


"""simple docstring"""






import pickle

import numpy as np
from matplotlib import pyplot as plt






class      A:

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=0.2					,				SCREAMING_SNAKE_CASE__=0.2   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	bp_numa
					_UpperCamelCase					:Optional[int]					     =	bp_numa
					_UpperCamelCase					:Tuple					     =	bp_numa
					_UpperCamelCase					:str					     =	conva_get[:2]
					_UpperCamelCase					:Any					     =	conva_get[2]
					_UpperCamelCase					:Optional[int]					     =	size_pa
					_UpperCamelCase					:Any					     =	rate_w
					_UpperCamelCase					:int					     =	rate_t
					_UpperCamelCase					:Dict					     =	[
					    np.mat(-1 * np.random.rand(self.conva[0]					,				self.conva[0]   ) + 0.5   )
					    for i in range(self.conva[1]   )
					]
					_UpperCamelCase					:int					     =	np.mat(-1 * np.random.rand(self.num_bpa					,				self.num_bpa   ) + 0.5   )
					_UpperCamelCase					:List[Any]					     =	np.mat(-1 * np.random.rand(self.num_bpa					,				self.num_bpa   ) + 0.5   )
					_UpperCamelCase					:List[str]					     =	-2 * np.random.rand(self.conva[1]   ) + 1
					_UpperCamelCase					:str					     =	-2 * np.random.rand(self.num_bpa   ) + 1
					_UpperCamelCase					:Union[str, Any]					     =	-2 * np.random.rand(self.num_bpa   ) + 1
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	{
					    '''num_bp1''': self.num_bpa,
					    '''num_bp2''': self.num_bpa,
					    '''num_bp3''': self.num_bpa,
					    '''conv1''': self.conva,
					    '''step_conv1''': self.step_conva,
					    '''size_pooling1''': self.size_poolinga,
					    '''rate_weight''': self.rate_weight,
					    '''rate_thre''': self.rate_thre,
					    '''w_conv1''': self.w_conva,
					    '''wkj''': self.wkj,
					    '''vji''': self.vji,
					    '''thre_conv1''': self.thre_conva,
					    '''thre_bp2''': self.thre_bpa,
					    '''thre_bp3''': self.thre_bpa,
					}
					with open(SCREAMING_SNAKE_CASE__					,				'''wb'''   ) as f:
								pickle.dump(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					print(f"Model saved： {save_path}"   )
		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					with open(SCREAMING_SNAKE_CASE__					,				'''rb'''   ) as f:
								_UpperCamelCase					:int					     =	pickle.load(SCREAMING_SNAKE_CASE__   )  # noqa: S301

