec2FeatureExtractor'''
				_SCREAMING_SNAKE_CASE	     ='''Wav2Vec2CTCTokenizer'''

				return config







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

				if is_finetuned:
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]		,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1])})
				else:
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

				if config_path is not None:
								_SCREAMING_SNAKE_CASE	     =SEWDConfig.from_pretrained(a__)
				else:
								_SCREAMING_SNAKE_CASE	     =convert_config(model[0]		,a__)
				_SCREAMING_SNAKE_CASE	     =model[0].eval()

				_SCREAMING_SNAKE_CASE	     =True if config.feat_extract_norm == '''layer''' else False
				_SCREAMING_SNAKE_CASE	     =WavaVecaFeatureExtractor(
				    feature_size=1		,sampling_rate=1_6000		,padding_value=0		,do_normalize=a__		,return_attention_mask=a__		,)

				if is_finetuned:
								if dict_path:
												_SCREAMING_SNAKE_CASE	     =Dictionary.load(a__)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_SCREAMING_SNAKE_CASE	     =target_dict.pad_index
												_SCREAMING_SNAKE_CASE	     =target_dict.bos_index
												_SCREAMING_SNAKE_CASE	     =target_dict.pad_index
												_SCREAMING_SNAKE_CASE	     =target_dict.bos_index
												_SCREAMING_SNAKE_CASE	     =target_dict.eos_index
												_SCREAMING_SNAKE_CASE	     =len(target_dict.symbols)
												_SCREAMING_SNAKE_CASE	     =os.path.join(a__		,'''vocab.json''')
												if not os.path.isdir(a__):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__))
																return
												os.makedirs(a__		,exist_ok=a__)
												with open(a__		,'''w'''		,encoding='''utf-8''') as vocab_handle:
																json.dump(target_dict.indices		,a__)
												_SCREAMING_SNAKE_CASE	     =WavaVecaCTCTokenizer(
												    a__		,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=a__		,)
												_SCREAMING_SNAKE_CASE	     =WavaVecaProcessor(feature_extractor=a__		,tokenizer=a__)
												processor.save_pretrained(a__)

								_SCREAMING_SNAKE_CASE	     =SEWDForCTC(a__)
				else:
								_SCREAMING_SNAKE_CASE	     =SEWDModel(a__)
								feature_extractor.save_pretrained(a__)

				recursively_load_weights(a__		,a__		,a__)

				hf_model.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_  :	Dict							  =							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'''
							)
							snake_case_  :	Dict							  =							parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)





'''simple docstring'''







from __future__ import annotations

from math import pi, pow






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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(lowercase_						,							2  ) * (3 * radius - height)






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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(lowercase_						,							lowercase_  )  )

		_snake_case	       =       (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)
		_snake_case	       =       (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)

		return vol_spherical_cap(lowercase_						,							lowercase_  ) + vol_spherical_cap(lowercase_						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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(lowercase_						,							lowercase_  )  )

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






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_  ):



		'''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(lowercase_						,							3  )






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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(lowercase_						,							2  ) * height * pi






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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(lowercase_						,							2  ) - pow(lowercase_						,							2  )) * height






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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(lowercase_						,							2  ) + pow(lowercase_						,							2  ) + radius_a * radius_a)
		)






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	):



		'''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()





