



'''simple docstring'''





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

SCREAMING_SNAKE_CASE__      					=     ["names", "prefix"]
SCREAMING_SNAKE_CASE__      					=     ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
SCREAMING_SNAKE_CASE__      					=     ["encoding_errors", "on_bad_lines"]
SCREAMING_SNAKE_CASE__      					=     ["date_format"]


@dataclass
class 				snake_case (datasets.BuilderConfig   ):
       lowerCAmelCase__  :str						   =					","
       lowerCAmelCase__  :Any						   =					None
       lowerCAmelCase__  :Union[str, Any]						   =					"infer"
       lowerCAmelCase__  :Optional[Any]						   =					None
       lowerCAmelCase__  :List[Any]						   =					None
       lowerCAmelCase__  :List[str]						   =					None
       lowerCAmelCase__  :List[Any]						   =					None
       lowerCAmelCase__  :Dict						   =					None
       lowerCAmelCase__  :int						   =					True
       lowerCAmelCase__  :Optional[int]						   =					None
       lowerCAmelCase__  :List[Any]						   =					None
       lowerCAmelCase__  :Any						   =					None
       lowerCAmelCase__  :Union[str, Any]						   =					None
       lowerCAmelCase__  :Dict						   =					False
       lowerCAmelCase__  :str						   =					None
       lowerCAmelCase__  :Union[str, Any]						   =					None
       lowerCAmelCase__  :Optional[Any]						   =					None
       lowerCAmelCase__  :int						   =					True
       lowerCAmelCase__  :List[Any]						   =					True
       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Union[str, Any]						   =					True
       lowerCAmelCase__  :Union[str, Any]						   =					None
       lowerCAmelCase__  :Tuple						   =					"."
       lowerCAmelCase__  :Tuple						   =					None
       lowerCAmelCase__  :Optional[Any]						   =					"\""
       lowerCAmelCase__  :Tuple						   =					0
       lowerCAmelCase__  :List[Any]						   =					None
       lowerCAmelCase__  :Optional[Any]						   =					None
       lowerCAmelCase__  :Optional[int]						   =					None
       lowerCAmelCase__  :Dict						   =					None
       lowerCAmelCase__  :Any						   =					True
       lowerCAmelCase__  :Optional[Any]						   =					True
       lowerCAmelCase__  :Union[str, Any]						   =					0
       lowerCAmelCase__  :str						   =					True
       lowerCAmelCase__  :Any						   =					False
       lowerCAmelCase__  :str						   =					None
       lowerCAmelCase__  :Optional[Any]						   =					10000
       lowerCAmelCase__  :str						   =					None
       lowerCAmelCase__  :int						   =					"strict"
       lowerCAmelCase__  :Any						   =					"error"
       lowerCAmelCase__  :Any						   =					None



       def 							_a      (				self      )  ->							Any:
           if self.delimiter is not None:
               lowercase__		       =						self.delimiter
           if self.column_names is not None:
               lowercase__		       =						self.column_names







       @property
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						{
               "sep": self.sep,
               "header": self.header,
               "names": self.names,
               "index_col": self.index_col,
               "usecols": self.usecols,
               "prefix": self.prefix,
               "mangle_dupe_cols": self.mangle_dupe_cols,
               "engine": self.engine,
               "converters": self.converters,
               "true_values": self.true_values,
               "false_values": self.false_values,
               "skipinitialspace": self.skipinitialspace,
               "skiprows": self.skiprows,
               "nrows": self.nrows,
               "na_values": self.na_values,
               "keep_default_na": self.keep_default_na,
               "na_filter": self.na_filter,
               "verbose": self.verbose,
               "skip_blank_lines": self.skip_blank_lines,
               "thousands": self.thousands,
               "decimal": self.decimal,
               "lineterminator": self.lineterminator,
               "quotechar": self.quotechar,
               "quoting": self.quoting,
               "escapechar": self.escapechar,
               "comment": self.comment,
               "encoding": self.encoding,
               "dialect": self.dialect,
               "error_bad_lines": self.error_bad_lines,
               "warn_bad_lines": self.warn_bad_lines,
               "skipfooter": self.skipfooter,
               "doublequote": self.doublequote,
               "memory_map": self.memory_map,
               "float_precision": self.float_precision,
               "chunksize": self.chunksize,
               "encoding_errors": self.encoding_errors,
               "on_bad_lines": self.on_bad_lines,
               "date_format": self.date_format,
           }

