
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCamelCase_						: Union[str, Any]         =  logging.get_logger(__name__)

lowerCamelCase_						: 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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
lowerCamelCase_						: List[str]         =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def        A__					(						lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							str	=						{}
				with open(lowerCamelCase     ,    """r"""						) as file:
								for line_number, line in enumerate(lowerCamelCase						):
												UpperCamelCase_:							Tuple	=						line.strip()
												if line:
																UpperCamelCase_:							int	=						line.split()
																UpperCamelCase_:							List[str]	=						line_number
																UpperCamelCase_:							Tuple	=						words[0]
																UpperCamelCase_:							Union[str, Any]	=						value
				return result







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Dict:
				for attribute in key.split("""."""						):
								UpperCamelCase_:							Optional[Any]	=						getattr(lowerCamelCase     ,    lowerCamelCase						)

				UpperCamelCase_:							Dict	=						None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowerCamelCase						):
												UpperCamelCase_:							Dict	=						PARAM_MAPPING[full_name.split("""."""						)[-1]]
												UpperCamelCase_:							Dict	=						"""param"""

				if weight_type is not None and weight_type != "param":
								UpperCamelCase_:							Any	=						getattr(lowerCamelCase     ,    lowerCamelCase						).shape
				elif weight_type is not None and weight_type == "param":
								UpperCamelCase_:							Optional[int]	=						hf_pointer
								for attribute in hf_param_name.split("""."""						):
												UpperCamelCase_:							Dict	=						getattr(lowerCamelCase     ,    lowerCamelCase						)
								UpperCamelCase_:							Tuple	=						shape_pointer.shape

								# let's reduce dimension
								UpperCamelCase_:							Union[str, Any]	=						value[0]
				else:
								UpperCamelCase_:							List[Any]	=						hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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":
								UpperCamelCase_:							Optional[Any]	=						value
				elif weight_type == "weight_g":
								UpperCamelCase_:							int	=						value
				elif weight_type == "weight_v":
								UpperCamelCase_:							Union[str, Any]	=						value
				elif weight_type == "bias":
								UpperCamelCase_:							Optional[int]	=						value
				elif weight_type == "param":
								for attribute in hf_param_name.split("""."""						):
												UpperCamelCase_:							str	=						getattr(lowerCamelCase     ,    lowerCamelCase						)
								UpperCamelCase_:							Optional[int]	=						value
				else:
								UpperCamelCase_:							Optional[Any]	=						value

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







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				UpperCamelCase_:							List[Any]	=						None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowerCamelCase						):
												UpperCamelCase_:							Union[str, Any]	=						PARAM_MAPPING[full_name.split("""."""						)[-1]]
												UpperCamelCase_:							List[str]	=						"""param"""

				if weight_type is not None and weight_type != "param":
								UpperCamelCase_:							Union[str, Any]	=						""".""".join([key, weight_type]						)
				elif weight_type is not None and weight_type == "param":
								UpperCamelCase_:							List[str]	=						""".""".join([key, hf_param_name]						)
				else:
								UpperCamelCase_:							Optional[Any]	=						key

				UpperCamelCase_:							Tuple	=						value if """lm_head""" in full_key else value[0]


lowerCamelCase_						: Dict         =  {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None						)  ->	List[str]:
				UpperCamelCase_:							int	=						False
				for key, mapped_key in MAPPING.items():
								UpperCamelCase_:							Union[str, Any]	=						"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
												UpperCamelCase_:							Any	=						True
												if "*" in mapped_key:
																UpperCamelCase_:							Optional[int]	=						name.split(lowerCamelCase						)[0].split("""."""						)[-2]
																UpperCamelCase_:							Union[str, Any]	=						mapped_key.replace("""*"""     ,    lowerCamelCase						)
												if "weight_g" in name:
																UpperCamelCase_:							Dict	=						"""weight_g"""
												elif "weight_v" in name:
																UpperCamelCase_:							Optional[int]	=						"""weight_v"""
												elif "bias" in name:
																UpperCamelCase_:							Union[str, Any]	=						"""bias"""
												elif "weight" in name:
																# TODO: don't match quantizer.weight_proj
																UpperCamelCase_:							Dict	=						"""weight"""
												else:
																UpperCamelCase_:							Optional[int]	=						None
												if hf_dict is not None:
																rename_dict(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)
												else:
																set_recursively(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)
												return is_used
				return is_used







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	str:
				UpperCamelCase_:							Tuple	=						[]
				UpperCamelCase_:							Optional[Any]	=						fairseq_model.state_dict()

				UpperCamelCase_:							int	=						hf_model.wavaveca.feature_extractor

				for name, value in fairseq_dict.items():
								UpperCamelCase_:							Union[str, Any]	=						False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    hf_model.config.feat_extract_norm == """group"""     ,    )
												UpperCamelCase_:							Union[str, Any]	=						True
								else:
												UpperCamelCase_:							List[str]	=						load_wavaveca_layer(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)
								if not is_used:
												unused_weights.append(lowerCamelCase						)

