



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()
_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						{
    '''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''',
}
_lowercase     							=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Any      ,			UpperCamelCase		:      Any      ,			UpperCamelCase		:      List[str]      ,			UpperCamelCase		:      Union[str, Any] )  ->str:



			'''simple docstring'''







			for attribute in key.split(""".""" ):
						lowerCamelCase__ :      Optional[int]								=		getattr(UpperCamelCase      ,			UpperCamelCase )

			if weight_type is not None:
						lowerCamelCase__ :      List[Any]								=		getattr(UpperCamelCase      ,			UpperCamelCase ).shape
			else:
						lowerCamelCase__ :      Dict								=		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__ :      Union[str, Any]								=		value
			elif weight_type == "weight_g":
						lowerCamelCase__ :      Union[str, Any]								=		value
			elif weight_type == "weight_v":
						lowerCamelCase__ :      int								=		value
			elif weight_type == "bias":
						lowerCamelCase__ :      str								=		value
			elif weight_type == "running_mean":
						lowerCamelCase__ :      Any								=		value
			elif weight_type == "running_var":
						lowerCamelCase__ :      Optional[Any]								=		value
			elif weight_type == "num_batches_tracked":
						lowerCamelCase__ :      List[str]								=		value
			elif weight_type == "inv_freq":
						lowerCamelCase__ :      Optional[Any]								=		value
			else:
						lowerCamelCase__ :      Union[str, Any]								=		value

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






def      _A				(UpperCamelCase		:      List[str]      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Optional[int] )  ->Tuple:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		[]
			lowerCamelCase__ :      str								=		fairseq_model.state_dict()

			lowerCamelCase__ :      Union[str, Any]								=		hf_model.wavaveca_conformer.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__ :      Dict								=		False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			hf_model.config.feat_extract_norm == """group"""      ,			)
									lowerCamelCase__ :      Tuple								=		True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase__ :      Any								=		"""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]:
															lowerCamelCase__ :      int								=		True
															if "*" in mapped_key:
																		lowerCamelCase__ :      Union[str, Any]								=		name.split(UpperCamelCase )[0].split(""".""" )[-2]
																		lowerCamelCase__ :      str								=		mapped_key.replace("""*"""      ,			UpperCamelCase )
															if "pos_bias_u" in name:
																		lowerCamelCase__ :      Optional[Any]								=		None
															elif "pos_bias_v" in name:
																		lowerCamelCase__ :      Optional[int]								=		None
															elif "weight_g" in name:
																		lowerCamelCase__ :      int								=		"""weight_g"""
															elif "weight_v" in name:
																		lowerCamelCase__ :      Any								=		"""weight_v"""
															elif "bias" in name:
																		lowerCamelCase__ :      Dict								=		"""bias"""
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCamelCase__ :      Optional[Any]								=		"""weight"""
															elif "running_mean" in name:
																		lowerCamelCase__ :      Union[str, Any]								=		"""running_mean"""
															elif "inv_freq" in name:
																		lowerCamelCase__ :      Optional[Any]								=		"""inv_freq"""
															elif "running_var" in name:
																		lowerCamelCase__ :      int								=		"""running_var"""
															elif "num_batches_tracked" in name:
																		lowerCamelCase__ :      str								=		"""num_batches_tracked"""
															else:
																		lowerCamelCase__ :      Any								=		None
															set_recursively(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
												continue
						if not is_used:
									unused_weights.append(UpperCamelCase )

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






def      _A				(UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      Any )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		full_name.split("""conv_layers.""" )[-1]
			lowerCamelCase__ :      List[str]								=		name.split(""".""" )
			lowerCamelCase__ :      Union[str, Any]								=		int(items[0] )
			lowerCamelCase__ :      Dict								=		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__ :      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." )
									lowerCamelCase__ :      Optional[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__ :      Union[str, Any]								=		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      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Optional[Any]=None      ,			UpperCamelCase		:      Dict=None      ,			UpperCamelCase		:      Union[str, Any]=True )  ->List[str]:



			'''simple docstring'''







			if config_path is not None:
						lowerCamelCase__ :      List[Any]								=		WavaVecaConformerConfig.from_pretrained(UpperCamelCase      ,			hidden_act="""swish""" )
			else:
						lowerCamelCase__ :      Tuple								=		WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						lowerCamelCase__ :      int								=		"""rotary"""

			if is_finetuned:
						if dict_path:
									lowerCamelCase__ :      Dict								=		Dictionary.load(UpperCamelCase )

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

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

						lowerCamelCase__ :      Union[str, Any]								=		WavaVecaConformerForCTC(UpperCamelCase )
			else:
						lowerCamelCase__ :      Union[str, Any]								=		WavaVecaConformerForPreTraining(UpperCamelCase )

			if is_finetuned:
						lowerCamelCase__ :      Optional[int]								=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]      ,			arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
			else:
						lowerCamelCase__ :      int								=		argparse.Namespace(task="""audio_pretraining""" )
						lowerCamelCase__ :      Dict								=		fairseq.tasks.setup_task(UpperCamelCase )

						lowerCamelCase__ :      Dict								=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,			task=UpperCamelCase )

			lowerCamelCase__ :      List[str]								=		model[0].eval()

			recursively_load_weights(UpperCamelCase      ,			UpperCamelCase      ,			not is_finetuned )

			hf_wavavec.save_pretrained(UpperCamelCase )


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'''
							)
							_lowercase     							=						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
							)








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



