




import pickle

import numpy as np
from matplotlib import pyplot as plt
class 			a__    :






						def __init__(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A=0.2		,			_A=0.2			):




											"""simple docstring"""



											__lowerCAmelCase				   =       bp_numa
											__lowerCAmelCase				   =       bp_numa
											__lowerCAmelCase				   =       bp_numa
											__lowerCAmelCase				   =       conva_get[:2]
											__lowerCAmelCase				   =       conva_get[2]
											__lowerCAmelCase				   =       size_pa
											__lowerCAmelCase				   =       rate_w
											__lowerCAmelCase				   =       rate_t
											__lowerCAmelCase				   =       [
											    np.mat(-1 * np.random.rand(self.conva[0]		,			self.conva[0]			) + 0.5			)
											    for i in range(self.conva[1]			)
											]
											__lowerCAmelCase				   =       np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa			) + 0.5			)
											__lowerCAmelCase				   =       np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa			) + 0.5			)
											__lowerCAmelCase				   =       -2 * np.random.rand(self.conva[1]			) + 1
											__lowerCAmelCase				   =       -2 * np.random.rand(self.num_bpa			) + 1
											__lowerCAmelCase				   =       -2 * np.random.rand(self.num_bpa			) + 1






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {
											    "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(_A		,			"wb"			) as f:
																pickle.dump(_A		,			_A			)

											print(f"""Model saved： {save_path}"""			)






						@classmethod
						def 				__SCREAMING_SNAKE_CASE(   cls		,			_A			):




											"""simple docstring"""



											with open(_A		,			"rb"			) as f:
																__lowerCAmelCase				   =       pickle.load(_A			)  # noqa: S301

											__lowerCAmelCase				   =       model_dic.get("conv1"			)
											conv_get.append(model_dic.get("step_conv1"			)			)
											__lowerCAmelCase				   =       model_dic.get("size_pooling1"			)
											__lowerCAmelCase				   =       model_dic.get("num_bp1"			)
											__lowerCAmelCase				   =       model_dic.get("num_bp2"			)
											__lowerCAmelCase				   =       model_dic.get("num_bp3"			)
											__lowerCAmelCase				   =       model_dic.get("rate_weight"			)
											__lowerCAmelCase				   =       model_dic.get("rate_thre"			)
											# create model instance
											__lowerCAmelCase				   =       CNN(_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			)
											# modify model parameter
											__lowerCAmelCase				   =       model_dic.get("w_conv1"			)
											__lowerCAmelCase				   =       model_dic.get("wkj"			)
											__lowerCAmelCase				   =       model_dic.get("vji"			)
											__lowerCAmelCase				   =       model_dic.get("thre_conv1"			)
											__lowerCAmelCase				   =       model_dic.get("thre_bp2"			)
											__lowerCAmelCase				   =       model_dic.get("thre_bp3"			)
											return conv_ins






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return 1 / (1 + np.exp(-1 * x			))






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return round(_A		,			3			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



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

											# expanding the data slice to One dimenssion
											__lowerCAmelCase				   =       []
											for each_focus in data_focus:
																focusa_list.extend(self.Expand_Mat(_A			)			)
											__lowerCAmelCase				   =       np.asarray(_A			)
											return focus_list, data_featuremap






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A="average_pool"			):




											"""simple docstring"""



											__lowerCAmelCase				   =       len(featuremaps[0]			)
											__lowerCAmelCase				   =       int(size_map / size_pooling			)
											__lowerCAmelCase				   =       []
											for i_map in range(len(_A			)			):
																__lowerCAmelCase				   =       featuremaps[i_map]
																__lowerCAmelCase				   =       []
																for i_focus in range(0		,			_A		,			_A			):
																					for j_focus in range(0		,			_A		,			_A			):
																										__lowerCAmelCase				   =       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(_A			)			)
																										elif pooling_type == "max_pooling":
																															# max pooling
																															map_pooled.append(np.max(_A			)			)
																__lowerCAmelCase				   =       np.asmatrix(_A			).reshape(_A		,			_A			)
																featuremap_pooled.append(_A			)
											return featuremap_pooled






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											for i in range(len(_A			)			):
																__lowerCAmelCase				   =       np.shape(data[i]			)
																__lowerCAmelCase				   =       data[i].reshape(1		,			shapes[0] * shapes[1]			)
																__lowerCAmelCase				   =       data_listed.getA().tolist()[0]
																data_expanded.extend(_A			)
											__lowerCAmelCase				   =       np.asarray(_A			)
											return data_expanded






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       np.asarray(_A			)
											__lowerCAmelCase				   =       np.shape(_A			)
											__lowerCAmelCase				   =       data_mat.reshape(1		,			shapes[0] * shapes[1]			)
											return data_expanded






