






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)

lowerCamelCase : Dict						   =       {
    '''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''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase : Union[str, Any]						   =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						str      ):
							for attribute in key.split("""."""      ):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												__lowercase      :							Optional[Any]				=     """lm_head"""

														__lowercase      :							Optional[Any]				=     getattr(lowerCAmelCase_       ,       lowerCAmelCase_      )

							if weight_type is not None:
														__lowercase      :							Tuple				=     getattr(lowerCAmelCase_       ,       lowerCAmelCase_      ).shape
							else:
														__lowercase      :							List[str]				=     hf_pointer.shape

							assert hf_shape == value.shape, (
							    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":
														__lowercase      :							Tuple				=     value
							elif weight_type == "weight_g":
														__lowercase      :							Union[str, Any]				=     value
							elif weight_type == "weight_v":
														__lowercase      :							Dict				=     value
							elif weight_type == "bias":
														__lowercase      :							str				=     value
							else:
														__lowercase      :							Optional[int]				=     value

							logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."      )
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Any      ):
							__lowercase      :							Optional[int]				=     []
							__lowercase      :							Dict				=     fairseq_model.state_dict()

							__lowercase      :							Tuple				=     hf_model.unispeech.feature_extractor

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

							logger.warning(F"Unused weights: {unused_weights}"      )
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							Optional[Any]				=     full_name.split("""conv_layers."""      )[-1]
							__lowercase      :							Tuple				=     name.split("""."""      )
							__lowercase      :							List[str]				=     int(items[0]      )
							__lowercase      :							List[Any]				=     int(items[1]      )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
																					)
																					__lowercase      :							int				=     value
																					logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
																					)
																					__lowercase      :							List[Any]				=     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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
																					    " found."
																					)
																					__lowercase      :							Dict				=     value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
																					)
																					__lowercase      :							str				=     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       snake_case_		(     lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Optional[int]=None       ,       lowerCAmelCase_       :						Optional[Any]=None       ,       lowerCAmelCase_       :						Tuple=True      ):
							if config_path is not None:
														__lowercase      :							str				=     UniSpeechConfig.from_pretrained(lowerCAmelCase_      )
							else:
														__lowercase      :							Dict				=     UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					__lowercase      :							List[Any]				=     Dictionary.load_from_json(lowerCAmelCase_      )

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

																					# fairseq has the <pad> and <s> switched
																					__lowercase      :							List[Any]				=     42
																					__lowercase      :							Union[str, Any]				=     43
																					with open(lowerCAmelCase_       ,       """w"""       ,       encoding="""utf-8"""      ) as vocab_handle:
																												json.dump(lowerCAmelCase_       ,       lowerCAmelCase_      )
																					__lowercase      :							Optional[int]				=     WavaVecaPhonemeCTCTokenizer(
																					    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_       ,       )
																					__lowercase      :							str				=     True if config.feat_extract_norm == """layer""" else False
																					__lowercase      :							List[str]				=     WavaVecaFeatureExtractor(
																					    feature_size=1       ,       sampling_rate=16000       ,       padding_value=0       ,       do_normalize=lowerCAmelCase_       ,       return_attention_mask=lowerCAmelCase_       ,       )
																					__lowercase      :							Tuple				=     WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,       tokenizer=lowerCAmelCase_      )
																					processor.save_pretrained(lowerCAmelCase_      )

														__lowercase      :							str				=     UniSpeechForCTC(lowerCAmelCase_      )
							else:
														__lowercase      :							Union[str, Any]				=     UniSpeechForPreTraining(lowerCAmelCase_      )

							if is_finetuned:
														__lowercase			,			__lowercase			,			__lowercase      :							List[Any]				=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""      )[:-1]      ), """w2v_path""": checkpoint_path}      )
							else:
														__lowercase			,			__lowercase			,			__lowercase      :							Optional[int]				=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

							__lowercase      :							Optional[int]				=     model[0].eval()

							recursively_load_weights(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

							hf_unispeech.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
	lowerCamelCase : Dict						   =       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'''
	)
	lowerCamelCase : str						   =       parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     word_bank or []
							# create a table
							__lowercase      :							int				=     len(lowerCAmelCase_      ) + 1

							__lowercase      :							list[list[list[str]]]				=     []
							for _ in range(lowerCAmelCase_      ):
														table.append([]      )
							# seed value
							__lowercase      :							Dict				=     [[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(lowerCAmelCase_      ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(lowerCAmelCase_      )] == word:
																																			__lowercase      :							list[list[str]]				=     [
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(lowerCAmelCase_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(lowerCAmelCase_      )]:
														combination.reverse()

							return table[len(lowerCAmelCase_      )]


if __name__ == "__main__":
	print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
	print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
	print(
	    all_construct(
	        '''hexagonosaurus''',
	        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
	    )
	)