           # some kwargs must not be passed if they don't have a default value
           # some others are deprecated and we can also not pass them if they are the default value
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
               if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()     ,_lowercase      ):
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
           if not (datasets.config.PANDAS_VERSION.major >= 2):
               for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
           if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
               for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

           return pd_read_csv_kwargs





class 				snake_case (datasets.ArrowBasedBuilder   ):
       lowerCAmelCase__  :Dict						   =					CsvConfig



       def 							_a      (				self      )  ->							Optional[int]:
           return datasets.DatasetInfo(features=self.config.features      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Optional[Any]:
           if not self.config.data_files:
               raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''      )
           lowercase__		       =						dl_manager.download_and_extract(self.config.data_files      )
           if isinstance(_lowercase     ,(str, list, tuple)      ):
               lowercase__		       =						data_files
               if isinstance(_lowercase     ,_lowercase      ):
                   lowercase__		       =						[files]
               lowercase__		       =						[dl_manager.iter_files(_lowercase      ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,gen_kwargs={"files": files}      )]
           lowercase__		       =						[]
           for split_name, files in data_files.items():
               if isinstance(_lowercase     ,_lowercase      ):
                   lowercase__		       =						[files]
               lowercase__		       =						[dl_manager.iter_files(_lowercase      ) for file in files]
               splits.append(datasets.SplitGenerator(name=_lowercase     ,gen_kwargs={"files": files}      )      )
           return splits



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
           if self.config.features is not None:
               lowercase__		       =						self.config.features.arrow_schema
               if all(not require_storage_cast(_lowercase      ) for feature in self.config.features.values()      ):
                   # cheaper cast
                   lowercase__		       =						pa.Table.from_arrays([pa_table[field.name] for field in schema]     ,schema=_lowercase      )
               else:
                   # more expensive cast; allows str <-> int/float or str to Audio for example
                   lowercase__		       =						table_cast(_lowercase     ,_lowercase      )
           return pa_table







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						self.config.features.arrow_schema if self.config.features else None
           # dtype allows reading an int column as str
           lowercase__		       =						(
               {
                   name: dtype.to_pandas_dtype() if not require_storage_cast(_lowercase      ) else object
                   for name, dtype, feature in zip(schema.names     ,schema.types     ,self.config.features.values()      )
               }
               if schema is not None
               else None
           )
           for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase      )      ):
               lowercase__		       =						pd.read_csv(_lowercase     ,iterator=_lowercase     ,dtype=_lowercase     ,**self.config.pd_read_csv_kwargs      )
               try:
                   for batch_idx, df in enumerate(_lowercase      ):
                       lowercase__		       =						pa.Table.from_pandas(_lowercase      )
                       # Uncomment for debugging (will print the Arrow table size and elements)
                       # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                       # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                       yield (file_idx, batch_idx), self._cast_table(_lowercase      )
               except ValueError as e:
                   logger.error(F'''Failed to read file \'{file}\' with error {type(_lowercase      )}: {e}'''      )
                   raise









'''simple docstring'''





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
}
SCREAMING_SNAKE_CASE__      					=     [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Any     ,_snake_case  :						Optional[int]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    for attribute in key.split("."       ):
        lowercase__		       =						getattr(_snake_case     ,_snake_case       )

    if weight_type is not None:
        lowercase__		       =						getattr(_snake_case     ,_snake_case       ).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        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						[]
    lowercase__		       =						fairseq_model.state_dict()

    lowercase__		       =						hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        lowercase__		       =						False
        if "conv_layers" in name:
            load_conv_layer(
                _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,hf_model.config.feat_extract_norm == "group"     ,)
            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(_snake_case       )[0].split("."       )[-2]
                        lowercase__		       =						mapped_key.replace("*"     ,_snake_case       )
                    if "weight_g" in name:
                        lowercase__		       =						"weight_g"
                    elif "weight_v" in name:
                        lowercase__		       =						"weight_v"
                    elif "bias" in name and "relative_attention_bias" not in name:
                        lowercase__		       =						"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowercase__		       =						"weight"
                    else:
                        lowercase__		       =						None

                    set_recursively(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                continue
        if not is_used:
            unused_weights.append(_snake_case       )

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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						int     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    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(_snake_case       )




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Any     ,_snake_case  :						List[Any]=None       ):





    '''simple docstring'''


    lowercase__		       =						torch.load(_snake_case       )
    lowercase__		       =						WavLMConfigOrig(checkpoint["cfg"]       )
    lowercase__		       =						WavLMOrig(_snake_case       )
    model.load_state_dict(checkpoint["model"]       )
    model.eval()

    if config_path is not None:
        lowercase__		       =						WavLMConfig.from_pretrained(_snake_case       )
    else:
        lowercase__		       =						WavLMConfig()

    lowercase__		       =						WavLMModel(_snake_case       )

    recursively_load_weights(_snake_case     ,_snake_case       )

    hf_wavlm.save_pretrained(_snake_case       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