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											__lowerCAmelCase				   =       0
											for i_map in range(_A			):
																__lowerCAmelCase				   =       np.ones((size_map, size_map)			)
																for i in range(0		,			_A		,			_A			):
																					for j in range(0		,			_A		,			_A			):
																										__lowerCAmelCase				   =       pd_pool[
																										    i_pool
																										]
																										__lowerCAmelCase				   =       i_pool + 1
																__lowerCAmelCase				   =       np.multiply(
																    _A		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])			)			)
																pd_all.append(_A			)
											return pd_all






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A=bool			):




											"""simple docstring"""



											print("----------------------Start Training-------------------------"			)
											print((" - - Shape: Train_Data  ", np.shape(_A			))			)
											print((" - - Shape: Teach_Data  ", np.shape(_A			))			)
											__lowerCAmelCase				   =       0
											__lowerCAmelCase				   =       []
											__lowerCAmelCase				   =       1_0_0_0_0
											while rp < n_repeat and mse >= error_accuracy:
																__lowerCAmelCase				   =       0
																print(f"""-------------Learning Time {rp}--------------"""			)
																for p in range(len(_A			)			):
																					# print('------------Learning Image: %d--------------'%p)
																					__lowerCAmelCase				   =       np.asmatrix(datas_train[p]			)
																					__lowerCAmelCase				   =       np.asarray(datas_teach[p]			)
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.convolute(
																					    _A		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
																					__lowerCAmelCase				   =       self.pooling(_A		,			self.size_poolinga			)
																					__lowerCAmelCase				   =       np.shape(_A			)
																					__lowerCAmelCase				   =       self._expand(_A			)
																					__lowerCAmelCase				   =       data_bp_input

																					__lowerCAmelCase				   =       np.dot(_A		,			self.vji.T			) - self.thre_bpa
																					__lowerCAmelCase				   =       self.sig(_A			)
																					__lowerCAmelCase				   =       np.dot(_A		,			self.wkj.T			) - self.thre_bpa
																					__lowerCAmelCase				   =       self.sig(_A			)

																					# --------------Model Leaning ------------------------
																					# calculate error and gradient---------------
																					__lowerCAmelCase				   =       np.multiply(
																					    (data_teach - bp_outa)		,			np.multiply(_A		,			(1 - bp_outa)			)			)
																					__lowerCAmelCase				   =       np.multiply(
																					    np.dot(_A		,			self.wkj			)		,			np.multiply(_A		,			(1 - bp_outa)			)			)
																					__lowerCAmelCase				   =       np.dot(_A		,			self.vji			)

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

																										__lowerCAmelCase				   =       self.w_conva[k_conv] + delta_w.reshape(
																										    (self.conva[0], self.conva[0])			)

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

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

											print("------------------Training Complished---------------------"			)
											print((" - - Training epoch: ", rp, f"""     - - Mse: {mse:.6f}""")			)
											if draw_e:
																draw_error()
											return mse






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											print("-------------------Start Testing-------------------------"			)
											print((" - - Shape: Test_Data  ", np.shape(_A			))			)
											for p in range(len(_A			)			):
																__lowerCAmelCase				   =       np.asmatrix(datas_test[p]			)
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.convolute(
																    _A		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
																__lowerCAmelCase				   =       self.pooling(_A		,			self.size_poolinga			)
																__lowerCAmelCase				   =       self._expand(_A			)

																__lowerCAmelCase				   =       data_bp_input
																__lowerCAmelCase				   =       bp_outa * self.vji.T - self.thre_bpa
																__lowerCAmelCase				   =       self.sig(_A			)
																__lowerCAmelCase				   =       bp_outa * self.wkj.T - self.thre_bpa
																__lowerCAmelCase				   =       self.sig(_A			)
																produce_out.extend(bp_outa.getA().tolist()			)
											__lowerCAmelCase				   =       [list(map(self.do_round		,			_A			)			) for each in produce_out]
											return np.asarray(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       np.asmatrix(_A			)
											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.convolute(
											    _A		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
											__lowerCAmelCase				   =       self.pooling(_A		,			self.size_poolinga			)

											return data_conveda, data_pooleda


if __name__ == "__main__":
					pass











import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

_a 						=      {
    "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",
    "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",
    "mask_emb": "masked_spec_embed",
}
_a 						=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







	for attribute in key.split('''.'''							):
		lowerCamelCase__								=		getattr(__snake_case       ,__snake_case							)

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

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





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		[]
	lowerCamelCase__								=		fairseq_model.state_dict()

	lowerCamelCase__								=		hf_model.feature_extractor

	# if encoder has different dim to decoder -> use proj_weight
	lowerCamelCase__								=		None

	for name, value in fairseq_dict.items():
		lowerCamelCase__								=		False
		if "conv_layers" in name:
			load_conv_layer(
			    __snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,hf_model.config.feat_extract_norm == '''group'''       ,)
			lowerCamelCase__								=		True
		elif name.split('''.'''							)[0] == "proj":
			lowerCamelCase__								=		fairseq_model.proj
			lowerCamelCase__								=		True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
					lowerCamelCase__								=		True
					if "*" in mapped_key:
						lowerCamelCase__								=		name.split(__snake_case							)[0].split('''.'''							)[-2]
						lowerCamelCase__								=		mapped_key.replace('''*'''       ,__snake_case							)
					if "weight_g" in name:
						lowerCamelCase__								=		'''weight_g'''
					elif "weight_v" in name:
						lowerCamelCase__								=		'''weight_v'''
					elif "bias" in name:
						lowerCamelCase__								=		'''bias'''
					elif "weight" in name:
						lowerCamelCase__								=		'''weight'''
					else:
						lowerCamelCase__								=		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}'							)

