


'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__A		={
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):

			for attribute in key.split("""."""   ):
						UpperCAmelCase__					:   Optional[Any]       				=       getattr(a__	, a__   )

			if weight_type is not None:
						UpperCAmelCase__					:   Optional[int]       				=       getattr(a__	, a__   ).shape
			else:
						UpperCAmelCase__					:   Optional[int]       				=       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__					:   Tuple       				=       value
			else:
						UpperCAmelCase__					:   Tuple       				=       value

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[Any]       				=       []
			UpperCAmelCase__					:   Any       				=       fairseq_model.state_dict()

			UpperCAmelCase__					:   Dict       				=       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCAmelCase__					:   int       				=       False
						if "conv_layers" in name:
									load_conv_layer(
									    a__	, a__	, a__	, a__	, hf_model.config.feat_extract_norm == """group"""	, )
									UpperCAmelCase__					:   Any       				=       True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCAmelCase__					:   List[str]       				=       """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

												if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
															UpperCAmelCase__					:   List[Any]       				=       True
															if "*" in mapped_key:
																		UpperCAmelCase__					:   str       				=       name.split(a__   )[0].split("""."""   )[-2]
																		UpperCAmelCase__					:   int       				=       mapped_key.replace("""*"""	, a__   )
															if "weight_g" in name:
																		UpperCAmelCase__					:   List[str]       				=       """weight_g"""
															elif "weight_v" in name:
																		UpperCAmelCase__					:   Optional[Any]       				=       """weight_v"""
															elif "weight" in name:
																		UpperCAmelCase__					:   List[str]       				=       """weight"""
															elif "bias" in name:
																		UpperCAmelCase__					:   Optional[Any]       				=       """bias"""
															else:
																		UpperCAmelCase__					:   Optional[Any]       				=       None
															set_recursively(a__	, a__	, a__	, a__	, a__   )
												continue
						if not is_used:
									unused_weights.append(a__   )

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







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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       SEWConfig()
			if is_finetuned:
						UpperCAmelCase__					:   int       				=       model.wav_encoder.wav_model.cfg
			else:
						UpperCAmelCase__					:   Optional[Any]       				=       model.cfg

			UpperCAmelCase__					:   List[Any]       				=       fs_config.conv_bias
			UpperCAmelCase__					:   str       				=       eval(fs_config.conv_feature_layers   )
			UpperCAmelCase__					:   List[Any]       				=       [x[0] for x in conv_layers]
			UpperCAmelCase__					:   List[Any]       				=       [x[1] for x in conv_layers]
			UpperCAmelCase__					:   int       				=       [x[2] for x in conv_layers]
			UpperCAmelCase__					:   Dict       				=       """gelu"""
			UpperCAmelCase__					:   Tuple       				=       """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
			UpperCAmelCase__					:   int       				=       0.0
			UpperCAmelCase__					:   Optional[Any]       				=       fs_config.activation_fn.name
			UpperCAmelCase__					:   Dict       				=       fs_config.encoder_embed_dim
			UpperCAmelCase__					:   Tuple       				=       0.02
			UpperCAmelCase__					:   Tuple       				=       fs_config.encoder_ffn_embed_dim
			UpperCAmelCase__					:   List[Any]       				=       1e-5
			UpperCAmelCase__					:   int       				=       fs_config.encoder_layerdrop
			UpperCAmelCase__					:   Tuple       				=       fs_config.encoder_attention_heads
			UpperCAmelCase__					:   Optional[int]       				=       fs_config.conv_pos_groups
			UpperCAmelCase__					:   Tuple       				=       fs_config.conv_pos
			UpperCAmelCase__					:   int       				=       len(a__   )
			UpperCAmelCase__					:   int       				=       fs_config.encoder_layers
			UpperCAmelCase__					:   Union[str, Any]       				=       fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						UpperCAmelCase__					:   Optional[int]       				=       model.cfg
						UpperCAmelCase__					:   Optional[int]       				=       fs_config.final_dropout
						UpperCAmelCase__					:   List[Any]       				=       fs_config.layerdrop
			UpperCAmelCase__					:   str       				=       fs_config.activation_dropout
			UpperCAmelCase__					:   List[Any]       				=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			UpperCAmelCase__					:   Optional[int]       				=       fs_config.attention_dropout
			UpperCAmelCase__					:   Union[str, Any]       				=       fs_config.dropout_input
			UpperCAmelCase__					:   Tuple       				=       fs_config.dropout
			UpperCAmelCase__					:   Union[str, Any]       				=       fs_config.mask_channel_length
			UpperCAmelCase__					:   Tuple       				=       fs_config.mask_channel_prob
			UpperCAmelCase__					:   Any       				=       fs_config.mask_length
			UpperCAmelCase__					:   Tuple       				=       fs_config.mask_prob

