'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

_a   :	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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_a   :	Optional[int] 							=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

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





       """simple docstring"""


       for attribute in key.split("."						):
              if is_finetuned:
                     if attribute in ["quantizer", "project_q", "project_hid"]:
                            # those layers are only relevant for pretraining and should be dropped
                            return

                     if attribute == "ctc_proj":
                            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                            __UpperCAmelCase  :				Optional[int]       				=		"lm_head"

              __UpperCAmelCase  :				Any       				=		getattr(lowerCamelCase__							,    lowerCamelCase__						)

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





       """simple docstring"""


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

       __UpperCAmelCase  :				Optional[Any]       				=		hf_model.unispeech.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  :				int       				=		True
              else:
                     for key, mapped_key in MAPPING.items():
                            __UpperCAmelCase  :				Dict       				=		"unispeech." + 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  :				str       				=		True
                                   if "*" in mapped_key:
                                          __UpperCAmelCase  :				List[str]       				=		name.split(lowerCamelCase__						)[0].split("."						)[-2]
                                          __UpperCAmelCase  :				Dict       				=		mapped_key.replace("*"							,    lowerCamelCase__						)
                                   if "weight_g" in name:
                                          __UpperCAmelCase  :				Union[str, Any]       				=		"weight_g"
                                   elif "weight_v" in name:
                                          __UpperCAmelCase  :				Any       				=		"weight_v"
                                   elif "bias" in name:
                                          __UpperCAmelCase  :				List[Any]       				=		"bias"
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          __UpperCAmelCase  :				Tuple       				=		"weight"
                                   else:
                                          __UpperCAmelCase  :				Optional[Any]       				=		None
                                   set_recursively(lowerCamelCase__							,    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__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		full_name.split("conv_layers."						)[-1]
       __UpperCAmelCase  :				List[Any]       				=		name.split("."						)
       __UpperCAmelCase  :				Any       				=		int(items[0]						)
       __UpperCAmelCase  :				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  :				Optional[Any]       				=		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  :				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  :				List[str]       				=		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  :				Union[str, 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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    lowerCamelCase__=True						)					->       Union[str, Any]:





       """simple docstring"""


       if config_path is not None:
              __UpperCAmelCase  :				Tuple       				=		UniSpeechConfig.from_pretrained(lowerCamelCase__						)
       else:
              __UpperCAmelCase  :				Tuple       				=		UniSpeechConfig()

       if is_finetuned:
              if dict_path:
                     __UpperCAmelCase  :				str       				=		Dictionary.load_from_json(lowerCamelCase__						)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     __UpperCAmelCase  :				Optional[Any]       				=		target_dict.pad_index
                     __UpperCAmelCase  :				Optional[Any]       				=		target_dict.bos_index
                     __UpperCAmelCase  :				int       				=		target_dict.eos_index
                     __UpperCAmelCase  :				List[str]       				=		len(target_dict.symbols						)
                     __UpperCAmelCase  :				Optional[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  :				Optional[Any]       				=		target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     __UpperCAmelCase  :				Optional[Any]       				=		42
                     __UpperCAmelCase  :				int       				=		43
                     with open(lowerCamelCase__							,    "w"							,    encoding="utf-8"						) as vocab_handle:
                            json.dump(lowerCamelCase__							,    lowerCamelCase__						)
                     __UpperCAmelCase  :				str       				=		WavaVecaPhonemeCTCTokenizer(
                         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[Any]       				=		True if config.feat_extract_norm == "layer" else False
                     __UpperCAmelCase  :				Optional[int]       				=		WavaVecaFeatureExtractor(
                         feature_size=1							,    sampling_rate=1_6000							,    padding_value=0							,    do_normalize=lowerCamelCase__							,    return_attention_mask=lowerCamelCase__							,    )
                     __UpperCAmelCase  :				List[Any]       				=		WavaVecaProcessor(feature_extractor=lowerCamelCase__							,    tokenizer=lowerCamelCase__						)
                     processor.save_pretrained(lowerCamelCase__						)

              __UpperCAmelCase  :				Optional[Any]       				=		UniSpeechForCTC(lowerCamelCase__						)
       else:
              __UpperCAmelCase  :				Any       				=		UniSpeechForPreTraining(lowerCamelCase__						)

       if is_finetuned:
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]							,    arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						), "w2v_path": checkpoint_path}						)
       else:
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

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

       recursively_load_weights(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)

       hf_unispeech.save_pretrained(lowerCamelCase__						)


if __name__ == "__main__":
   _a   :	Tuple 							=   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"
   )
   _a   :	Optional[int] 							=   parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )







'''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()