	return proj_weight





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	lowerCamelCase__								=		full_name.split('''conv_layers.'''							)[-1]
	lowerCamelCase__								=		name.split('''.'''							)
	lowerCamelCase__								=		int(items[0]							)
	lowerCamelCase__								=		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__								=		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__								=		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__								=		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__								=		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 lowerCAmelCase__(__snake_case							)				->      Any:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		emb.weight.shape
	lowerCamelCase__								=		nn.Linear(__snake_case       ,__snake_case       ,bias=__snake_case							)
	lowerCamelCase__								=		emb.weight.data
	return lin_layer





def lowerCAmelCase__(__snake_case							)				->      Any:







	'''simple docstring'''







	with open(__snake_case       ,'''r'''       ,encoding='''utf-8'''							) as f:
		lowerCamelCase__								=		f.readlines()
		lowerCamelCase__								=		[line.split(''' '''							)[0] for line in lines]

	lowerCamelCase__								=		len(__snake_case							)

	lowerCamelCase__								=		{
	    '''<s>''': 0,
	    '''<pad>''': 1,
	    '''</s>''': 2,
	    '''<unk>''': 3,
	}

	vocab_dict.update(dict(zip(__snake_case       ,range(4       ,num_words + 4							)							)							)							)
	return vocab_dict





@torch.no_grad()
def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,)				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		WavaVecaConfig.from_pretrained(__snake_case							)
	lowerCamelCase__								=		SpeechaTextaConfig.from_pretrained(
	    __snake_case       ,vocab_size=__snake_case       ,decoder_layers=__snake_case       ,do_stable_layer_norm=__snake_case							)

	lowerCamelCase__								=		WavaVecaFeatureExtractor(
	    feature_size=1       ,sampling_rate=16000       ,padding_value=0       ,do_normalize=__snake_case       ,return_attention_mask=__snake_case       ,)

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

	# set weights for wav2vec2 encoder
	lowerCamelCase__								=		WavaVecaModel(__snake_case							)
	lowerCamelCase__								=		recursively_load_weights_wavaveca(model.encoder       ,__snake_case							)

	lowerCamelCase__								=		SpeechaTextaForCausalLM(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,strict=__snake_case							)

	# set output linear layer
	unexpected_keys.remove('''embed_out'''							)
	lowerCamelCase__								=		nn.Parameter(model.decoder.embed_out.detach()							)

	# layer norm is init to identity matrix so leaving it is fine
	logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'							)
	logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'							)

	lowerCamelCase__								=		SpeechEncoderDecoderModel(encoder=__snake_case       ,decoder=__snake_case							)
	lowerCamelCase__								=		False

	# add projection layer
	lowerCamelCase__								=		nn.Parameter(projection_layer.weight							)
	lowerCamelCase__								=		nn.Parameter(projection_layer.bias							)

	lowerCamelCase__								=		create_vocab_dict(__snake_case							)

	with open(os.path.join(__snake_case       ,'''vocab.json'''							)       ,'''w'''							) as fp:
		json.dump(__snake_case       ,__snake_case							)

	lowerCamelCase__								=		SpeechaTextaTokenizer(os.path.join(__snake_case       ,'''vocab.json'''							)							)
	tokenizer.save_pretrained(__snake_case							)

	lowerCamelCase__								=		hf_wavavec.config.to_dict()
	lowerCamelCase__								=		tokenizer.pad_token_id
	lowerCamelCase__								=		tokenizer.bos_token_id
	lowerCamelCase__								=		tokenizer.eos_token_id
	lowerCamelCase__								=		'''speech_to_text_2'''
	lowerCamelCase__								=		'''wav2vec2'''

	lowerCamelCase__								=		SpeechEncoderDecoderConfig.from_dict(__snake_case							)

	hf_wavavec.save_pretrained(__snake_case							)
	feature_extractor.save_pretrained(__snake_case							)


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(
			    "--encoder_config_path",
			    default="facebook/wav2vec2-large-lv60",
			    type=str,
			    help="Path to hf encoder wav2vec2 checkpoint config",
			)
			parser.add_argument(
			    "--decoder_config_path",
			    default="facebook/s2t-small-mustc-en-fr-st",
			    type=str,
			    help="Path to hf decoder s2t checkpoint config",
			)
			parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
			parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

			_a 						=      parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    vocab_size=args.vocab_size,
			    num_decoder_layers=args.num_decoder_layers,
			)



