



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()
__snake_case    :		str  =logging.get_logger(__name__)

__snake_case    :		str  ={
    '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',
}
__snake_case    :		int  =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				for attribute in key.split('''.'''):
								lowerCAmelCase__					:    Dict												=					getattr(lowerCamelCase_	,lowerCamelCase_)

				if weight_type is not None:
								lowerCAmelCase__					:    Tuple												=					getattr(lowerCamelCase_	,lowerCamelCase_).shape
				else:
								lowerCAmelCase__					:    Tuple												=					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__					:    List[str]												=					value
				elif weight_type == "weight_g":
								lowerCAmelCase__					:    List[str]												=					value
				elif weight_type == "weight_v":
								lowerCAmelCase__					:    List[str]												=					value
				elif weight_type == "bias":
								lowerCAmelCase__					:    Any												=					value
				else:
								lowerCAmelCase__					:    Any												=					value

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					[]
				lowerCAmelCase__					:    Any												=					fairseq_model.state_dict()

				lowerCAmelCase__					:    Tuple												=					hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowerCAmelCase__					:    Any												=					None

				for name, value in fairseq_dict.items():
								lowerCAmelCase__					:    List[str]												=					False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,hf_model.config.feat_extract_norm == '''group'''	,)
												lowerCAmelCase__					:    Optional[int]												=					True
								elif name.split('''.''')[0] == "proj":
												lowerCAmelCase__					:    Any												=					fairseq_model.proj
												lowerCAmelCase__					:    Tuple												=					True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
																				lowerCAmelCase__					:    List[str]												=					True
																				if "*" in mapped_key:
																								lowerCAmelCase__					:    Union[str, Any]												=					name.split(lowerCamelCase_)[0].split('''.''')[-2]
																								lowerCAmelCase__					:    List[str]												=					mapped_key.replace('''*'''	,lowerCamelCase_)
																				if "weight_g" in name:
																								lowerCAmelCase__					:    Any												=					'''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase__					:    int												=					'''weight_v'''
																				elif "bias" in name:
																								lowerCAmelCase__					:    int												=					'''bias'''
																				elif "weight" in name:
																								lowerCAmelCase__					:    Any												=					'''weight'''
																				else:
																								lowerCAmelCase__					:    Union[str, Any]												=					None
																				set_recursively(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase_)

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

				return proj_weight






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Any	,lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					full_name.split('''conv_layers.''')[-1]
				lowerCAmelCase__					:    List[Any]												=					name.split('''.''')
				lowerCAmelCase__					:    Union[str, Any]												=					int(items[0])
				lowerCAmelCase__					:    List[Any]												=					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__					:    List[Any]												=					value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												lowerCAmelCase__					:    Union[str, 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."
												)
												lowerCAmelCase__					:    Optional[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."""
												)
												lowerCAmelCase__					:    Optional[int]												=					value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
				else:
								unused_weights.append(lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					emb.weight.shape
				lowerCAmelCase__					:    Dict												=					nn.Linear(lowerCamelCase_	,lowerCamelCase_	,bias=lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					emb.weight.data
				return lin_layer






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				with open(lowerCamelCase_	,'''r'''	,encoding='''utf-8''') as f:
								lowerCAmelCase__					:    Optional[int]												=					f.readlines()
								lowerCAmelCase__					:    str												=					[line.split(''' ''')[0] for line in lines]

				lowerCAmelCase__					:    List[Any]												=					len(lowerCamelCase_)

				lowerCAmelCase__					:    Optional[Any]												=					{
				    '''<s>''': 0,
				    '''<pad>''': 1,
				    '''</s>''': 2,
				    '''<unk>''': 3,
				}

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






@torch.no_grad()
def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Any	,lowerCamelCase_		:		Optional[Any]	,):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					WavaVecaConfig.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Any												=					SpeechaTextaConfig.from_pretrained(
				    lowerCamelCase_	,vocab_size=lowerCamelCase_	,decoder_layers=lowerCamelCase_	,do_stable_layer_norm=lowerCamelCase_)

				lowerCAmelCase__					:    List[Any]												=					WavaVecaFeatureExtractor(
				    feature_size=1	,sampling_rate=16000	,padding_value=0	,do_normalize=lowerCamelCase_	,return_attention_mask=lowerCamelCase_	,)

