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()
__SCREAMING_SNAKE_CASE						:   int         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Dict         =			{
    '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',
}
__SCREAMING_SNAKE_CASE						:   Dict         =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Optional[int]:

						'''simple docstring'''



						for attribute in key.split("."						):
												_snake_case						:					List[str]    =   getattr(__lowercase ,      __lowercase						)

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

						logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)
def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Tuple    =   []
						_snake_case						:					List[str]    =   fairseq_model.state_dict()

						_snake_case						:					List[str]    =   hf_model.feature_extractor

						# if encoder has different dim to decoder -> use proj_weight
						_snake_case						:					Dict    =   None

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

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

						return proj_weight
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Any:

						'''simple docstring'''



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

						'''simple docstring'''



						_snake_case						:					int    =   emb.weight.shape
						_snake_case						:					List[Any]    =   nn.Linear(__lowercase ,      __lowercase ,      bias=__lowercase						)
						_snake_case						:					Any    =   emb.weight.data
						return lin_layer
def  snake_case						(__lowercase						)	->	int:

						'''simple docstring'''



						with open(__lowercase ,      "r" ,      encoding="utf-8"						) as f:
												_snake_case						:					List[Any]    =   f.readlines()
												_snake_case						:					Tuple    =   [line.split(" "						)[0] for line in lines]

						_snake_case						:					str    =   len(__lowercase						)

						_snake_case						:					List[str]    =   {
						    "<s>": 0,
						    "<pad>": 1,
						    "</s>": 2,
						    "<unk>": 3,
						}

						vocab_dict.update(dict(zip(__lowercase ,      range(4 ,      num_words + 4						)						)						)						)
						return vocab_dict
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      )	->	Dict:

						'''simple docstring'''



						_snake_case						:					str    =   WavaVecaConfig.from_pretrained(__lowercase						)
						_snake_case						:					Tuple    =   SpeechaTextaConfig.from_pretrained(
						    __lowercase ,      vocab_size=__lowercase ,      decoder_layers=__lowercase ,      do_stable_layer_norm=__lowercase						)

						_snake_case						:					List[str]    =   WavaVecaFeatureExtractor(
						    feature_size=1 ,      sampling_rate=16_000 ,      padding_value=0 ,      do_normalize=__lowercase ,      return_attention_mask=__lowercase ,      )

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

						# set weights for wav2vec2 encoder
						_snake_case						:					Union[str, Any]    =   WavaVecaModel(__lowercase						)
						_snake_case						:					Optional[int]    =   recursively_load_weights_wavaveca(model.encoder ,      __lowercase						)

						_snake_case						:					Dict    =   SpeechaTextaForCausalLM(__lowercase						)
						_snake_case						:					List[str]    =   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,      strict=__lowercase						)

						# set output linear layer
						unexpected_keys.remove("embed_out"						)
						_snake_case						:					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}"""						)

						_snake_case						:					Tuple    =   SpeechEncoderDecoderModel(encoder=__lowercase ,      decoder=__lowercase						)
						_snake_case						:					Tuple    =   False

						# add projection layer
						_snake_case						:					Tuple    =   nn.Parameter(projection_layer.weight						)
						_snake_case						:					Tuple    =   nn.Parameter(projection_layer.bias						)

						_snake_case						:					Union[str, Any]    =   create_vocab_dict(__lowercase						)

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

						_snake_case						:					Tuple    =   SpeechaTextaTokenizer(os.path.join(__lowercase ,      "vocab.json"						)						)
						tokenizer.save_pretrained(__lowercase						)

						_snake_case						:					int    =   hf_wavavec.config.to_dict()
						_snake_case						:					int    =   tokenizer.pad_token_id
						_snake_case						:					Union[str, Any]    =   tokenizer.bos_token_id
						_snake_case						:					int    =   tokenizer.eos_token_id
						_snake_case						:					Optional[Any]    =   "speech_to_text_2"
						_snake_case						:					Union[str, Any]    =   "wav2vec2"

						_snake_case						:					Optional[int]    =   SpeechEncoderDecoderConfig.from_dict(__lowercase						)

						hf_wavavec.save_pretrained(__lowercase						)
						feature_extractor.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			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')

				__SCREAMING_SNAKE_CASE						:   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,
				)




def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						_snake_case						:					Dict    =   0
						for ch in input_str:
												_snake_case						:					int    =   ord(__lowercase						)
												_snake_case						:					List[Any]    =   pow(2 ,      __lowercase						)

												# If we already turned on bit for current character's unicode
												if bitmap >> ch_unicode & 1 == 1:
																		return False
												bitmap |= ch_bit_index_on
						return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()