

"""simple docstring"""





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

__UpperCamelCase  :      Union[str, Any]							      =		{
    '''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''',
}
__UpperCamelCase  :      List[Any]							      =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_						,     A_ ):
	for attribute in key.split('''.''' ):
		lowerCAmelCase__	:						Any  				=	getattr(A_						,     A_ )

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

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Tuple  				=	[]
	lowerCAmelCase__	:						Union[str, Any]  				=	fairseq_model.state_dict()

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

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

	return proj_weight

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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	emb.weight.shape
	lowerCAmelCase__	:						List[str]  				=	nn.Linear(A_						,     A_						,     bias=A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	emb.weight.data
	return lin_layer

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	with open(A_						,     '''r'''						,     encoding='''utf-8''' ) as f:
		lowerCAmelCase__	:						str  				=	f.readlines()
		lowerCAmelCase__	:						Any  				=	[line.split(''' ''' )[0] for line in lines]

	lowerCAmelCase__	:						Tuple  				=	len(A_ )

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

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

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_						,     A_						,     A_						,     A_						,     ):
	lowerCAmelCase__	:						Optional[int]  				=	WavaVecaConfig.from_pretrained(A_ )
	lowerCAmelCase__	:						Dict  				=	SpeechaTextaConfig.from_pretrained(
	    A_						,     vocab_size=A_						,     decoder_layers=A_						,     do_stable_layer_norm=A_ )

	lowerCAmelCase__	:						Optional[Any]  				=	WavaVecaFeatureExtractor(
	    feature_size=1						,     sampling_rate=1_60_00						,     padding_value=0						,     do_normalize=A_						,     return_attention_mask=A_						,     )

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

	# set weights for wav2vec2 encoder
	lowerCAmelCase__	:						str  				=	WavaVecaModel(A_ )
	lowerCAmelCase__	:						Dict  				=	recursively_load_weights_wavaveca(model.encoder						,     A_ )

	lowerCAmelCase__	:						List[Any]  				=	SpeechaTextaForCausalLM(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,     strict=A_ )

	# set output linear layer
	unexpected_keys.remove('''embed_out''' )
	lowerCAmelCase__	:						Optional[Any]  				=	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__	:						Union[str, Any]  				=	SpeechEncoderDecoderModel(encoder=A_						,     decoder=A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	False

	# add projection layer
	lowerCAmelCase__	:						Optional[int]  				=	nn.Parameter(projection_layer.weight )
	lowerCAmelCase__	:						List[Any]  				=	nn.Parameter(projection_layer.bias )

	lowerCAmelCase__	:						str  				=	create_vocab_dict(A_ )

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

	lowerCAmelCase__	:						str  				=	SpeechaTextaTokenizer(os.path.join(A_						,     '''vocab.json''' ) )
	tokenizer.save_pretrained(A_ )

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

	lowerCAmelCase__	:						Dict  				=	SpeechEncoderDecoderConfig.from_dict(A_ )

	hf_wavavec.save_pretrained(A_ )
	feature_extractor.save_pretrained(A_ )


if __name__ == "__main__":
							__UpperCamelCase  :      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(
							    '''--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''')

							__UpperCamelCase  :      List[Any]							      =		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,
							)







"""simple docstring"""





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	self.dq_store.pop()
								self.key_reference.remove(lowercase_		)
						else:
							self.dq_store.remove(lowercase_		)

						self.dq_store.appendleft(lowercase_		)
						self.key_reference.add(lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      LRUCache[str | int]							      =		LRUCache(4)
							lru_cache.refer('''A''')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('''A''')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