			UpperCAmelCase__					:   str       				=       """Wav2Vec2FeatureExtractor"""
			UpperCAmelCase__					:   int       				=       """Wav2Vec2CTCTokenizer"""

			return config







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, UpperCamelCase__=None	, UpperCamelCase__=True   ):

			if is_finetuned:
						UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   int       				=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]	, arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
			else:
						UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

			if config_path is not None:
						UpperCAmelCase__					:   str       				=       SEWConfig.from_pretrained(a__   )
			else:
						UpperCAmelCase__					:   Any       				=       convert_config(model[0]	, a__   )
			UpperCAmelCase__					:   int       				=       model[0].eval()

			UpperCAmelCase__					:   Union[str, Any]       				=       True if config.feat_extract_norm == """layer""" else False
			UpperCAmelCase__					:   Any       				=       WavaVecaFeatureExtractor(
			    feature_size=1	, sampling_rate=1_6_0_0_0	, padding_value=0	, do_normalize=a__	, return_attention_mask=a__	, )

			if is_finetuned:
						if dict_path:
									UpperCAmelCase__					:   Optional[Any]       				=       Dictionary.load(a__   )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									UpperCAmelCase__					:   Union[str, Any]       				=       target_dict.pad_index
									UpperCAmelCase__					:   Optional[Any]       				=       target_dict.bos_index
									UpperCAmelCase__					:   int       				=       target_dict.pad_index
									UpperCAmelCase__					:   Optional[int]       				=       target_dict.bos_index
									UpperCAmelCase__					:   Dict       				=       target_dict.eos_index
									UpperCAmelCase__					:   Dict       				=       len(target_dict.symbols   )
									UpperCAmelCase__					:   Optional[int]       				=       os.path.join(a__	, """vocab.json"""   )
									if not os.path.isdir(a__   ):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(a__   )   )
												return
									os.makedirs(a__	, exist_ok=a__   )
									with open(a__	, """w"""	, encoding="""utf-8"""   ) as vocab_handle:
												json.dump(target_dict.indices	, a__   )
									UpperCAmelCase__					:   Tuple       				=       WavaVecaCTCTokenizer(
									    a__	, 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=a__	, )
									UpperCAmelCase__					:   str       				=       WavaVecaProcessor(feature_extractor=a__	, tokenizer=a__   )
									processor.save_pretrained(a__   )

						UpperCAmelCase__					:   Dict       				=       SEWForCTC(a__   )
			else:
						UpperCAmelCase__					:   List[Any]       				=       SEWModel(a__   )
						feature_extractor.save_pretrained(a__   )

			recursively_load_weights(a__	, a__	, a__   )

			hf_model.save_pretrained(a__   )


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_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		__A		=parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)



'''simple docstring'''



import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


__A		,	__A		,	__A		=False, False, False


@dataclass
class 						_snake_case   :
					lowerCAmelCase :Optional[int]						=   None
					lowerCAmelCase :bool						=   True
					lowerCAmelCase :bool						=   True
					lowerCAmelCase :Optional[str]						=   None
					# Automatically constructed
					lowerCAmelCase :ClassVar[str]						=   "dict"
					lowerCAmelCase :ClassVar[Any]						=   pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}		)
					lowerCAmelCase :str						=   field(default='''Audio'''    ,				init=a__    ,				repr=a__		)




					def __call__( self):
								return self.pa_type




					def 			snake_case__  ( self							,							_lowerCamelCase):
								try:
											import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
								except ImportError as err:
											raise ImportError("""To support encoding audio data, please install 'soundfile'.""") from err
								if isinstance(_lowerCamelCase							,							_lowerCamelCase):
											return {"bytes": None, "path": value}
								elif isinstance(_lowerCamelCase							,							_lowerCamelCase):
											return {"bytes": value, "path": None}
								elif "array" in value:
											# convert the audio array to wav bytes
											UpperCAmelCase__					:   Optional[int]       				=       BytesIO()
											sf.write(_lowerCamelCase							,							value["""array"""]							,							value["""sampling_rate"""]							,							format="""wav""")
											return {"bytes": buffer.getvalue(), "path": None}
								elif value.get("""path""") is not None and os.path.isfile(value["""path"""]):
											# we set "bytes": None to not duplicate the data if they're already available locally
											if value["path"].endswith("""pcm"""):
														# "PCM" only has raw audio bytes
														if value.get("""sampling_rate""") is None:
																	# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																	raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object""")
														if value.get("""bytes"""):
																	# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																	UpperCAmelCase__					:   Tuple       				=       np.frombuffer(value["""bytes"""]							,							dtype=np.intaa).astype(np.floataa) / 3_2767
														else:
																	UpperCAmelCase__					:   List[str]       				=       np.memmap(value["""path"""]							,							dtype="""h"""							,							mode="""r""").astype(np.floataa) / 3_2767

