






"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__       :				Any 					=					50_000
A__       :				int 					=					5_000

A__					,						A__       :				Optional[int] 					=					os.path.split(__file__)
A__       :				List[Any] 					=					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def  _snake_case				(     lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    Tuple  )				->	str:







							for i in range(lowerCamelCase__  ):
														lowerCamelCase_       :							int									=dataset[i]

@get_duration
def  _snake_case				(     lowerCamelCase__	:    Any				,						lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    int  )				->	List[Any]:







							for i in range(0				,						len(lowerCamelCase__  )				,						lowerCamelCase__  ):
														lowerCamelCase_       :							str									=dataset[i : i + batch_size]

@get_duration
def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Any				,						lowerCamelCase__	:    List[str]  )				->	Dict:







							with dataset.formatted_as(type=lowerCamelCase__  ):
														for i in range(lowerCamelCase__  ):
																					lowerCamelCase_       :							List[str]									=dataset[i]

@get_duration
def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Any				,						lowerCamelCase__	:    Union[str, Any]				,						lowerCamelCase__	:    Tuple  )				->	Tuple:







							with dataset.formatted_as(type=lowerCamelCase__  ):
														for i in range(0				,						lowerCamelCase__				,						lowerCamelCase__  ):
																					lowerCamelCase_       :							List[Any]									=dataset[i : i + batch_size]

def  _snake_case				(     )				->	Optional[Any]:
							lowerCamelCase_       :							Optional[Any]									={'''num examples''': SPEED_TEST_N_EXAMPLES}
							lowerCamelCase_       :							Optional[Any]									=[
							    (read, {'''length''': SMALL_TEST}),
							    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_000}),
							    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
							    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
							    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
							    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_000}),
							]

							lowerCamelCase_       :							List[str]									=[
							    (read, {'''length''': SMALL_TEST}),
							    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_000}),
							    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("generating dataset"  )
														lowerCamelCase_       :							Any									=datasets.Features(
														    {"list": datasets.Sequence(datasets.Value("float32"  )  ), "numbers": datasets.Value("float32"  )}  )
														lowerCamelCase_       :							int									=generate_example_dataset(
														    os.path.join(lowerCamelCase__				,						"dataset.arrow"  )				,						lowerCamelCase__				,						num_examples=lowerCamelCase__				,						seq_shapes={"list": (100,)}				,						)
														print("first set of iterations"  )
														for func, kwargs in functions:
																					print(func.__name__				,						str(lowerCamelCase__  )  )
																					lowerCamelCase_       :							Union[str, Any]									=func(lowerCamelCase__				,						**lowerCamelCase__  )

														print("shuffling dataset"  )
														lowerCamelCase_       :							Union[str, Any]									=dataset.shuffle()
														print("Second set of iterations (after shuffling"  )
														for func, kwargs in functions_shuffled:
																					print("shuffled "				,						func.__name__				,						str(lowerCamelCase__  )  )
																					lowerCamelCase_       :							List[Any]									=func(
																					    lowerCamelCase__				,						**lowerCamelCase__  )

							with open(lowerCamelCase__				,						"wb"  ) as f:
														f.write(json.dumps(lowerCamelCase__  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
						benchmark_iterating()




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    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.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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for attribute in key.split('''.'''	):
        __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    if weight_type is not None:
        __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).shape
    else:
        __UpperCamelCase     :Union[str, Any]			  =		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":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_g":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "weight_v":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "bias":
        __UpperCamelCase     :Union[str, Any]			  =		value
    else:
        __UpperCamelCase     :str			  =		value

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		[]
    __UpperCamelCase     :int			  =		fairseq_model.state_dict()

    __UpperCamelCase     :List[Any]			  =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase     :List[Any]			  =		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'''  ,			)
            __UpperCamelCase     :List[str]			  =		True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase     :Dict			  =		'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
                    __UpperCamelCase     :Optional[Any]			  =		True
                    if "*" in mapped_key:
                        __UpperCamelCase     :List[str]			  =		name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
                        __UpperCamelCase     :Optional[int]			  =		mapped_key.replace('''*'''  ,			SCREAMING_SNAKE_CASE	)
                    if "weight_g" in name:
                        __UpperCamelCase     :int			  =		'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase     :List[Any]			  =		'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase     :Dict			  =		'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase     :Dict			  =		'''bias'''
                    else:
                        __UpperCamelCase     :Dict			  =		None
                    set_recursively(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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		full_name.split('''conv_layers.'''	)[-1]
    __UpperCamelCase     :Optional[int]			  =		name.split('''.'''	)
    __UpperCamelCase     :str			  =		int(items[0]	)
    __UpperCamelCase     :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."""
            )
            __UpperCamelCase     :Dict			  =		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."""
            )
            __UpperCamelCase     :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."
            )
            __UpperCamelCase     :int			  =		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."""
            )
            __UpperCamelCase     :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(SCREAMING_SNAKE_CASE	)



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=True	):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase     :Tuple			  =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		HubertConfig()

    if is_finetuned:
        if dict_path:
            __UpperCamelCase     :Optional[int]			  =		Dictionary.load(SCREAMING_SNAKE_CASE	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase     :Optional[int]			  =		target_dict.pad_index
            __UpperCamelCase     :Dict			  =		target_dict.bos_index
            __UpperCamelCase     :str			  =		target_dict.eos_index
            __UpperCamelCase     :Dict			  =		len(target_dict.symbols	)
            __UpperCamelCase     :List[Any]			  =		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	)
            with open(SCREAMING_SNAKE_CASE  ,			'''w'''  ,			encoding='''utf-8'''	) as vocab_handle:
                json.dump(target_dict.indices  ,			SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		WavaVecaCTCTokenizer(
                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  ,			)
            __UpperCamelCase     :Union[str, Any]			  =		True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase     :Any			  =		WavaVecaFeatureExtractor(
                feature_size=1  ,			sampling_rate=16_000  ,			padding_value=0  ,			do_normalize=SCREAMING_SNAKE_CASE  ,			return_attention_mask=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Any			  =		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE  ,			tokenizer=SCREAMING_SNAKE_CASE	)
            processor.save_pretrained(SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :List[str]			  =		HubertForCTC(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :str			  =		HubertModel(SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Dict			  =		model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE	)


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_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





