




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()
lowercase__  		=		logging.get_logger(__name__)

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







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					{}
				with open(lowercase__							,					'r'	) as file:
								for line_number, line in enumerate(lowercase__	):
												_lowerCamelCase  :    Union[str, Any]					=					line.strip()
												if line:
																_lowerCamelCase  :    Optional[Any]					=					line.split()
																_lowerCamelCase  :    Dict					=					line_number
																_lowerCamelCase  :    Union[str, Any]					=					words[0]
																_lowerCamelCase  :    Tuple					=					value
				return result







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				for attribute in key.split('.'	):
								_lowerCamelCase  :    int					=					getattr(lowercase__							,					lowercase__	)

				_lowerCamelCase  :    Union[str, Any]					=					None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowercase__	):
												_lowerCamelCase  :    Union[str, Any]					=					PARAM_MAPPING[full_name.split('.'	)[-1]]
												_lowerCamelCase  :    str					=					'param'

				if weight_type is not None and weight_type != "param":
								_lowerCamelCase  :    List[str]					=					getattr(lowercase__							,					lowercase__	).shape
				elif weight_type is not None and weight_type == "param":
								_lowerCamelCase  :    Any					=					hf_pointer
								for attribute in hf_param_name.split('.'	):
												_lowerCamelCase  :    List[str]					=					getattr(lowercase__							,					lowercase__	)
								_lowerCamelCase  :    Dict					=					shape_pointer.shape

								# let's reduce dimension
								_lowerCamelCase  :    List[str]					=					value[0]
				else:
								_lowerCamelCase  :    Optional[int]					=					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  :    Any					=					value
				elif weight_type == "weight_g":
								_lowerCamelCase  :    List[Any]					=					value
				elif weight_type == "weight_v":
								_lowerCamelCase  :    List[str]					=					value
				elif weight_type == "bias":
								_lowerCamelCase  :    List[str]					=					value
				elif weight_type == "param":
								for attribute in hf_param_name.split('.'	):
												_lowerCamelCase  :    int					=					getattr(lowercase__							,					lowercase__	)
								_lowerCamelCase  :    Union[str, Any]					=					value
				else:
								_lowerCamelCase  :    int					=					value

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowercase__	):
												_lowerCamelCase  :    Any					=					PARAM_MAPPING[full_name.split('.'	)[-1]]
												_lowerCamelCase  :    Union[str, Any]					=					'param'

				if weight_type is not None and weight_type != "param":
								_lowerCamelCase  :    str					=					'.'.join([key, weight_type]	)
				elif weight_type is not None and weight_type == "param":
								_lowerCamelCase  :    Optional[Any]					=					'.'.join([key, hf_param_name]	)
				else:
								_lowerCamelCase  :    Dict					=					key

				_lowerCamelCase  :    Dict					=					value if 'lm_head' in full_key else value[0]


lowercase__  		=		{
    """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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=None							,					lowercase__=None	):
				_lowerCamelCase  :    int					=					False
				for key, mapped_key in MAPPING.items():
								_lowerCamelCase  :    Optional[int]					=					'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  :    int					=					True
												if "*" in mapped_key:
																_lowerCamelCase  :    List[str]					=					name.split(lowercase__	)[0].split('.'	)[-2]
																_lowerCamelCase  :    Optional[int]					=					mapped_key.replace('*'							,					lowercase__	)
												if "weight_g" in name:
																_lowerCamelCase  :    List[Any]					=					'weight_g'
												elif "weight_v" in name:
																_lowerCamelCase  :    str					=					'weight_v'
												elif "bias" in name:
																_lowerCamelCase  :    List[str]					=					'bias'
												elif "weight" in name:
																# TODO: don't match quantizer.weight_proj
																_lowerCamelCase  :    Optional[int]					=					'weight'
												else:
																_lowerCamelCase  :    Optional[Any]					=					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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Any					=					[]
				_lowerCamelCase  :    Tuple					=					fairseq_model.state_dict()

				_lowerCamelCase  :    str					=					hf_model.wavaveca.feature_extractor

				for name, value in fairseq_dict.items():
								_lowerCamelCase  :    Dict					=					False
								if "conv_layers" in name:
												load_conv_layer(
												    lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					hf_model.config.feat_extract_norm == 'group'							,					)
												_lowerCamelCase  :    Union[str, Any]					=					True
								else:
												_lowerCamelCase  :    Any					=					load_wavaveca_layer(lowercase__							,					lowercase__							,					lowercase__	)
								if not is_used:
												unused_weights.append(lowercase__	)