					_UpperCamelCase					:Dict					     =	model_dic.get('''conv1'''   )
					conv_get.append(model_dic.get('''step_conv1'''   )   )
					_UpperCamelCase					:Dict					     =	model_dic.get('''size_pooling1'''   )
					_UpperCamelCase					:int					     =	model_dic.get('''num_bp1'''   )
					_UpperCamelCase					:Optional[Any]					     =	model_dic.get('''num_bp2'''   )
					_UpperCamelCase					:int					     =	model_dic.get('''num_bp3'''   )
					_UpperCamelCase					:Union[str, Any]					     =	model_dic.get('''rate_weight'''   )
					_UpperCamelCase					:Union[str, Any]					     =	model_dic.get('''rate_thre'''   )
					# create model instance
					_UpperCamelCase					:int					     =	CNN(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					# modify model parameter
					_UpperCamelCase					:Optional[int]					     =	model_dic.get('''w_conv1'''   )
					_UpperCamelCase					:Union[str, Any]					     =	model_dic.get('''wkj'''   )
					_UpperCamelCase					:int					     =	model_dic.get('''vji'''   )
					_UpperCamelCase					:Union[str, Any]					     =	model_dic.get('''thre_conv1'''   )
					_UpperCamelCase					:List[Any]					     =	model_dic.get('''thre_bp2'''   )
					_UpperCamelCase					:Optional[Any]					     =	model_dic.get('''thre_bp3'''   )
					return conv_ins
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					return 1 / (1 + np.exp(-1 * x   ))
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return round(SCREAMING_SNAKE_CASE__					,				3   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	convs[0]
					_UpperCamelCase					:Tuple					     =	convs[1]
					_UpperCamelCase					:str					     =	np.shape(SCREAMING_SNAKE_CASE__   )[0]
					# get the data slice of original image data, data_focus
					_UpperCamelCase					:List[Any]					     =	[]
					for i_focus in range(0					,				size_data - size_conv + 1					,				SCREAMING_SNAKE_CASE__   ):
								for j_focus in range(0					,				size_data - size_conv + 1					,				SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:Any					     =	data[
											    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
											]
											data_focus.append(SCREAMING_SNAKE_CASE__   )
        # calculate the feature map of every single kernel, and saved as list of matrix
					_UpperCamelCase					:Optional[int]					     =	[]
					_UpperCamelCase					:List[str]					     =	int((size_data - size_conv) / conv_step + 1   )
					for i_map in range(SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Union[str, Any]					     =	[]
								for i_focus in range(len(SCREAMING_SNAKE_CASE__   )   ):
											_UpperCamelCase					:List[str]					     =	(
											    np.sum(np.multiply(data_focus[i_focus]					,				w_convs[i_map]   )   )
											    - thre_convs[i_map]
											)
											featuremap.append(self.sig(SCREAMING_SNAKE_CASE__   )   )
								_UpperCamelCase					:Union[str, Any]					     =	np.asmatrix(SCREAMING_SNAKE_CASE__   ).reshape(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								data_featuremap.append(SCREAMING_SNAKE_CASE__   )

					# expanding the data slice to One dimenssion
					_UpperCamelCase					:Union[str, Any]					     =	[]
					for each_focus in data_focus:
								focusa_list.extend(self.Expand_Mat(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:str					     =	np.asarray(SCREAMING_SNAKE_CASE__   )
					return focus_list, data_featuremap
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__="average_pool"   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	len(featuremaps[0]   )
					_UpperCamelCase					:Optional[int]					     =	int(size_map / size_pooling   )
					_UpperCamelCase					:List[Any]					     =	[]
					for i_map in range(len(SCREAMING_SNAKE_CASE__   )   ):
								_UpperCamelCase					:Optional[int]					     =	featuremaps[i_map]
								_UpperCamelCase					:Union[str, Any]					     =	[]
								for i_focus in range(0					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
											for j_focus in range(0					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
														_UpperCamelCase					:Any					     =	feature_map[
														    i_focus : i_focus + size_pooling,
														    j_focus : j_focus + size_pooling,
														]
														if pooling_type == "average_pool":
																	# average pooling
																	map_pooled.append(np.average(SCREAMING_SNAKE_CASE__   )   )
														elif pooling_type == "max_pooling":
																	# max pooling
																	map_pooled.append(np.max(SCREAMING_SNAKE_CASE__   )   )
								_UpperCamelCase					:List[Any]					     =	np.asmatrix(SCREAMING_SNAKE_CASE__   ).reshape(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								featuremap_pooled.append(SCREAMING_SNAKE_CASE__   )
					return featuremap_pooled
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	[]
					for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
								_UpperCamelCase					:List[str]					     =	np.shape(data[i]   )
								_UpperCamelCase					:List[str]					     =	data[i].reshape(1					,				shapes[0] * shapes[1]   )
								_UpperCamelCase					:List[str]					     =	data_listed.getA().tolist()[0]
								data_expanded.extend(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	np.asarray(SCREAMING_SNAKE_CASE__   )
					return data_expanded
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.asarray(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	np.shape(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	data_mat.reshape(1					,				shapes[0] * shapes[1]   )
					return data_expanded
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	[]
					_UpperCamelCase					:List[Any]					     =	0
					for i_map in range(SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Tuple					     =	np.ones((size_map, size_map)   )
								for i in range(0					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
											for j in range(0					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
														_UpperCamelCase					:List[Any]					     =	pd_pool[
														    i_pool
														]
														_UpperCamelCase					:Tuple					     =	i_pool + 1
								_UpperCamelCase					:str					     =	np.multiply(
								    SCREAMING_SNAKE_CASE__					,				np.multiply(out_map[i_map]					,				(1 - out_map[i_map])   )   )
								pd_all.append(SCREAMING_SNAKE_CASE__   )
					return pd_all
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=bool   )      ->	Optional[Any]:

