






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_UpperCAmelCase      :		Optional[int] 					=     logging.get_logger(__name__)

_UpperCAmelCase      :		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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
_UpperCAmelCase      :		Tuple 					=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 						lowerCAmelCase_		(lowercase__    :							Optional[Any]							)		->     Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__         =    {}
				with open(lowercase__  ,       '''r'''							) as file:
								for line_number, line in enumerate(lowercase__							):
												lowerCAmelCase__         =    line.strip()
												if line:
																lowerCAmelCase__         =    line.split()
																lowerCAmelCase__         =    line_number
																lowerCAmelCase__         =    words[0]
																lowerCAmelCase__         =    value
				return result





def 						lowerCAmelCase_		(lowercase__    :							int  ,       lowercase__    :							str  ,       lowercase__    :							int  ,       lowercase__    :							str  ,       lowercase__    :							Union[str, Any]							)		->     Optional[int]:


				'''simple docstring'''




				for attribute in key.split('''.'''							):
								lowerCAmelCase__         =    getattr(lowercase__  ,       lowercase__							)

				lowerCAmelCase__         =    None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowercase__							):
												lowerCAmelCase__         =    PARAM_MAPPING[full_name.split('''.'''							)[-1]]
												lowerCAmelCase__         =    '''param'''

				if weight_type is not None and weight_type != "param":
								lowerCAmelCase__         =    getattr(lowercase__  ,       lowercase__							).shape
				elif weight_type is not None and weight_type == "param":
								lowerCAmelCase__         =    hf_pointer
								for attribute in hf_param_name.split('''.'''							):
												lowerCAmelCase__         =    getattr(lowercase__  ,       lowercase__							)
								lowerCAmelCase__         =    shape_pointer.shape

								# let's reduce dimension
								lowerCAmelCase__         =    value[0]
				else:
								lowerCAmelCase__         =    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":
								lowerCAmelCase__         =    value
				elif weight_type == "weight_g":
								lowerCAmelCase__         =    value
				elif weight_type == "weight_v":
								lowerCAmelCase__         =    value
				elif weight_type == "bias":
								lowerCAmelCase__         =    value
				elif weight_type == "param":
								for attribute in hf_param_name.split('''.'''							):
												lowerCAmelCase__         =    getattr(lowercase__  ,       lowercase__							)
								lowerCAmelCase__         =    value
				else:
								lowerCAmelCase__         =    value

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





def 						lowerCAmelCase_		(lowercase__    :							List[Any]  ,       lowercase__    :							Optional[int]  ,       lowercase__    :							List[str]  ,       lowercase__    :							int  ,       lowercase__    :							Dict							)		->     Tuple:


				'''simple docstring'''




				lowerCAmelCase__         =    None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowercase__							):
												lowerCAmelCase__         =    PARAM_MAPPING[full_name.split('''.'''							)[-1]]
												lowerCAmelCase__         =    '''param'''

				if weight_type is not None and weight_type != "param":
								lowerCAmelCase__         =    '''.'''.join([key, weight_type]							)
				elif weight_type is not None and weight_type == "param":
								lowerCAmelCase__         =    '''.'''.join([key, hf_param_name]							)
				else:
								lowerCAmelCase__         =    key

				lowerCAmelCase__         =    value if '''lm_head''' in full_key else value[0]


_UpperCAmelCase      :		Tuple 					=     {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}





def 						lowerCAmelCase_		(lowercase__    :							int  ,       lowercase__    :							List[str]  ,       lowercase__    :							Any=None  ,       lowercase__    :							List[Any]=None							)		->     Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__         =    False
				for key, mapped_key in MAPPING.items():
								lowerCAmelCase__         =    '''wav2vec2.''' + 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]:
												lowerCAmelCase__         =    True
												if "*" in mapped_key:
																lowerCAmelCase__         =    name.split(lowercase__							)[0].split('''.'''							)[-2]
																lowerCAmelCase__         =    mapped_key.replace('''*'''  ,       lowercase__							)
												if "weight_g" in name:
																lowerCAmelCase__         =    '''weight_g'''
												elif "weight_v" in name:
																lowerCAmelCase__         =    '''weight_v'''
												elif "bias" in name:
																lowerCAmelCase__         =    '''bias'''
												elif "weight" in name:
																# TODO: don't match quantizer.weight_proj
																lowerCAmelCase__         =    '''weight'''
												else:
																lowerCAmelCase__         =    None
												if hf_dict is not None:
																rename_dict(lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__							)
												else:
																set_recursively(lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__							)
												return is_used
				return is_used





def 						lowerCAmelCase_		(lowercase__    :							Tuple  ,       lowercase__    :							Union[str, Any]  ,       lowercase__    :							Any							)		->     Optional[int]:


				'''simple docstring'''




				lowerCAmelCase__         =    []
				lowerCAmelCase__         =    fairseq_model.state_dict()

				lowerCAmelCase__         =    hf_model.wavaveca.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCAmelCase__         =    False
								if "conv_layers" in name:
												load_conv_layer(
												    lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       hf_model.config.feat_extract_norm == '''group'''  ,       )
												lowerCAmelCase__         =    True
								else:
												lowerCAmelCase__         =    load_wavaveca_layer(lowercase__  ,       lowercase__  ,       lowercase__							)
								if not is_used:
												unused_weights.append(lowercase__							)

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





def 						lowerCAmelCase_		(lowercase__    :							Tuple  ,       lowercase__    :							Union[str, Any]  ,       lowercase__    :							Optional[Any]  ,       lowercase__    :							Tuple  ,       lowercase__    :							Union[str, Any]							)		->     Dict:


				'''simple docstring'''




				lowerCAmelCase__         =    full_name.split('''conv_layers.'''							)[-1]
				lowerCAmelCase__         =    name.split('''.'''							)
				lowerCAmelCase__         =    int(items[0]							)
				lowerCAmelCase__         =    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.'							)
												lowerCAmelCase__         =    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.'							)
												lowerCAmelCase__         =    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.'							)
												lowerCAmelCase__         =    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.'							)
												lowerCAmelCase__         =    value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
				else:
								unused_weights.append(lowercase__							)





@torch.no_grad()
def 						lowerCAmelCase_		(lowercase__    :							Optional[int]  ,       lowercase__    :							Tuple  ,       lowercase__    :							Any=None  ,       lowercase__    :							Tuple=None  ,       lowercase__    :							List[Any]=True  ,       lowercase__    :							List[Any]=False							)		->     int:


				'''simple docstring'''




				if config_path is not None:
								lowerCAmelCase__         =    WavaVecaConfig.from_pretrained(lowercase__							)
				else:
								lowerCAmelCase__         =    WavaVecaConfig()

				if is_seq_class:
								lowerCAmelCase__         =    read_txt_into_dict(lowercase__							)
								lowerCAmelCase__         =    idalabel
								lowerCAmelCase__         =    WavaVecaForSequenceClassification(lowercase__							)
								lowerCAmelCase__         =    WavaVecaFeatureExtractor(
								    feature_size=1  ,       sampling_rate=1_60_00  ,       padding_value=0  ,       do_normalize=lowercase__  ,       return_attention_mask=lowercase__  ,       )
								feature_extractor.save_pretrained(lowercase__							)

				elif is_finetuned:
								if dict_path:
												lowerCAmelCase__         =    Dictionary.load(lowercase__							)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCAmelCase__         =    target_dict.pad_index
												lowerCAmelCase__         =    target_dict.bos_index
												lowerCAmelCase__         =    target_dict.eos_index
												lowerCAmelCase__         =    len(target_dict.symbols							)
												lowerCAmelCase__         =    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__							)
												lowerCAmelCase__         =    target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    1
												with open(lowercase__  ,       '''w'''  ,       encoding='''utf-8'''							) as vocab_handle:
																json.dump(lowercase__  ,       lowercase__							)
												lowerCAmelCase__         =    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__  ,       )
												lowerCAmelCase__         =    True if config.feat_extract_norm == '''layer''' else False
												lowerCAmelCase__         =    WavaVecaFeatureExtractor(
												    feature_size=1  ,       sampling_rate=1_60_00  ,       padding_value=0  ,       do_normalize=lowercase__  ,       return_attention_mask=lowercase__  ,       )
												lowerCAmelCase__         =    WavaVecaProcessor(feature_extractor=lowercase__  ,       tokenizer=lowercase__							)
												processor.save_pretrained(lowercase__							)

								lowerCAmelCase__         =    WavaVecaForCTC(lowercase__							)
				else:
								lowerCAmelCase__         =    WavaVecaForPreTraining(lowercase__							)

				if is_finetuned or is_seq_class:
								lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__         =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]  ,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
				else:
								lowerCAmelCase__         =    argparse.Namespace(task='''audio_pretraining'''							)
								lowerCAmelCase__         =    fairseq.tasks.setup_task(lowercase__							)

								lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__         =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,       task=lowercase__							)

				lowerCAmelCase__         =    model[0].eval()

				recursively_load_weights(lowercase__  ,       lowercase__  ,       not is_finetuned							)

				hf_wavavec.save_pretrained(lowercase__							)


if __name__ == "__main__":
			_UpperCAmelCase      :		Union[str, 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"
			)
			parser.add_argument(
			    "--is_seq_class",
			    action="store_true",
			    help="Whether the model to convert is a fine-tuned sequence classification model or not",
			)
			_UpperCAmelCase      :		str 					=     parser.parse_args()

			_UpperCAmelCase      :		Optional[int] 					=     not args.not_finetuned and not args.is_seq_class
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.dict_path,
			    is_finetuned,
			    args.is_seq_class,
			)













from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 a						    = []
 a						    = []
 a						    = []

 for rt in rc.restypes:
  a						    = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
  restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
  a						    = {name: i for i, name in enumerate(a					)}
  restype_atomaa_to_atomaa_list.append(
      [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

  restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

 # Add dummy mapping for restype 'UNK'
 restype_atomaa_to_atomaa_list.append([0] * 14					)
 restype_atomaa_to_atomaa_list.append([0] * 37					)
 restype_atomaa_mask_list.append([0.0] * 14					)

 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device      ,				)
 a						    = protein['''aatype'''].to(torch.long					)

 # create the mapping for (residx, atom14) --> atom37, i.e. an array
 # with shape (num_res, 14) containing the atom37 indices for this protein
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = restype_atomaa_mask[protein_aatype]

 a						    = residx_atomaa_mask
 a						    = residx_atomaa_to_atomaa.long()

 # create the gather indices for mapping back
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = residx_atomaa_to_atomaa.long()

 # create the corresponding mask
 a						    = torch.zeros([21, 37]      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device					)
 for restype, restype_letter in enumerate(rc.restypes					):
  a						    = rc.restype_atoa[restype_letter]
  a						    = rc.residue_atoms[restype_name]
  for atom_name in atom_names:
   a						    = rc.atom_order[atom_name]
   a						    = 1

 a						    = restype_atomaa_mask[protein_aatype]
 a						    = residx_atomaa_mask

 return protein

def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out


