






"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__A		:       Any  				=		{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__A		:       Optional[Any]  				=		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 						lowercase    (				__snake_case				: List[str]		,					__snake_case				: List[Any]		,					__snake_case				: Tuple		,					__snake_case				: Dict		,					__snake_case				: Tuple	):
		for attribute in key.split('''.'''	):
				lowercase_ : Union[str, Any]							       =   getattr(__snake_case		,					__snake_case	)

		if weight_type is not None:
				lowercase_ : Any							       =   getattr(__snake_case		,					__snake_case	).shape
		else:
				lowercase_ : Union[str, Any]							       =   hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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_ : int							       =   value
		elif weight_type == "weight_g":
				lowercase_ : Tuple							       =   value
		elif weight_type == "weight_v":
				lowercase_ : Optional[Any]							       =   value
		elif weight_type == "bias":
				lowercase_ : List[Any]							       =   value
		elif weight_type == "running_mean":
				lowercase_ : Optional[Any]							       =   value
		elif weight_type == "running_var":
				lowercase_ : List[Any]							       =   value
		elif weight_type == "num_batches_tracked":
				lowercase_ : Optional[Any]							       =   value
		elif weight_type == "inv_freq":
				lowercase_ : List[Any]							       =   value
		else:
				lowercase_ : Union[str, Any]							       =   value

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



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Union[str, Any]		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[int]							       =   []
		lowercase_ : List[Any]							       =   fairseq_model.state_dict()

		lowercase_ : Optional[int]							       =   hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase_ : int							       =   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_ : Any							       =   True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase_ : Optional[int]							       =   '''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
										lowercase_ : List[str]							       =   True
										if "*" in mapped_key:
												lowercase_ : int							       =   name.split(__snake_case	)[0].split('''.'''	)[-2]
												lowercase_ : Any							       =   mapped_key.replace('''*'''		,					__snake_case	)
										if "pos_bias_u" in name:
												lowercase_ : List[Any]							       =   None
										elif "pos_bias_v" in name:
												lowercase_ : Optional[Any]							       =   None
										elif "weight_g" in name:
												lowercase_ : Tuple							       =   '''weight_g'''
										elif "weight_v" in name:
												lowercase_ : Dict							       =   '''weight_v'''
										elif "bias" in name:
												lowercase_ : str							       =   '''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowercase_ : Dict							       =   '''weight'''
										elif "running_mean" in name:
												lowercase_ : Any							       =   '''running_mean'''
										elif "inv_freq" in name:
												lowercase_ : Union[str, Any]							       =   '''inv_freq'''
										elif "running_var" in name:
												lowercase_ : int							       =   '''running_var'''
										elif "num_batches_tracked" in name:
												lowercase_ : Any							       =   '''num_batches_tracked'''
										else:
												lowercase_ : Optional[int]							       =   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 						lowercase    (				__snake_case				: Optional[int]		,					__snake_case				: Tuple		,					__snake_case				: Optional[int]		,					__snake_case				: Any		,					__snake_case				: Union[str, Any]	):
		lowercase_ : Tuple							       =   full_name.split('''conv_layers.'''	)[-1]
		lowercase_ : List[Any]							       =   name.split('''.'''	)
		lowercase_ : Union[str, Any]							       =   int(items[0]	)
		lowercase_ : str							       =   int(items[1]	)

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
						lowercase_ : Tuple							       =   value
						logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
						lowercase_ : Tuple							       =   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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''	)
						lowercase_ : Any							       =   value
						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''	)
						lowercase_ : Optional[int]							       =   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 						lowercase    (				__snake_case				: Union[str, Any]		,					__snake_case				: List[str]		,					__snake_case				: Tuple=None		,					__snake_case				: Tuple=None		,					__snake_case				: str=True	):
		if config_path is not None:
				lowercase_ : Dict							       =   WavaVecaConformerConfig.from_pretrained(__snake_case		,					hidden_act='''swish'''	)
		else:
				lowercase_ : Dict							       =   WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				lowercase_ : str							       =   '''rotary'''

		if is_finetuned:
				if dict_path:
						lowercase_ : Tuple							       =   Dictionary.load(__snake_case	)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowercase_ : Tuple							       =   target_dict.pad_index
						lowercase_ : List[str]							       =   target_dict.bos_index
						lowercase_ : Dict							       =   target_dict.eos_index
						lowercase_ : List[str]							       =   len(target_dict.symbols	)
						lowercase_ : Tuple							       =   os.path.join(__snake_case		,					'''vocab.json'''	)
						if not os.path.isdir(__snake_case	):
								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case	)	)
								return
						os.makedirs(__snake_case		,					exist_ok=__snake_case	)
						lowercase_ : Union[str, Any]							       =   target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowercase_ : Tuple							       =   0
						lowercase_ : List[str]							       =   1
						with open(__snake_case		,					'''w'''		,					encoding='''utf-8'''	) as vocab_handle:
								json.dump(__snake_case		,					__snake_case	)
						lowercase_ : Optional[int]							       =   WavaVecaCTCTokenizer(
						    __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=__snake_case		,					)
						lowercase_ : int							       =   True if config.feat_extract_norm == '''layer''' else False
						lowercase_ : Any							       =   WavaVecaFeatureExtractor(
						    feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=__snake_case		,					return_attention_mask=__snake_case		,					)
						lowercase_ : Union[str, Any]							       =   WavaVecaProcessor(feature_extractor=__snake_case		,					tokenizer=__snake_case	)
						processor.save_pretrained(__snake_case	)

				lowercase_ : Tuple							       =   WavaVecaConformerForCTC(__snake_case	)
		else:
				lowercase_ : Union[str, Any]							       =   WavaVecaConformerForPreTraining(__snake_case	)

		if is_finetuned:
				lowercase_       ,					lowercase_       ,					lowercase_ : int							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]		,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
		else:
				lowercase_ : List[Any]							       =   argparse.Namespace(task='''audio_pretraining'''	)
				lowercase_ : Union[str, Any]							       =   fairseq.tasks.setup_task(__snake_case	)

				lowercase_       ,					lowercase_       ,					lowercase_ : str							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,					task=__snake_case	)

		lowercase_ : str							       =   model[0].eval()

		recursively_load_weights(__snake_case		,					__snake_case		,					not is_finetuned	)

		hf_wavavec.save_pretrained(__snake_case	)


if __name__ == "__main__":
						__A		:       List[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(
						    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
						)
						__A		:       int  				=		parser.parse_args()
						convert_wavaveca_conformer_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)











"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



