


"""simple docstring"""




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()
__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {
    '''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''',
}
__UpperCamelCase						=      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						int:
						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
																								SCREAMING_SNAKE_CASE													=					'lm_head'

												SCREAMING_SNAKE_CASE													=					getattr(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

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

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					[]
						SCREAMING_SNAKE_CASE													=					fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE													=					hf_model.unispeech.feature_extractor

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

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					full_name.split('conv_layers.'  )[-1]
						SCREAMING_SNAKE_CASE													=					name.split('.'  )
						SCREAMING_SNAKE_CASE													=					int(items[0]  )
						SCREAMING_SNAKE_CASE													=					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.'
																		)
																		SCREAMING_SNAKE_CASE													=					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.'
																		)
																		SCREAMING_SNAKE_CASE													=					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."
																		)
																		SCREAMING_SNAKE_CASE													=					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.'
																		)
																		SCREAMING_SNAKE_CASE													=					value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE_  )


@torch.no_grad()
def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				List[Any]=None ,			SCREAMING_SNAKE_CASE_		:				List[str]=True  )   ->						str:
						if config_path is not None:
												SCREAMING_SNAKE_CASE													=					UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
						else:
												SCREAMING_SNAKE_CASE													=					UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		SCREAMING_SNAKE_CASE													=					Dictionary.load_from_json(SCREAMING_SNAKE_CASE_  )

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

																		# fairseq has the <pad> and <s> switched
																		SCREAMING_SNAKE_CASE													=					42
																		SCREAMING_SNAKE_CASE													=					43
																		with open(SCREAMING_SNAKE_CASE_ ,			'w' ,			encoding='utf-8'  ) as vocab_handle:
																								json.dump(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					WavaVecaPhonemeCTCTokenizer(
																		    SCREAMING_SNAKE_CASE_ ,			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=SCREAMING_SNAKE_CASE_ ,			)
																		SCREAMING_SNAKE_CASE													=					True if config.feat_extract_norm == 'layer' else False
																		SCREAMING_SNAKE_CASE													=					WavaVecaFeatureExtractor(
																		    feature_size=1 ,			sampling_rate=1_60_00 ,			padding_value=0 ,			do_normalize=SCREAMING_SNAKE_CASE_ ,			return_attention_mask=SCREAMING_SNAKE_CASE_ ,			)
																		SCREAMING_SNAKE_CASE													=					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ ,			tokenizer=SCREAMING_SNAKE_CASE_  )
																		processor.save_pretrained(SCREAMING_SNAKE_CASE_  )

												SCREAMING_SNAKE_CASE													=					UniSpeechForCTC(SCREAMING_SNAKE_CASE_  )
						else:
												SCREAMING_SNAKE_CASE													=					UniSpeechForPreTraining(SCREAMING_SNAKE_CASE_  )

						if is_finetuned:
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] ,			arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  ), 'w2v_path': checkpoint_path}  )
						else:
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

						SCREAMING_SNAKE_CASE													=					model[0].eval()

						recursively_load_weights(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE_  )


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






"""simple docstring"""




# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__UpperCamelCase						=      open  # noqa: we just need to have a builtin inside this module to test it properly