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

				# set weights for wav2vec2 encoder
				lowerCAmelCase__					:    Any												=					WavaVecaModel(lowerCamelCase_)
				lowerCAmelCase__					:    List[str]												=					recursively_load_weights_wavaveca(model.encoder	,lowerCamelCase_)

				lowerCAmelCase__					:    Any												=					SpeechaTextaForCausalLM(lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,strict=lowerCamelCase_)

				# set output linear layer
				unexpected_keys.remove('''embed_out''')
				lowerCAmelCase__					:    Dict												=					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__					:    Tuple												=					SpeechEncoderDecoderModel(encoder=lowerCamelCase_	,decoder=lowerCamelCase_)
				lowerCAmelCase__					:    List[str]												=					False

				# add projection layer
				lowerCAmelCase__					:    List[Any]												=					nn.Parameter(projection_layer.weight)
				lowerCAmelCase__					:    Union[str, Any]												=					nn.Parameter(projection_layer.bias)

				lowerCAmelCase__					:    Any												=					create_vocab_dict(lowerCamelCase_)

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

				lowerCAmelCase__					:    Union[str, Any]												=					SpeechaTextaTokenizer(os.path.join(lowerCamelCase_	,'''vocab.json'''))
				tokenizer.save_pretrained(lowerCamelCase_)

				lowerCAmelCase__					:    Optional[Any]												=					hf_wavavec.config.to_dict()
				lowerCAmelCase__					:    List[str]												=					tokenizer.pad_token_id
				lowerCAmelCase__					:    Dict												=					tokenizer.bos_token_id
				lowerCAmelCase__					:    Optional[Any]												=					tokenizer.eos_token_id
				lowerCAmelCase__					:    Union[str, Any]												=					'''speech_to_text_2'''
				lowerCAmelCase__					:    Optional[Any]												=					'''wav2vec2'''

				lowerCAmelCase__					:    str												=					SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_)

				hf_wavavec.save_pretrained(lowerCamelCase_)
				feature_extractor.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case    :		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(
							    '--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=1_0_2_2_4, type=int, help='Vocab size of decoder')
							parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

							__snake_case    :		str  =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,
							)










import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					self.__linear
						elif kernel == "rbf":
										if self.gamma == 0:
														raise ValueError('''rbf kernel requires gamma'''			)
										if not isinstance(self.gamma		,(float, int)			):
														raise ValueError('''gamma must be float or int'''			)
										if not self.gamma > 0:
														raise ValueError('''gamma must be > 0'''			)
										lowerCAmelCase__					:    Optional[Any]												=					self.__rbf
										# in the future, there could be a default value like in sklearn
										# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
										# previously it was 1/(n_features)
						else:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					classes

						# using Wolfe's Dual to calculate w.
						# Primal problem: minimize 1/2*norm_squared(w)
						#   constraint: yn(w . xn + b) >= 1
						#
						# With l a vector
						# Dual problem: maximize sum_n(ln) -
						#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
						#   constraint: self.C >= ln >= 0
						#           and sum_n(ln*yn) = 0
						# Then we get w using w = sum_n(ln*yn*xn)
						# At the end we can get b ~= mean(yn - w . xn)
						#
						# Since we use kernels, we only need l_star to calculate b
						# and to classify observations

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    str												=					np.shape(__lowerCamelCase			)
										for i in range(__lowerCamelCase			):
														for j in range(__lowerCamelCase			):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]		,observations[j]			)
																		)
										return 1 / 2 * s - sum(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					LinearConstraint(__lowerCamelCase		,0		,0			)
						lowerCAmelCase__					:    List[str]												=					Bounds(0		,self.regularization			)

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

						# calculating mean offset of separation plane to points
						lowerCAmelCase__					:    Optional[Any]												=					0
						for i in range(__lowerCamelCase			):
										for j in range(__lowerCamelCase			):
														s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
														    observations[i]		,observations[j]			)
						lowerCAmelCase__					:    Dict												=					s / n



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]		,__lowerCamelCase			)
						    for n in range(len(self.classes			)			)			)
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

