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 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			List[Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : int    )				->			Tuple:






						'''simple docstring'''



						for attribute in key.split("""."""    ):
												UpperCAmelCase_=       getattr(lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(lowerCAmelCase_    ):
																		UpperCAmelCase_=       PARAM_MAPPING[full_name.split("""."""    )[-1]]
																		UpperCAmelCase_=       """param"""

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

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

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



def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Tuple    )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(lowerCAmelCase_    ):
																		UpperCAmelCase_=       PARAM_MAPPING[full_name.split("""."""    )[-1]]
																		UpperCAmelCase_=       """param"""

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

						UpperCAmelCase_=       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 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : int=None      ,lowerCAmelCase_       : Optional[Any]=None    )				->			Union[str, Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Union[str, Any]    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       fairseq_model.state_dict()

						UpperCAmelCase_=       hf_model.wavaveca.feature_extractor

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

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



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[Any]    )				->			Any:






						'''simple docstring'''



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



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






						'''simple docstring'''



						if config_path is not None:
												UpperCAmelCase_=       WavaVecaConfig.from_pretrained(lowerCAmelCase_    )
						else:
												UpperCAmelCase_=       WavaVecaConfig()

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

						elif is_finetuned:
												if dict_path:
																		UpperCAmelCase_=       Dictionary.load(lowerCAmelCase_    )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		UpperCAmelCase_=       target_dict.pad_index
																		UpperCAmelCase_=       target_dict.bos_index
																		UpperCAmelCase_=       target_dict.eos_index
																		UpperCAmelCase_=       len(target_dict.symbols    )
																		UpperCAmelCase_=       os.path.join(lowerCAmelCase_      ,"""vocab.json"""    )
																		if not os.path.isdir(lowerCAmelCase_    ):
																								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase_    )    )
																								return
																		os.makedirs(lowerCAmelCase_      ,exist_ok=lowerCAmelCase_    )
																		UpperCAmelCase_=       target_dict.indices

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

												UpperCAmelCase_=       WavaVecaForCTC(lowerCAmelCase_    )
						else:
												UpperCAmelCase_=       WavaVecaForPreTraining(lowerCAmelCase_    )

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

												UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,task=lowerCAmelCase_    )

						UpperCAmelCase_=       model[0].eval()

						recursively_load_weights(lowerCAmelCase_      ,lowerCAmelCase_      ,not is_finetuned    )

						hf_wavavec.save_pretrained(lowerCAmelCase_    )


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,
	)









__A       =						6_5521



def 	__a					(   lowerCAmelCase_       : str    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       1
						UpperCAmelCase_=       0
						for plain_chr in plain_text:
												UpperCAmelCase_=       (a + ord(lowerCAmelCase_    )) % MOD_ADLER
												UpperCAmelCase_=       (b + a) % MOD_ADLER
						return (b << 16) | a




