


'''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()
__lowercase     :						Dict	=     logging.get_logger(__name__)

__lowercase     :						Tuple	=     {
    '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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[int]						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					for attribute in key.split('.'       ):
										__a      :	Optional[int]    =  getattr(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					if weight_type is not None:
										__a      :	Any    =  getattr(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ).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      :	Union[str, Any]    =  value
					elif weight_type == "weight_g":
										__a      :	str    =  value
					elif weight_type == "weight_v":
										__a      :	Any    =  value
					elif weight_type == "bias":
										__a      :	int    =  value
					else:
										__a      :	Optional[int]    =  value

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       int       ):
					__a      :	Dict    =  []
					__a      :	Any    =  fairseq_model.state_dict()

					__a      :	Union[str, Any]    =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[int]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[int]       ):
					__a      :	Union[str, Any]    =  full_name.split('conv_layers.'       )[-1]
					__a      :	Dict    =  name.split('.'       )
					__a      :	Optional[Any]    =  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      :	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      :	Dict    =  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      :	Optional[Any]    =  value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
					else:
										unused_weights.append(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       int       ):
					__a      :	Optional[Any]    =  SEWConfig()
					if is_finetuned:
										__a      :	Optional[int]    =  model.wav_encoder.wav_model.cfg
					else:
										__a      :	List[str]    =  model.cfg

					__a      :	Any    =  fs_config.conv_bias
					__a      :	Optional[Any]    =  eval(fs_config.conv_feature_layers       )
					__a      :	int    =  [x[0] for x in conv_layers]
					__a      :	str    =  [x[1] for x in conv_layers]
					__a      :	Optional[Any]    =  [x[2] for x in conv_layers]
					__a      :	Optional[int]    =  'gelu'
					__a      :	List[Any]    =  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
					__a      :	Optional[Any]    =  0.0
					__a      :	List[Any]    =  fs_config.activation_fn.name
					__a      :	Any    =  fs_config.encoder_embed_dim
					__a      :	Tuple    =  0.0_2
					__a      :	Optional[Any]    =  fs_config.encoder_ffn_embed_dim
					__a      :	Tuple    =  1e-5
					__a      :	int    =  fs_config.encoder_layerdrop
					__a      :	List[str]    =  fs_config.encoder_attention_heads
					__a      :	Optional[int]    =  fs_config.conv_pos_groups
					__a      :	Optional[Any]    =  fs_config.conv_pos
					__a      :	List[Any]    =  len(_SCREAMING_SNAKE_CASE       )
					__a      :	Optional[int]    =  fs_config.encoder_layers
					__a      :	Optional[Any]    =  fs_config.squeeze_factor

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

					__a      :	str    =  'Wav2Vec2FeatureExtractor'
					__a      :	Any    =  'Wav2Vec2CTCTokenizer'

					return config






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       str=None						,				_SCREAMING_SNAKE_CASE				:       int=None						,				_SCREAMING_SNAKE_CASE				:       str=True       ):

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

					if config_path is not None:
										__a      :	Dict    =  SEWConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
					else:
										__a      :	List[str]    =  convert_config(model[0]						,				_SCREAMING_SNAKE_CASE       )
					__a      :	int    =  model[0].eval()

					__a      :	List[str]    =  True if config.feat_extract_norm == 'layer' else False
					__a      :	Tuple    =  WavaVecaFeatureExtractor(
					    feature_size=1						,				sampling_rate=16_000						,				padding_value=0						,				do_normalize=_SCREAMING_SNAKE_CASE						,				return_attention_mask=_SCREAMING_SNAKE_CASE						,				)

					if is_finetuned:
										if dict_path:
															__a      :	Any    =  Dictionary.load(_SCREAMING_SNAKE_CASE       )

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

										__a      :	Any    =  SEWForCTC(_SCREAMING_SNAKE_CASE       )
					else:
										__a      :	str    =  SEWModel(_SCREAMING_SNAKE_CASE       )
										feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE       )

					recursively_load_weights(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
							__lowercase     :						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'
							)
							__lowercase     :						str	=     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'''



from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')