					"""simple docstring"""



					print('''----------------------Start Training-------------------------'''   )
					print((''' - - Shape: Train_Data  ''', np.shape(SCREAMING_SNAKE_CASE__   ))   )
					print((''' - - Shape: Teach_Data  ''', np.shape(SCREAMING_SNAKE_CASE__   ))   )
					_UpperCamelCase					:List[str]					     =	0
					_UpperCamelCase					:Dict					     =	[]
					_UpperCamelCase					:Tuple					     =	1_00_00
					while rp < n_repeat and mse >= error_accuracy:
								_UpperCamelCase					:str					     =	0
								print(f"-------------Learning Time {rp}--------------"   )
								for p in range(len(SCREAMING_SNAKE_CASE__   )   ):
											# print('------------Learning Image: %d--------------'%p)
											_UpperCamelCase					:Any					     =	np.asmatrix(datas_train[p]   )
											_UpperCamelCase					:List[str]					     =	np.asarray(datas_teach[p]   )
											_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	self.convolute(
											    SCREAMING_SNAKE_CASE__					,				self.conva					,				self.w_conva					,				self.thre_conva					,				conv_step=self.step_conva					,				)
											_UpperCamelCase					:Union[str, Any]					     =	self.pooling(SCREAMING_SNAKE_CASE__					,				self.size_poolinga   )
											_UpperCamelCase					:int					     =	np.shape(SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Dict					     =	self._expand(SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Union[str, Any]					     =	data_bp_input

											_UpperCamelCase					:int					     =	np.dot(SCREAMING_SNAKE_CASE__					,				self.vji.T   ) - self.thre_bpa
											_UpperCamelCase					:Tuple					     =	self.sig(SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:List[str]					     =	np.dot(SCREAMING_SNAKE_CASE__					,				self.wkj.T   ) - self.thre_bpa
											_UpperCamelCase					:List[Any]					     =	self.sig(SCREAMING_SNAKE_CASE__   )

											# --------------Model Leaning ------------------------
											# calculate error and gradient---------------
											_UpperCamelCase					:Dict					     =	np.multiply(
											    (data_teach - bp_outa)					,				np.multiply(SCREAMING_SNAKE_CASE__					,				(1 - bp_outa)   )   )
											_UpperCamelCase					:str					     =	np.multiply(
											    np.dot(SCREAMING_SNAKE_CASE__					,				self.wkj   )					,				np.multiply(SCREAMING_SNAKE_CASE__					,				(1 - bp_outa)   )   )
											_UpperCamelCase					:List[Any]					     =	np.dot(SCREAMING_SNAKE_CASE__					,				self.vji   )

											_UpperCamelCase					:str					     =	pd_i_all / (self.size_poolinga * self.size_poolinga)
											_UpperCamelCase					:Tuple					     =	pd_conva_pooled.T.getA().tolist()
											_UpperCamelCase					:Tuple					     =	self._calculate_gradient_from_pool(
											    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				shape_featuremapa[0]					,				shape_featuremapa[1]					,				self.size_poolinga					,				)
											# weight and threshold learning process---------
											# convolution layer
											for k_conv in range(self.conva[1]   ):
														_UpperCamelCase					:Optional[int]					     =	self._expand_mat(pd_conva_all[k_conv]   )
														_UpperCamelCase					:int					     =	self.rate_weight * np.dot(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

														_UpperCamelCase					:str					     =	self.w_conva[k_conv] + delta_w.reshape(
														    (self.conva[0], self.conva[0])   )

