




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()
__A        =    logging.get_logger(__name__)

__A        =    {
    "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",
}
__A        =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str]      )     ->       int:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Union[str, Any]      )     ->       Tuple:





				"""simple docstring"""


				for attribute in key.split('.'      ):
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )

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

				if weight_type is not None and weight_type != "param":
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      ).shape
				elif weight_type is not None and weight_type == "param":
								__lowerCamelCase		       =    hf_pointer
								for attribute in hf_param_name.split('.'      ):
												__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )
								__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(UpperCamelCase__ ,     UpperCamelCase__      )
								__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_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Any      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(UpperCamelCase__      ):
												__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]


__A        =    {
    "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_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Union[str, Any]=None ,     UpperCamelCase__				:      Optional[int]=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(UpperCamelCase__      )[0].split('.'      )[-2]
																__lowerCamelCase		       =    mapped_key.replace('*' ,     UpperCamelCase__      )
												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(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
												else:
																set_recursively(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
												return is_used
				return is_used







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Union[str, Any]      )     ->       Optional[Any]:





				"""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(
												    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     hf_model.config.feat_extract_norm == 'group' ,     )
												__lowerCamelCase		       =    True
								else:
												__lowerCamelCase		       =    load_wavaveca_layer(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								if not is_used:
												unused_weights.append(UpperCamelCase__      )

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      int      )     ->       Any:





				"""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(UpperCamelCase__      )







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Any=None ,     UpperCamelCase__				:      str=None ,     UpperCamelCase__				:      int=True ,     UpperCamelCase__				:      str=False      )     ->       str:





				"""simple docstring"""


				if config_path is not None:
								__lowerCamelCase		       =    WavaVecaConfig.from_pretrained(UpperCamelCase__      )
				else:
								__lowerCamelCase		       =    WavaVecaConfig()

				if is_seq_class:
								__lowerCamelCase		       =    read_txt_into_dict(UpperCamelCase__      )
								__lowerCamelCase		       =    idalabel
								__lowerCamelCase		       =    WavaVecaForSequenceClassification(UpperCamelCase__      )
								__lowerCamelCase		       =    WavaVecaFeatureExtractor(
								    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=UpperCamelCase__ ,     return_attention_mask=UpperCamelCase__ ,     )
								feature_extractor.save_pretrained(UpperCamelCase__      )

				elif is_finetuned:
								if dict_path:
												__lowerCamelCase		       =    Dictionary.load(UpperCamelCase__      )

												# 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(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__      )
												__lowerCamelCase		       =    target_dict.indices

												# fairseq has the <pad> and <s> switched
												__lowerCamelCase		       =    0
												__lowerCamelCase		       =    1
												with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as vocab_handle:
																json.dump(UpperCamelCase__ ,     UpperCamelCase__      )
												__lowerCamelCase		       =    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__ ,     )
												__lowerCamelCase		       =    True if config.feat_extract_norm == 'layer' else False
												__lowerCamelCase		       =    WavaVecaFeatureExtractor(
												    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=UpperCamelCase__ ,     return_attention_mask=UpperCamelCase__ ,     )
												__lowerCamelCase		       =    WavaVecaProcessor(feature_extractor=UpperCamelCase__ ,     tokenizer=UpperCamelCase__      )
												processor.save_pretrained(UpperCamelCase__      )

								__lowerCamelCase		       =    WavaVecaForCTC(UpperCamelCase__      )
				else:
								__lowerCamelCase		       =    WavaVecaForPreTraining(UpperCamelCase__      )

				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(UpperCamelCase__      )

								__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=UpperCamelCase__      )

				__lowerCamelCase		       =    model[0].eval()

				recursively_load_weights(UpperCamelCase__ ,     UpperCamelCase__ ,     not is_finetuned      )

				hf_wavavec.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    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",
					)
					__A        =    parser.parse_args()

					__A        =    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,
					)








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


