



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()
snake_case    :						str  =						logging.get_logger(__name__)

snake_case    :						str  =						{
    '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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Tuple:







						"""simple docstring"""

						for attribute in key.split("."       ):
												A__	:							Tuple						    = getattr(__lowercase							,     __lowercase       )

						if weight_type is not None:
												A__	:							int						    = getattr(__lowercase							,     __lowercase       ).shape
						else:
												A__	:							List[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":
												A__	:							Tuple						    = value
						elif weight_type == "weight_g":
												A__	:							Union[str, Any]						    = value
						elif weight_type == "weight_v":
												A__	:							Optional[Any]						    = value
						elif weight_type == "bias":
												A__	:							Union[str, Any]						    = value
						else:
												A__	:							Optional[Any]						    = value

						logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Optional[Any]:







						"""simple docstring"""

						A__	:							List[str]						    = []
						A__	:							Union[str, Any]						    = fairseq_model.state_dict()

						A__	:							int						    = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												A__	:							Optional[Any]						    = False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     hf_model.config.feat_extract_norm == "group"							,     )
																		A__	:							Tuple						    = True
												else:
																		for key, mapped_key in MAPPING.items():
																								A__	:							Dict						    = "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]:
																														A__	:							int						    = True
																														if "*" in mapped_key:
																																				A__	:							Optional[Any]						    = name.split(__lowercase       )[0].split("."       )[-2]
																																				A__	:							Any						    = mapped_key.replace("*"							,     __lowercase       )
																														if "weight_g" in name:
																																				A__	:							List[Any]						    = "weight_g"
																														elif "weight_v" in name:
																																				A__	:							Optional[int]						    = "weight_v"
																														elif "weight" in name:
																																				A__	:							Tuple						    = "weight"
																														elif "bias" in name:
																																				A__	:							Tuple						    = "bias"
																														else:
																																				A__	:							Optional[Any]						    = None
																														set_recursively(__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																								continue
												if not is_used:
																		unused_weights.append(__lowercase       )

						logger.warning(F'Unused weights: {unused_weights}'       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Optional[Any]						    = full_name.split("conv_layers."       )[-1]
						A__	:							Tuple						    = name.split("."       )
						A__	:							str						    = int(items[0]       )
						A__	:							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.'
																		)
																		A__	:							List[str]						    = 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.'
																		)
																		A__	:							Optional[int]						    = 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."
																		)
																		A__	:							Union[str, Any]						    = 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.'
																		)
																		A__	:							int						    = value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
						else:
												unused_weights.append(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Optional[Any]:







						"""simple docstring"""

						A__	:							Optional[Any]						    = SEWConfig()
						if is_finetuned:
												A__	:							Union[str, Any]						    = model.wav_encoder.wav_model.cfg
						else:
												A__	:							Optional[Any]						    = model.cfg

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

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

						A__	:							Any						    = "Wav2Vec2FeatureExtractor"
						A__	:							List[str]						    = "Wav2Vec2CTCTokenizer"

						return config
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase=None							,     __lowercase=None							,     __lowercase=True       )		->		str:







						"""simple docstring"""

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

						if config_path is not None:
												A__	:							Union[str, Any]						    = SEWConfig.from_pretrained(__lowercase       )
						else:
												A__	:							Tuple						    = convert_config(model[0]							,     __lowercase       )
						A__	:							Tuple						    = model[0].eval()

						A__	:							Optional[int]						    = True if config.feat_extract_norm == "layer" else False
						A__	:							List[str]						    = WavaVecaFeatureExtractor(
						    feature_size=1							,     sampling_rate=1_6_0_0_0							,     padding_value=0							,     do_normalize=__lowercase							,     return_attention_mask=__lowercase							,     )

						if is_finetuned:
												if dict_path:
																		A__	:							Optional[Any]						    = Dictionary.load(__lowercase       )