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					full_name.split('conv_layers.'	)[-1]
				_lowerCamelCase  :    Union[str, Any]					=					name.split('.'	)
				_lowerCamelCase  :    List[Any]					=					int(items[0]	)
				_lowerCamelCase  :    Optional[Any]					=					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  :    Union[str, Any]					=					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  :    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:
												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  :    Optional[int]					=					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  :    List[str]					=					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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=None							,					lowercase__=None							,					lowercase__=True							,					lowercase__=False	):
				if config_path is not None:
								_lowerCamelCase  :    Union[str, Any]					=					WavaVecaConfig.from_pretrained(lowercase__	)
				else:
								_lowerCamelCase  :    Union[str, Any]					=					WavaVecaConfig()

				if is_seq_class:
								_lowerCamelCase  :    Union[str, Any]					=					read_txt_into_dict(lowercase__	)
								_lowerCamelCase  :    Dict					=					idalabel
								_lowerCamelCase  :    Any					=					WavaVecaForSequenceClassification(lowercase__	)
								_lowerCamelCase  :    int					=					WavaVecaFeatureExtractor(
								    feature_size=1							,					sampling_rate=16000							,					padding_value=0							,					do_normalize=lowercase__							,					return_attention_mask=lowercase__							,					)
								feature_extractor.save_pretrained(lowercase__	)

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

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_lowerCamelCase  :    str					=					target_dict.pad_index
												_lowerCamelCase  :    List[Any]					=					target_dict.bos_index
												_lowerCamelCase  :    Optional[Any]					=					target_dict.eos_index
												_lowerCamelCase  :    int					=					len(target_dict.symbols	)
												_lowerCamelCase  :    Union[str, Any]					=					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  :    str					=					target_dict.indices

												# fairseq has the <pad> and <s> switched
												_lowerCamelCase  :    Optional[Any]					=					0
												_lowerCamelCase  :    List[Any]					=					1
												with open(lowercase__							,					'w'							,					encoding='utf-8'	) as vocab_handle:
																json.dump(lowercase__							,					lowercase__	)
												_lowerCamelCase  :    Optional[int]					=					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  :    Dict					=					True if config.feat_extract_norm == 'layer' else False
												_lowerCamelCase  :    Any					=					WavaVecaFeatureExtractor(
												    feature_size=1							,					sampling_rate=16000							,					padding_value=0							,					do_normalize=lowercase__							,					return_attention_mask=lowercase__							,					)
												_lowerCamelCase  :    Dict					=					WavaVecaProcessor(feature_extractor=lowercase__							,					tokenizer=lowercase__	)
												processor.save_pretrained(lowercase__	)

								_lowerCamelCase  :    List[str]					=					WavaVecaForCTC(lowercase__	)
				else:
								_lowerCamelCase  :    Any					=					WavaVecaForPreTraining(lowercase__	)

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

								_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    int					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,					task=lowercase__	)

				_lowerCamelCase  :    Dict					=					model[0].eval()

				recursively_load_weights(lowercase__							,					lowercase__							,					not is_finetuned	)

				hf_wavavec.save_pretrained(lowercase__	)


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

			lowercase__  		=		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,
			)





"""simple docstring"""






from __future__ import annotations

lowercase__  		=		10







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					1
				_lowerCamelCase  :    Optional[int]					=					max(lowercase__	)
				while placement <= max_digit:
								# declare and initialize empty buckets
								_lowerCamelCase  :    list[list]					=					[[] for _ in range(lowercase__	)]
								# split list_of_ints between the buckets
								for i in list_of_ints:
												_lowerCamelCase  :    Dict					=					int((i / placement) % RADIX	)
												buckets[tmp].append(lowercase__	)
								# put each buckets' contents into list_of_ints
								_lowerCamelCase  :    str					=					0
								for b in range(lowercase__	):
												for i in buckets[b]:
																_lowerCamelCase  :    str					=					i
																a += 1
        # move to next
								placement *= RADIX
				return list_of_ints


if __name__ == "__main__":
			import doctest

			doctest.testmod()