														UpperCAmelCase__					:   List[str]       				=       BytesIO(bytes())
														sf.write(_lowerCamelCase							,							_lowerCamelCase							,							value["""sampling_rate"""]							,							format="""wav""")
														return {"bytes": buffer.getvalue(), "path": None}
											else:
														return {"bytes": None, "path": value.get("""path""")}
								elif value.get("""bytes""") is not None or value.get("""path""") is not None:
											# store the audio bytes, and path is used to infer the audio format using the file extension
											return {"bytes": value.get("""bytes"""), "path": value.get("""path""")}
								else:
											raise ValueError(
											    f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''')




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not self.decode:
											raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""")

								UpperCAmelCase__				,    UpperCAmelCase__					:   Tuple       				=       (value["""path"""], BytesIO(value["""bytes"""])) if value["""bytes"""] is not None else (value["""path"""], None)
								if path is None and file is None:
											raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''')

								try:
											import librosa
											import soundfile as sf
								except ImportError as err:
											raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'.""") from err

								UpperCAmelCase__					:   Dict       				=       xsplitext(_lowerCamelCase)[1][1:].lower() if path is not None else None
								if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
											raise RuntimeError(
											    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """)
								elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
											raise RuntimeError(
											    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """)

								if file is None:
											UpperCAmelCase__					:   int       				=       token_per_repo_id or {}
											UpperCAmelCase__					:   Optional[int]       				=       path.split("""::""")[-1]
											try:
														UpperCAmelCase__					:   Dict       				=       string_to_dict(_lowerCamelCase							,							config.HUB_DATASETS_URL)["""repo_id"""]
														UpperCAmelCase__					:   Dict       				=       token_per_repo_id[repo_id]
											except (ValueError, KeyError):
														UpperCAmelCase__					:   List[Any]       				=       None

											with xopen(_lowerCamelCase							,							"""rb"""							,							use_auth_token=_lowerCamelCase) as f:
														UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       sf.read(_lowerCamelCase)

								else:
											UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       sf.read(_lowerCamelCase)

								UpperCAmelCase__					:   str       				=       array.T
								if self.mono:
											UpperCAmelCase__					:   List[Any]       				=       librosa.to_mono(_lowerCamelCase)
								if self.sampling_rate and self.sampling_rate != sampling_rate:
											UpperCAmelCase__					:   int       				=       librosa.resample(_lowerCamelCase							,							orig_sr=_lowerCamelCase							,							target_sr=self.sampling_rate)
											UpperCAmelCase__					:   Tuple       				=       self.sampling_rate

								return {"path": path, "array": array, "sampling_rate": sampling_rate}




					def 			snake_case__  ( self):
								from .features import Value

								if self.decode:
											raise ValueError("""Cannot flatten a decoded Audio feature.""")
								return {
								    "bytes": Value("""binary"""),
								    "path": Value("""string"""),
								}




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if pa.types.is_string(storage.type):
											UpperCAmelCase__					:   Dict       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.binary())
											UpperCAmelCase__					:   Tuple       				=       pa.StructArray.from_arrays([bytes_array, storage]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								elif pa.types.is_binary(storage.type):
											UpperCAmelCase__					:   Optional[int]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.string())
											UpperCAmelCase__					:   str       				=       pa.StructArray.from_arrays([storage, path_array]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices("""array"""):
											UpperCAmelCase__					:   Optional[Any]       				=       pa.array([Audio().encode_example(_lowerCamelCase) if x is not None else None for x in storage.to_pylist()])
								elif pa.types.is_struct(storage.type):
											if storage.type.get_field_index("""bytes""") >= 0:
														UpperCAmelCase__					:   int       				=       storage.field("""bytes""")
											else:
														UpperCAmelCase__					:   List[str]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.binary())
											if storage.type.get_field_index("""path""") >= 0:
														UpperCAmelCase__					:   List[Any]       				=       storage.field("""path""")
											else:
														UpperCAmelCase__					:   Optional[int]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.string())
											UpperCAmelCase__					:   Optional[int]       				=       pa.StructArray.from_arrays([bytes_array, path_array]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								return array_cast(_lowerCamelCase							,							self.pa_type)




					def 			snake_case__  ( self							,							_lowerCamelCase):

								@no_op_if_value_is_null
								def path_to_bytes(_lowerCamelCase):
											with xopen(_lowerCamelCase							,							"""rb""") as f:
														UpperCAmelCase__					:   int       				=       f.read()
											return bytes_

								UpperCAmelCase__					:   Optional[Any]       				=       pa.array(
								    [
								        (path_to_bytes(x["""path"""]) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
								        for x in storage.to_pylist()
								    ]							,							type=pa.binary()							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       pa.array(
								    [os.path.basename(_lowerCamelCase) if path is not None else None for path in storage.field("""path""").to_pylist()]							,							type=pa.string()							,							)
								UpperCAmelCase__					:   List[str]       				=       pa.StructArray.from_arrays([bytes_array, path_array]							,							["""bytes""", """path"""]							,							mask=bytes_array.is_null())
								return array_cast(_lowerCamelCase							,							self.pa_type)