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







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Dict:
				UpperCamelCase_:							List[Any]	=						full_name.split("""conv_layers."""						)[-1]
				UpperCamelCase_:							Optional[Any]	=						name.split("""."""						)
				UpperCamelCase_:							Dict	=						int(items[0]						)
				UpperCamelCase_:							Tuple	=						int(items[1]						)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
												UpperCamelCase_:							Optional[Any]	=						value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
												UpperCamelCase_:							Dict	=						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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
												UpperCamelCase_:							Optional[Any]	=						value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
												UpperCamelCase_:							Any	=						value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
				else:
								unused_weights.append(lowerCamelCase						)







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    lowerCamelCase=True     ,    lowerCamelCase=False						)  ->	int:
				if config_path is not None:
								UpperCamelCase_:							str	=						WavaVecaConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							Optional[int]	=						WavaVecaConfig()

				if is_seq_class:
								UpperCamelCase_:							Optional[Any]	=						read_txt_into_dict(lowerCamelCase						)
								UpperCamelCase_:							List[Any]	=						idalabel
								UpperCamelCase_:							int	=						WavaVecaForSequenceClassification(lowerCamelCase						)
								UpperCamelCase_:							Union[str, Any]	=						WavaVecaFeatureExtractor(
								    feature_size=1     ,    sampling_rate=1_60_00     ,    padding_value=0     ,    do_normalize=lowerCamelCase     ,    return_attention_mask=lowerCamelCase     ,    )
								feature_extractor.save_pretrained(lowerCamelCase						)

				elif is_finetuned:
								if dict_path:
												UpperCamelCase_:							str	=						Dictionary.load(lowerCamelCase						)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												UpperCamelCase_:							int	=						target_dict.pad_index
												UpperCamelCase_:							Tuple	=						target_dict.bos_index
												UpperCamelCase_:							str	=						target_dict.eos_index
												UpperCamelCase_:							Any	=						len(target_dict.symbols						)
												UpperCamelCase_:							int	=						os.path.join(lowerCamelCase     ,    """vocab.json"""						)
												if not os.path.isdir(lowerCamelCase						):
																logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase						)						)
																return
												os.makedirs(lowerCamelCase     ,    exist_ok=lowerCamelCase						)
												UpperCamelCase_:							Dict	=						target_dict.indices

												# fairseq has the <pad> and <s> switched
												UpperCamelCase_:							Union[str, Any]	=						0
												UpperCamelCase_:							List[str]	=						1
												with open(lowerCamelCase     ,    """w"""     ,    encoding="""utf-8"""						) as vocab_handle:
																json.dump(lowerCamelCase     ,    lowerCamelCase						)
												UpperCamelCase_:							Tuple	=						WavaVecaCTCTokenizer(
												    lowerCamelCase     ,    unk_token=target_dict.unk_word     ,    pad_token=target_dict.pad_word     ,    bos_token=target_dict.bos_word     ,    eos_token=target_dict.eos_word     ,    word_delimiter_token="""|"""     ,    do_lower_case=lowerCamelCase     ,    )
												UpperCamelCase_:							List[str]	=						True if config.feat_extract_norm == """layer""" else False
												UpperCamelCase_:							Optional[int]	=						WavaVecaFeatureExtractor(
												    feature_size=1     ,    sampling_rate=1_60_00     ,    padding_value=0     ,    do_normalize=lowerCamelCase     ,    return_attention_mask=lowerCamelCase     ,    )
												UpperCamelCase_:							Optional[Any]	=						WavaVecaProcessor(feature_extractor=lowerCamelCase     ,    tokenizer=lowerCamelCase						)
												processor.save_pretrained(lowerCamelCase						)

								UpperCamelCase_:							Optional[Any]	=						WavaVecaForCTC(lowerCamelCase						)
				else:
								UpperCamelCase_:							Optional[Any]	=						WavaVecaForPreTraining(lowerCamelCase						)

				if is_finetuned or is_seq_class:
								UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Optional[Any]	=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]     ,    arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
				else:
								UpperCamelCase_:							Any	=						argparse.Namespace(task="""audio_pretraining"""						)
								UpperCamelCase_:							Optional[int]	=						fairseq.tasks.setup_task(lowerCamelCase						)

								UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							int	=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,    task=lowerCamelCase						)

				UpperCamelCase_:							Tuple	=						model[0].eval()

				recursively_load_weights(lowerCamelCase     ,    lowerCamelCase     ,    not is_finetuned						)

				hf_wavavec.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: int         =  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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							parser.add_argument(
							    """--is_seq_class""",
							    action="""store_true""",
							    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
							)
							lowerCamelCase_						: Optional[Any]         =  parser.parse_args()

							lowerCamelCase_						: Optional[int]         =  not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)








import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.bias''']

				for i in range(len(config.upsample_rates						) * len(config.resblock_kernel_sizes						)						):
								for j in range(len(config.resblock_dilation_sizes						)						):
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

												UpperCamelCase_:							Union[str, Any]	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
												UpperCamelCase_:							int	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

				UpperCamelCase_:							str	=						torch.load(lowerCamelCase						)
				load_weights(orig_checkpoint["""model"""]["""generator"""]     ,    lowerCamelCase     ,    lowerCamelCase						)

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[Any]	=						torch.from_numpy(lowerCamelCase						).float()

				model.save_pretrained(lowerCamelCase						)

				if repo_id:
								print("""Pushing to the hub..."""						)
								model.push_to_hub(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Union[str, Any]         =  argparse.ArgumentParser()
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
							parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							lowerCamelCase_						: Optional[int]         =  parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






