


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


a					  =     {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							a					  =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					for attribute in key.split("""."""  ):
										lowercase_  							=			getattr(UpperCAmelCase__						,							UpperCAmelCase__  )

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			[]
					lowercase_  							=			fairseq_model.state_dict()

					lowercase_  							=			hf_model.feature_extractor

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

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

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

					return proj_weight


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			full_name.split("""conv_layers."""  )[-1]
					lowercase_  							=			name.split("""."""  )
					lowercase_  							=			int(items[0]  )
					lowercase_  							=			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.'''
															)
															lowercase_  							=			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.'''
															)
															lowercase_  							=			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."
															)
															lowercase_  							=			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.'''
															)
															lowercase_  							=			value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
					else:
										unused_weights.append(UpperCAmelCase__  )


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_							,					lowercase_  							=			emb.weight.shape
					lowercase_  							=			nn.Linear(UpperCAmelCase__						,							UpperCAmelCase__						,							bias=UpperCAmelCase__  )
					lowercase_  							=			emb.weight.data
					return lin_layer


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					with open(UpperCAmelCase__						,							"""r"""						,							encoding="""utf-8"""  ) as f:
										lowercase_  							=			f.readlines()
										lowercase_  							=			[line.split(""" """  )[0] for line in lines]

					lowercase_  							=			len(UpperCAmelCase__  )

					lowercase_  							=			{
					    """<s>""": 0,
					    """<pad>""": 1,
					    """</s>""": 2,
					    """<unk>""": 3,
					}

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


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							):
					lowercase_  							=			WavaVecaConfig.from_pretrained(UpperCAmelCase__  )
					lowercase_  							=			SpeechaTextaConfig.from_pretrained(
					    UpperCAmelCase__						,							vocab_size=UpperCAmelCase__						,							decoder_layers=UpperCAmelCase__						,							do_stable_layer_norm=UpperCAmelCase__  )

					lowercase_  							=			WavaVecaFeatureExtractor(
					    feature_size=1						,							sampling_rate=1_6_0_0_0						,							padding_value=0						,							do_normalize=UpperCAmelCase__						,							return_attention_mask=UpperCAmelCase__						,							)

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

					# set weights for wav2vec2 encoder
					lowercase_  							=			WavaVecaModel(UpperCAmelCase__  )
					lowercase_  							=			recursively_load_weights_wavaveca(model.encoder						,							UpperCAmelCase__  )

					lowercase_  							=			SpeechaTextaForCausalLM(UpperCAmelCase__  )
					lowercase_							,					lowercase_  							=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,							strict=UpperCAmelCase__  )

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

					lowercase_  							=			SpeechEncoderDecoderModel(encoder=UpperCAmelCase__						,							decoder=UpperCAmelCase__  )
					lowercase_  							=			False

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

					lowercase_  							=			create_vocab_dict(UpperCAmelCase__  )

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

					lowercase_  							=			SpeechaTextaTokenizer(os.path.join(UpperCAmelCase__						,							"""vocab.json"""  )  )
					tokenizer.save_pretrained(UpperCAmelCase__  )

					lowercase_  							=			hf_wavavec.config.to_dict()
					lowercase_  							=			tokenizer.pad_token_id
					lowercase_  							=			tokenizer.bos_token_id
					lowercase_  							=			tokenizer.eos_token_id
					lowercase_  							=			"""speech_to_text_2"""
					lowercase_  							=			"""wav2vec2"""

					lowercase_  							=			SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__  )

					hf_wavavec.save_pretrained(UpperCAmelCase__  )
					feature_extractor.save_pretrained(UpperCAmelCase__  )


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=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')

							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,
							)


