
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    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   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    List[str]							):




			"""simple docstring"""


			for attribute in key.split("."							):
						lowerCAmelCase_         =      getattr(__lowerCAmelCase       ,				__lowerCAmelCase							)

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

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      fairseq_model.state_dict()

			lowerCAmelCase_         =      hf_model.feature_extractor
			lowerCAmelCase_         =      hf_model.adapter

			for name, value in fairseq_dict.items():
						lowerCAmelCase_         =      False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				hf_model.config.feat_extract_norm == "group"       ,				)
									lowerCAmelCase_         =      True
						elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]							):
									load_adapter(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
									lowerCAmelCase_         =      True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
															lowerCAmelCase_         =      True
															if "*" in mapped_key:
																		lowerCAmelCase_         =      name.split(__lowerCAmelCase							)[0].split("."							)[-2]
																		lowerCAmelCase_         =      mapped_key.replace("*"       ,				__lowerCAmelCase							)
															if "weight_g" in name:
																		lowerCAmelCase_         =      "weight_g"
															elif "weight_v" in name:
																		lowerCAmelCase_         =      "weight_v"
															elif "bias" in name:
																		lowerCAmelCase_         =      "bias"
															elif "weight" in name:
																		lowerCAmelCase_         =      "weight"
															else:
																		lowerCAmelCase_         =      None
															set_recursively(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
												continue
						if not is_used:
									unused_weights.append(__lowerCAmelCase							)

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


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






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      full_name.split("adaptor."							)[-1]
			lowerCAmelCase_         =      name.split("."							)

			if items[1].isdigit():
						lowerCAmelCase_         =      int(items[1]							)
			else:
						lowerCAmelCase_         =      None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
												lowerCAmelCase_         =      value
												logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""							)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
												lowerCAmelCase_         =      value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
												lowerCAmelCase_         =      value
												logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""							)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
												lowerCAmelCase_         =      value
												logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""							)
			elif isinstance(__lowerCAmelCase       ,				__lowerCAmelCase							):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
									lowerCAmelCase_         =      value
									logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""							)
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
									lowerCAmelCase_         =      value
									logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""							)
			else:
						unused_weights.append(__lowerCAmelCase							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_      ,    lowerCAmelCase_         =      emb.weight.shape
			lowerCAmelCase_         =      nn.Linear(__lowerCAmelCase       ,				__lowerCAmelCase       ,				bias=__lowerCAmelCase							)
			lowerCAmelCase_         =      emb.weight.data
			return lin_layer






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    Dict       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      WavaVecaConfig.from_pretrained(
			    __lowerCAmelCase       ,				add_adapter=__lowerCAmelCase       ,				adapter_stride=__lowerCAmelCase       ,				adapter_kernel_size=__lowerCAmelCase       ,				use_auth_token=__lowerCAmelCase       ,				output_hidden_size=__lowerCAmelCase       ,				)
			lowerCAmelCase_         =      MBartConfig.from_pretrained(__lowerCAmelCase							)

			# load model
			lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]       ,				arg_overrides={
			        "config_yaml": config_yaml_path,
			        "data": "/".join(dict_path.split("/"							)[:-1]							),
			        "w2v_path": checkpoint_path,
			        "load_pretrained_decoder_from": None,
			    }       ,				)
			lowerCAmelCase_         =      model[0].eval()

			# load feature extractor
			lowerCAmelCase_         =      WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase       ,				use_auth_token=__lowerCAmelCase							)

			# set weights for wav2vec2 encoder
			lowerCAmelCase_         =      WavaVecaModel(__lowerCAmelCase							)

			recursively_load_weights_wavaveca(model.encoder       ,				__lowerCAmelCase							)

			# load decoder weights
			lowerCAmelCase_         =      MBartForCausalLM(__lowerCAmelCase							)
			lowerCAmelCase_      ,    lowerCAmelCase_         =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,				strict=__lowerCAmelCase							)
			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_         =      SpeechEncoderDecoderModel(encoder=__lowerCAmelCase       ,				decoder=__lowerCAmelCase							)
			lowerCAmelCase_         =      False

			lowerCAmelCase_         =      MBartaaTokenizer(__lowerCAmelCase							)
			tokenizer.save_pretrained(__lowerCAmelCase							)

			lowerCAmelCase_         =      hf_wavavec.config.to_dict()
			lowerCAmelCase_         =      tokenizer.pad_token_id
			lowerCAmelCase_         =      tokenizer.bos_token_id
			lowerCAmelCase_         =      tokenizer.eos_token_id
			lowerCAmelCase_         =      "mbart50"
			lowerCAmelCase_         =      "wav2vec2"

			lowerCAmelCase_         =      tokenizer.eos_token_id
			lowerCAmelCase_         =      250004
			lowerCAmelCase_         =      tokenizer.eos_token_id

			lowerCAmelCase_         =      SpeechEncoderDecoderConfig.from_dict(__lowerCAmelCase							)

			hf_wavavec.save_pretrained(__lowerCAmelCase							)
			feature_extractor.save_pretrained(__lowerCAmelCase							)


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("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
			parser.add_argument(
			    "--encoder_config_path",
			    default="facebook/wav2vec2-xls-r-1b",
			    type=str,
			    help="Path to hf encoder wav2vec2 checkpoint config",
			)
			parser.add_argument(
			    "--decoder_config_path",
			    default="facebook/mbart-large-50-one-to-many-mmt",
			    type=str,
			    help="Path to hf decoder checkpoint config",
			)
			parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
			parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
			parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
			parser.add_argument("--encoder_output_dim", default=10_24, type=int, help="encoder output dim")
			parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

			_A  =	parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



