'''simple docstring'''




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   :	List[Any] 							=   logging.get_logger(__name__)

_a   :	Optional[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",
    "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   :	List[str] 							=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       for attribute in key.split("."						):
              __UpperCAmelCase  :				Any       				=		getattr(lowerCamelCase__							,    lowerCamelCase__						)

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

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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		[]
       __UpperCAmelCase  :				List[Any]       				=		fairseq_model.state_dict()

       __UpperCAmelCase  :				Union[str, Any]       				=		hf_model.feature_extractor
       __UpperCAmelCase  :				Dict       				=		hf_model.adapter

       for name, value in fairseq_dict.items():
              __UpperCAmelCase  :				str       				=		False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    hf_model.config.feat_extract_norm == "group"							,    )
                     __UpperCAmelCase  :				int       				=		True
              elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]						):
                     load_adapter(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
                     __UpperCAmelCase  :				int       				=		True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
                                   __UpperCAmelCase  :				Union[str, Any]       				=		True
                                   if "*" in mapped_key:
                                          __UpperCAmelCase  :				int       				=		name.split(lowerCamelCase__						)[0].split("."						)[-2]
                                          __UpperCAmelCase  :				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:
                                          __UpperCAmelCase  :				Optional[Any]       				=		"weight"
                                   else:
                                          __UpperCAmelCase  :				Dict       				=		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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       str:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		full_name.split("adaptor."						)[-1]
       __UpperCAmelCase  :				Optional[int]       				=		name.split("."						)

       if items[1].isdigit():
              __UpperCAmelCase  :				Union[str, Any]       				=		int(items[1]						)
       else:
              __UpperCAmelCase  :				Optional[int]       				=		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."""
                            __UpperCAmelCase  :				Optional[Any]       				=		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."""
                            __UpperCAmelCase  :				List[Any]       				=		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."""
                            __UpperCAmelCase  :				str       				=		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."""
                            __UpperCAmelCase  :				List[str]       				=		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."""
                     __UpperCAmelCase  :				List[Any]       				=		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."""
                     __UpperCAmelCase  :				Dict       				=		value
                     logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
       else:
              unused_weights.append(lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		emb.weight.shape
       __UpperCAmelCase  :				str       				=		nn.Linear(lowerCamelCase__							,    lowerCamelCase__							,    bias=lowerCamelCase__						)
       __UpperCAmelCase  :				Dict       				=		emb.weight.data
       return lin_layer

@torch.no_grad()
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		WavaVecaConfig.from_pretrained(
           lowerCamelCase__							,    add_adapter=lowerCamelCase__							,    adapter_stride=lowerCamelCase__							,    adapter_kernel_size=lowerCamelCase__							,    use_auth_token=lowerCamelCase__							,    output_hidden_size=lowerCamelCase__							,    )
       __UpperCAmelCase  :				int       				=		MBartConfig.from_pretrained(lowerCamelCase__						)

       # load model
       __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		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,
           }							,    )
       __UpperCAmelCase  :				int       				=		model[0].eval()

       # load feature extractor
       __UpperCAmelCase  :				List[str]       				=		WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__							,    use_auth_token=lowerCamelCase__						)

       # set weights for wav2vec2 encoder
       __UpperCAmelCase  :				Tuple       				=		WavaVecaModel(lowerCamelCase__						)

       recursively_load_weights_wavaveca(model.encoder							,    lowerCamelCase__						)

       # load decoder weights
       __UpperCAmelCase  :				List[Any]       				=		MBartForCausalLM(lowerCamelCase__						)
       __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		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}"""						)

       __UpperCAmelCase  :				int       				=		SpeechEncoderDecoderModel(encoder=lowerCamelCase__							,    decoder=lowerCamelCase__						)
       __UpperCAmelCase  :				List[str]       				=		False

       __UpperCAmelCase  :				Union[str, Any]       				=		MBartaaTokenizer(lowerCamelCase__						)
       tokenizer.save_pretrained(lowerCamelCase__						)

       __UpperCAmelCase  :				Union[str, Any]       				=		hf_wavavec.config.to_dict()
       __UpperCAmelCase  :				int       				=		tokenizer.pad_token_id
       __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer.bos_token_id
       __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer.eos_token_id
       __UpperCAmelCase  :				Tuple       				=		"mbart50"
       __UpperCAmelCase  :				Tuple       				=		"wav2vec2"

       __UpperCAmelCase  :				int       				=		tokenizer.eos_token_id
       __UpperCAmelCase  :				Any       				=		25_0004
       __UpperCAmelCase  :				int       				=		tokenizer.eos_token_id

       __UpperCAmelCase  :				Union[str, Any]       				=		SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__						)

       hf_wavavec.save_pretrained(lowerCamelCase__						)
       feature_extractor.save_pretrained(lowerCamelCase__						)


if __name__ == "__main__":
   _a   :	Dict 							=   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=1024, type=int, help="encoder output dim")
   parser.add_argument("--start_token_id", default=250004, type=int, help="`decoder_start_token_id` of model config")

   _a   :	str 							=   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,
   )







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