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

												A__	:							Any						    = SEWForCTC(__lowercase       )
						else:
												A__	:							Optional[int]						    = SEWModel(__lowercase       )
												feature_extractor.save_pretrained(__lowercase       )

						recursively_load_weights(__lowercase							,     __lowercase							,     __lowercase       )

						hf_model.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						str  =						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'
		)
		snake_case    :						str  =						parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

snake_case    :						Optional[int]  =						get_logger()

snake_case    :						Optional[dict]  =						None




class 							lowerCAmelCase__  (	TensorFormatter[Mapping, 'jax.Array', Mapping]						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Optional[int]=None			,		_A      :     str=None			,		**_A      :     Tuple):
											super().__init__(features=_A)
											import jax
											from jaxlib.xla_client import Device

											if isinstance(_A			,		_A):
																	raise ValueError(
																	    F'Expected {device} to be a `str` not {type(_A)}, as `jaxlib.xla_extension.Device` '
																	    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
																	    "the device with `str()` to get its string identifier that will be internally mapped "
																	    "to the actual `jaxlib.xla_extension.Device`.")
											A__	:							Union[str, Any]						    = device if isinstance(_A			,		_A) else str(jax.devices()[0])
											# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
											# with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Any						    = self._map_devices_to_str()
											if self.device not in list(DEVICE_MAPPING.keys()):
																	logger.warning(
																	    F'Device with string identifier {self.device} not listed among the available '
																	    F'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
																	    F'device: {str(jax.devices()[0])}.')
																	A__	:							str						    = str(jax.devices()[0])
											A__	:							List[Any]						    = jnp_array_kwargs







					@staticmethod
					def  _lowercase			(       ):
											import jax

											return {str(_A): device for device in jax.devices()}







					def  _lowercase			(       self      :     Tuple			,		_A      :     int):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		_A) and column:
																	if all(
																	    isinstance(_A			,		jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
																							return jnp.stack(_A			,		axis=0)
											return column







					def  _lowercase			(       self      :     List[str]			,		_A      :     Union[str, Any]):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		(str, bytes, type(_A))):
																	return value
											elif isinstance(_A			,		(np.character, np.ndarray)) and np.issubdtype(value.dtype			,		np.character):
																	return value.tolist()

											A__	:							Dict						    = {}

											if isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.integer):
																	# the default int precision depends on the jax config
																	# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																	if jax.config.jax_enable_xaa:
																							A__	:							Union[str, Any]						    = {"dtype": jnp.intaa}
																	else:
																							A__	:							int						    = {"dtype": jnp.intaa}
											elif isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.floating):
																	A__	:							str						    = {"dtype": jnp.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(_A			,		PIL.Image.Image):
																							A__	:							Any						    = np.asarray(_A)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Optional[Any]						    = self._map_devices_to_str()

											with jax.default_device(DEVICE_MAPPING[self.device]):
																	# calling jnp.array on a np.ndarray does copy the data
																	# see https://github.com/google/jax/issues/4486
																	return jnp.array(_A			,		**{**default_dtype, **self.jnp_array_kwargs})







					def  _lowercase			(       self      :     Dict			,		_A      :     Any):
											import jax

											# support for torch, tf, jax etc.
											if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(_A			,		torch.Tensor):
																							return self._tensorize(data_struct.detach().cpu().numpy()[()])
											if hasattr(_A			,		"__array__") and not isinstance(_A			,		jax.Array):
																	A__	:							Any						    = data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_A			,		np.ndarray):
																	if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																							return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											elif isinstance(_A			,		(list, tuple)):
																	return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											return self._tensorize(_A)







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     dict):
											return map_nested(self._recursive_tensorize			,		_A			,		map_list=_A)







					def  _lowercase			(       self      :     List[str]			,		_A      :     pa.Table):
											A__	:							Dict						    = self.numpy_arrow_extractor().extract_row(_A)
											A__	:							int						    = self.python_features_decoder.decode_row(_A)
											return self.recursive_tensorize(_A)







					def  _lowercase			(       self      :     int			,		_A      :     pa.Table):
											A__	:							int						    = self.numpy_arrow_extractor().extract_column(_A)
											A__	:							Optional[int]						    = self.python_features_decoder.decode_column(_A			,		pa_table.column_names[0])
											A__	:							Dict						    = self.recursive_tensorize(_A)
											A__	:							List[Any]						    = self._consolidate(_A)
											return column







					def  _lowercase			(       self      :     Tuple			,		_A      :     pa.Table):
											A__	:							Any						    = self.numpy_arrow_extractor().extract_batch(_A)
											A__	:							str						    = self.python_features_decoder.decode_batch(_A)
											A__	:							Optional[Any]						    = self.recursive_tensorize(_A)
											for column_name in batch:
																	A__	:							Optional[int]						    = self._consolidate(batch[column_name])
											return batch