






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()
lowercase    				=   logging.get_logger(__name__)

lowercase    				=   {
    "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",
}
lowercase    				=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_):
							for attribute in key.split('.'):
														snake_case_           =				getattr(a_				,	a_)

							if weight_type is not None:
														snake_case_           =				getattr(a_				,	a_).shape
							else:
														snake_case_           =				hf_pointer.shape

							assert hf_shape == value.shape, (
							    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
							    f''' {value.shape} for {full_name}'''
							)

							if weight_type == "weight":
														snake_case_           =				value
							elif weight_type == "weight_g":
														snake_case_           =				value
							elif weight_type == "weight_v":
														snake_case_           =				value
							elif weight_type == "bias":
														snake_case_           =				value
							else:
														snake_case_           =				value

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





def 		__UpperCAmelCase     (	a_				,	a_):
							snake_case_           =				[]
							snake_case_           =				fairseq_model.state_dict()

							snake_case_           =				hf_model.feature_extractor

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

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

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

							return proj_weight





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_):
							snake_case_           =				full_name.split('conv_layers.')[-1]
							snake_case_           =				name.split('.')
							snake_case_           =				int(items[0])
							snake_case_           =				int(items[1])

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    f'''{full_name} has size {value.shape}, but'''
																					    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
																					)
																					snake_case_           =				value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    f'''{full_name} has size {value.shape}, but'''
																					    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																					)
																					snake_case_           =				value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
																					    " found."
																					)
																					snake_case_           =				value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    f'''{full_name} has size {value.shape}, but'''
																					    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
																					)
																					snake_case_           =				value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
							else:
														unused_weights.append(a_)





def 		__UpperCAmelCase     (	a_):
							snake_case_     ,						snake_case_           =				emb.weight.shape
							snake_case_           =				nn.Linear(a_				,	a_				,	bias=a_)
							snake_case_           =				emb.weight.data
							return lin_layer





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

							snake_case_           =				len(a_)

							snake_case_           =				{
							    '<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 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_				,	a_				,	a_				,	):
							snake_case_           =				WavaVecaConfig.from_pretrained(a_)
							snake_case_           =				SpeechaTextaConfig.from_pretrained(
							    a_				,	vocab_size=a_				,	decoder_layers=a_				,	do_stable_layer_norm=a_)

							snake_case_           =				WavaVecaFeatureExtractor(
							    feature_size=1				,	sampling_rate=1_60_00				,	padding_value=0				,	do_normalize=a_				,	return_attention_mask=a_				,	)

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

							# set weights for wav2vec2 encoder
							snake_case_           =				WavaVecaModel(a_)
							snake_case_           =				recursively_load_weights_wavaveca(model.encoder				,	a_)

							snake_case_           =				SpeechaTextaForCausalLM(a_)
							snake_case_     ,						snake_case_           =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,	strict=a_)

							# set output linear layer
							unexpected_keys.remove('embed_out')
							snake_case_           =				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}''')

							snake_case_           =				SpeechEncoderDecoderModel(encoder=a_				,	decoder=a_)
							snake_case_           =				False

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

							snake_case_           =				create_vocab_dict(a_)

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

							snake_case_           =				SpeechaTextaTokenizer(os.path.join(a_				,	'vocab.json'))
							tokenizer.save_pretrained(a_)

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

							snake_case_           =				SpeechEncoderDecoderConfig.from_dict(a_)

							hf_wavavec.save_pretrained(a_)
							feature_extractor.save_pretrained(a_)


if __name__ == "__main__":
	lowercase    				=   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=10224, type=int, help="Vocab size of decoder")
	parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

	lowercase    				=   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,
	)













def 		__UpperCAmelCase     (	a_ = 10**9):
							snake_case_           =				1
							snake_case_           =				2

							snake_case_           =				0
							snake_case_           =				0
							snake_case_           =				0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														snake_case_           =				2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
	print(f'{solution() = }')





