





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()
__SCREAMING_SNAKE_CASE							:    Tuple          =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    Any          ={
    '''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''',
}
__SCREAMING_SNAKE_CASE							:    Tuple          =[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ ):
				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							=     """lm_head"""

								lowercase							=     getattr(UpperCamelCase__       ,UpperCamelCase__ )

				if weight_type is not None:
								lowercase							=     getattr(UpperCamelCase__       ,UpperCamelCase__ ).shape
				else:
								lowercase							=     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							=     value
				elif weight_type == "weight_g":
								lowercase							=     value
				elif weight_type == "weight_v":
								lowercase							=     value
				elif weight_type == "bias":
								lowercase							=     value
				else:
								lowercase							=     value

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


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ ):
				lowercase							=     []
				lowercase							=     fairseq_model.state_dict()

				lowercase							=     hf_model.unispeech.feature_extractor

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

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


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ ):
				lowercase							=     full_name.split("""conv_layers.""" )[-1]
				lowercase							=     name.split(""".""" )
				lowercase							=     int(items[0] )
				lowercase							=     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							=     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							=     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							=     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							=     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       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=True ):
				if config_path is not None:
								lowercase							=     UniSpeechConfig.from_pretrained(UpperCamelCase__ )
				else:
								lowercase							=     UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowercase							=     Dictionary.load_from_json(UpperCamelCase__ )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowercase							=     target_dict.pad_index
												lowercase							=     target_dict.bos_index
												lowercase							=     target_dict.eos_index
												lowercase							=     len(target_dict.symbols )
												lowercase							=     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__ )
												lowercase							=     target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowercase							=     42
												lowercase							=     43
												with open(UpperCamelCase__       ,"""w"""       ,encoding="""utf-8""" ) as vocab_handle:
																json.dump(UpperCamelCase__       ,UpperCamelCase__ )
												lowercase							=     WavaVecaPhonemeCTCTokenizer(
												    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__       ,)
												lowercase							=     True if config.feat_extract_norm == """layer""" else False
												lowercase							=     WavaVecaFeatureExtractor(
												    feature_size=1       ,sampling_rate=16_000       ,padding_value=0       ,do_normalize=UpperCamelCase__       ,return_attention_mask=UpperCamelCase__       ,)
												lowercase							=     WavaVecaProcessor(feature_extractor=UpperCamelCase__       ,tokenizer=UpperCamelCase__ )
												processor.save_pretrained(UpperCamelCase__ )

								lowercase							=     UniSpeechForCTC(UpperCamelCase__ )
				else:
								lowercase							=     UniSpeechForPreTraining(UpperCamelCase__ )

				if is_finetuned:
								lowercase					,    lowercase					,    lowercase							=     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							=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

				lowercase							=     model[0].eval()

				recursively_load_weights(UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__ )

				hf_unispeech.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE							:    List[Any]          =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'''
					)
					__SCREAMING_SNAKE_CASE							:    Any          =parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "encoder-decoder"
							lowerCamelCase_							     =       True


							def __init__(				self     :Optional[int]				,					**__A     :str		) -> int:



									"""simple docstring"""



									super().__init__(**__A		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""encoder"""		)
									SCREAMING_SNAKE_CASE__								=   encoder_config.pop("""model_type"""		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""decoder"""		)
									SCREAMING_SNAKE_CASE__								=   decoder_config.pop("""model_type"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   True


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :PretrainedConfig				,					__A     :PretrainedConfig				,					**__A     :List[str]		) -> PretrainedConfig:



									"""simple docstring"""



									logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   True

									return cls(encoder=encoder_config.to_dict()				,					decoder=decoder_config.to_dict()				,					**__A		)


							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__								=   self.encoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.decoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.__class__.model_type
									return output