														_UpperCamelCase					:Dict					     =	(
														    self.thre_conva[k_conv]
														    - np.sum(pd_conva_all[k_conv]   ) * self.rate_thre
														)
											# all connected layer
											_UpperCamelCase					:List[Any]					     =	self.wkj + pd_k_all.T * bp_outa * self.rate_weight
											_UpperCamelCase					:Optional[int]					     =	self.vji + pd_j_all.T * bp_outa * self.rate_weight
											_UpperCamelCase					:Optional[int]					     =	self.thre_bpa - pd_k_all * self.rate_thre
											_UpperCamelCase					:Tuple					     =	self.thre_bpa - pd_j_all * self.rate_thre
											# calculate the sum error of all single image
											_UpperCamelCase					:Tuple					     =	np.sum(abs(data_teach - bp_outa   )   )
											error_count += errors
											# print('   ----Teach      ',data_teach)
											# print('   ----BP_output  ',bp_out3)
								_UpperCamelCase					:Optional[int]					     =	rp + 1
								_UpperCamelCase					:Tuple					     =	error_count / patterns
								all_mse.append(SCREAMING_SNAKE_CASE__   )

					def draw_error():
								_UpperCamelCase					:Dict					     =	[error_accuracy for i in range(int(n_repeat * 1.2   )   )]
								plt.plot(SCREAMING_SNAKE_CASE__					,				'''+-'''   )
								plt.plot(SCREAMING_SNAKE_CASE__					,				'''r--'''   )
								plt.xlabel('''Learning Times'''   )
								plt.ylabel('''All_mse'''   )
								plt.grid(SCREAMING_SNAKE_CASE__					,				alpha=0.5   )
								plt.show()

					print('''------------------Training Complished---------------------'''   )
					print((''' - - Training epoch: ''', rp, f"     - - Mse: {mse:.6f}")   )
					if draw_e:
								draw_error()
					return mse
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	[]
					print('''-------------------Start Testing-------------------------'''   )
					print((''' - - Shape: Test_Data  ''', np.shape(SCREAMING_SNAKE_CASE__   ))   )
					for p in range(len(SCREAMING_SNAKE_CASE__   )   ):
								_UpperCamelCase					:int					     =	np.asmatrix(datas_test[p]   )
								_UpperCamelCase     ,      _UpperCamelCase					:int					     =	self.convolute(
								    SCREAMING_SNAKE_CASE__					,				self.conva					,				self.w_conva					,				self.thre_conva					,				conv_step=self.step_conva					,				)
								_UpperCamelCase					:Union[str, Any]					     =	self.pooling(SCREAMING_SNAKE_CASE__					,				self.size_poolinga   )
								_UpperCamelCase					:int					     =	self._expand(SCREAMING_SNAKE_CASE__   )

								_UpperCamelCase					:List[Any]					     =	data_bp_input
								_UpperCamelCase					:int					     =	bp_outa * self.vji.T - self.thre_bpa
								_UpperCamelCase					:str					     =	self.sig(SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:str					     =	bp_outa * self.wkj.T - self.thre_bpa
								_UpperCamelCase					:int					     =	self.sig(SCREAMING_SNAKE_CASE__   )
								produce_out.extend(bp_outa.getA().tolist()   )
					_UpperCamelCase					:Union[str, Any]					     =	[list(map(self.do_round					,				SCREAMING_SNAKE_CASE__   )   ) for each in produce_out]
					return np.asarray(SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	np.asmatrix(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	self.convolute(
					    SCREAMING_SNAKE_CASE__					,				self.conva					,				self.w_conva					,				self.thre_conva					,				conv_step=self.step_conva					,				)
					_UpperCamelCase					:Optional[Any]					     =	self.pooling(SCREAMING_SNAKE_CASE__					,				self.size_poolinga   )

					return data_conveda, data_pooleda


if __name__ == "__main__":
					pass





"""simple docstring"""






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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__				:Optional[int]			   =	logging.get_logger(__name__)

UpperCamelCase__				:int			   =	{
    """post_extract_proj""": """feature_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.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_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		)

			if weight_type is not None:
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		).shape
			else:
						_UpperCamelCase					:Optional[int]					     =	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":
						_UpperCamelCase					:str					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Optional[Any]					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:str					     =	value
			else:
						_UpperCamelCase					:int					     =	value

