




"""simple docstring"""

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase  :			Union[str, Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Optional[int]        =       {
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	for attribute in key.split('''.'''	):
		A_  :				Dict	   =		getattr(_UpperCAmelCase  ,      _UpperCAmelCase	)

	if weight_type is not None:
		A_  :				Union[str, Any]	   =		getattr(_UpperCAmelCase  ,      _UpperCAmelCase	).shape
	else:
		A_  :				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":
		A_  :				Optional[Any]	   =		value
	elif weight_type == "weight_g":
		A_  :				str	   =		value
	elif weight_type == "weight_v":
		A_  :				List[Any]	   =		value
	elif weight_type == "bias":
		A_  :				str	   =		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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






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

	A_  :				List[str]	   =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[int]	   =		full_name.split('''conv_layers.'''	)[-1]
	A_  :				Any	   =		name.split('''.'''	)
	A_  :				Optional[int]	   =		int(items[0]	)
	A_  :				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."""
			)
			A_  :				Dict	   =		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[Any]	   =		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_  :				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(_UpperCAmelCase	)

@torch.no_grad()
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=None  ,      _UpperCAmelCase=None  ,      _UpperCAmelCase=True	):




	"""simple docstring"""






	if config_path is not None:
		A_  :				List[Any]	   =		HubertConfig.from_pretrained(_UpperCAmelCase	)
	else:
		A_  :				Optional[Any]	   =		HubertConfig()

	if is_finetuned:
		if dict_path:
			A_  :				Optional[Any]	   =		Dictionary.load(_UpperCAmelCase	)

			# 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_  :				List[str]	   =		target_dict.bos_index
			A_  :				int	   =		target_dict.eos_index
			A_  :				Union[str, Any]	   =		len(target_dict.symbols	)
			A_  :				str	   =		os.path.join(_UpperCAmelCase  ,      '''vocab.json'''	)
			if not os.path.isdir(_UpperCAmelCase	):
				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCAmelCase	)	)
				return
			os.makedirs(_UpperCAmelCase  ,      exist_ok=_UpperCAmelCase	)
			with open(_UpperCAmelCase  ,      '''w'''  ,      encoding='''utf-8'''	) as vocab_handle:
				json.dump(target_dict.indices  ,      _UpperCAmelCase	)
			A_  :				List[str]	   =		WavaVecaCTCTokenizer(
			    _UpperCAmelCase  ,      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=_UpperCAmelCase  ,      )
			A_  :				Optional[Any]	   =		True if config.feat_extract_norm == '''layer''' else False
			A_  :				Optional[Any]	   =		WavaVecaFeatureExtractor(
			    feature_size=1  ,      sampling_rate=1_6000  ,      padding_value=0  ,      do_normalize=_UpperCAmelCase  ,      return_attention_mask=_UpperCAmelCase  ,      )
			A_  :				List[Any]	   =		WavaVecaProcessor(feature_extractor=_UpperCAmelCase  ,      tokenizer=_UpperCAmelCase	)
			processor.save_pretrained(_UpperCAmelCase	)

		A_  :				Dict	   =		HubertForCTC(_UpperCAmelCase	)
	else:
		A_  :				str	   =		HubertModel(_UpperCAmelCase	)

	if is_finetuned:
		A_   , A_   , A_  :				Dict	   =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]  ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
	else:
		A_   , A_   , A_  :				List[str]	   =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

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

	recursively_load_weights(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

	hf_wavavec.save_pretrained(_UpperCAmelCase	)


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       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'
		)
		_lowerCamelCase  :			Union[str, Any]        =       parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)











"""simple docstring"""

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_lowerCamelCase  :			List[Any]        =       'Create a default config file for Accelerate with only a few flags set.'

def 		lowercase_      (		_UpperCAmelCase="no"  ,      _UpperCAmelCase = default_json_config_file  ,      _UpperCAmelCase = False	):




	"""simple docstring"""






	A_  :				int	   =		Path(_UpperCAmelCase	)
	path.parent.mkdir(parents=_UpperCAmelCase  ,      exist_ok=_UpperCAmelCase	)
	if path.exists():
		print(
		    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
		return False
	A_  :				List[Any]	   =		mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
	A_  :				Tuple	   =		{
	    '''compute_environment''': '''LOCAL_MACHINE''',
	    '''mixed_precision''': mixed_precision,
	}
	if torch.cuda.is_available():
		A_  :				Optional[Any]	   =		torch.cuda.device_count()
		A_  :				Optional[Any]	   =		num_gpus
		A_  :				int	   =		False
		if num_gpus > 1:
			A_  :				List[str]	   =		'''MULTI_GPU'''
		else:
			A_  :				Optional[int]	   =		'''NO'''
	elif is_xpu_available() and use_xpu:
		A_  :				Union[str, Any]	   =		torch.xpu.device_count()
		A_  :				Dict	   =		num_xpus
		A_  :				List[str]	   =		False
		if num_xpus > 1:
			A_  :				str	   =		'''MULTI_XPU'''
		else:
			A_  :				str	   =		'''NO'''
	elif is_npu_available():
		A_  :				Tuple	   =		torch.npu.device_count()
		A_  :				Tuple	   =		num_npus
		A_  :				List[str]	   =		False
		if num_npus > 1:
			A_  :				Any	   =		'''MULTI_NPU'''
		else:
			A_  :				List[Any]	   =		'''NO'''
	else:
		A_  :				List[Any]	   =		0
		A_  :				Dict	   =		True
		A_  :				Tuple	   =		1
		A_  :				int	   =		'''NO'''
	A_  :				Union[str, Any]	   =		ClusterConfig(**_UpperCAmelCase	)
	config.to_json_file(_UpperCAmelCase	)
	return path

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		parser.add_parser('''default'''  ,      parents=_UpperCAmelCase  ,      help=_UpperCAmelCase  ,      formatter_class=_UpperCAmelCase	)
	parser.add_argument(
	    '''--config_file'''  ,      default=_UpperCAmelCase  ,      help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )  ,      dest='''save_location'''  ,      )

	parser.add_argument(
	    '''--mixed_precision'''  ,      choices=['''no''', '''fp16''', '''bf16''']  ,      type=_UpperCAmelCase  ,      help='''Whether or not to use mixed precision training. '''
	    '''Choose between FP16 and BF16 (bfloat16) training. '''
	    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''  ,      default='''no'''  ,      )
	parser.set_defaults(func=_UpperCAmelCase	)
	return parser

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Dict	   =		write_basic_config(args.mixed_precision  ,      args.save_location	)
	if config_file:
		print(f"""accelerate configuration saved at {config_file}"""	)