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	[]
			_UpperCamelCase					:List[str]					     =	fairseq_model.state_dict()

			_UpperCamelCase					:str					     =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:Optional[Any]					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:Dict					     =	True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCamelCase					:Optional[int]					     =	'''sew.''' + 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]:
															_UpperCamelCase					:List[str]					     =	True
															if "*" in mapped_key:
																		_UpperCamelCase					:List[str]					     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
																		_UpperCamelCase					:Tuple					     =	mapped_key.replace('''*'''       ,					snake_case__		)
															if "weight_g" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight_g'''
															elif "weight_v" in name:
																		_UpperCamelCase					:Union[str, Any]					     =	'''weight_v'''
															elif "weight" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight'''
															elif "bias" in name:
																		_UpperCamelCase					:List[Any]					     =	'''bias'''
															else:
																		_UpperCamelCase					:List[Any]					     =	None
															set_recursively(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
												continue
						if not is_used:
									unused_weights.append(snake_case__		)

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Optional[int]					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	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."
									)
									_UpperCamelCase					:Optional[int]					     =	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."
									)
									_UpperCamelCase					:Optional[Any]					     =	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."
									)
									_UpperCamelCase					:int					     =	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."
									)
									_UpperCamelCase					:Dict					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(snake_case__		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:str					     =	SEWConfig()
			if is_finetuned:
						_UpperCamelCase					:Optional[int]					     =	model.wav_encoder.wav_model.cfg
			else:
						_UpperCamelCase					:Dict					     =	model.cfg

			_UpperCamelCase					:Dict					     =	fs_config.conv_bias
			_UpperCamelCase					:int					     =	eval(fs_config.conv_feature_layers		)
			_UpperCamelCase					:List[Any]					     =	[x[0] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[1] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[2] for x in conv_layers]
			_UpperCamelCase					:str					     =	'''gelu'''
			_UpperCamelCase					:Optional[int]					     =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			_UpperCamelCase					:List[Any]					     =	0.0
			_UpperCamelCase					:Optional[int]					     =	fs_config.activation_fn.name
			_UpperCamelCase					:str					     =	fs_config.encoder_embed_dim
			_UpperCamelCase					:Dict					     =	0.02
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_ffn_embed_dim
			_UpperCamelCase					:str					     =	1E-5
			_UpperCamelCase					:int					     =	fs_config.encoder_layerdrop
			_UpperCamelCase					:Union[str, Any]					     =	fs_config.encoder_attention_heads
			_UpperCamelCase					:List[str]					     =	fs_config.conv_pos_groups
			_UpperCamelCase					:List[Any]					     =	fs_config.conv_pos
			_UpperCamelCase					:List[str]					     =	len(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_layers
			_UpperCamelCase					:Optional[int]					     =	fs_config.squeeze_factor

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

			_UpperCamelCase					:List[Any]					     =	'''Wav2Vec2FeatureExtractor'''
			_UpperCamelCase					:Optional[Any]					     =	'''Wav2Vec2CTCTokenizer'''

			return config





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True		)					->  int:

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

			if config_path is not None:
						_UpperCamelCase					:Any					     =	SEWConfig.from_pretrained(snake_case__		)
			else:
						_UpperCamelCase					:List[str]					     =	convert_config(model[0]       ,					snake_case__		)
			_UpperCamelCase					:List[str]					     =	model[0].eval()

			_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
			_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
			    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=snake_case__       ,					return_attention_mask=snake_case__       ,					)

			if is_finetuned:
						if dict_path:
									_UpperCamelCase					:int					     =	Dictionary.load(snake_case__		)

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

						_UpperCamelCase					:Tuple					     =	SEWForCTC(snake_case__		)
			else:
						_UpperCamelCase					:str					     =	SEWModel(snake_case__		)
						feature_extractor.save_pretrained(snake_case__		)

			recursively_load_weights(snake_case__       ,					snake_case__       ,					snake_case__		)

			hf_model.save_pretrained(snake_case__		)


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

