



"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__snake_case				:  int =       logging.get_logger(__name__)

__snake_case				:  Optional[int] =       {
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
__snake_case				:  Optional[int] =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]


def      _lowercase							(    __snake_case      )		->  Optional[Any]:
			__lowerCAmelCase :    List[str]				=			{}
			with open(_lowercase   ,"r"      ) as file:
						for line_number, line in enumerate(_lowercase      ):
									__lowerCAmelCase :    Tuple				=			line.strip()
									if line:
												__lowerCAmelCase :    List[str]				=			line.split()
												__lowerCAmelCase :    Tuple				=			line_number
												__lowerCAmelCase :    List[Any]				=			words[0]
												__lowerCAmelCase :    str				=			value
			return result


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  List[str]:


			for attribute in key.split("."      ):
						__lowerCAmelCase :    Dict				=			getattr(_lowercase   ,_lowercase      )

			__lowerCAmelCase :    Tuple				=			None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_lowercase      ):
									__lowerCAmelCase :    Dict				=			PARAM_MAPPING[full_name.split("."      )[-1]]
									__lowerCAmelCase :    Union[str, Any]				=			"param"

			if weight_type is not None and weight_type != "param":
						__lowerCAmelCase :    Tuple				=			getattr(_lowercase   ,_lowercase      ).shape
			elif weight_type is not None and weight_type == "param":
						__lowerCAmelCase :    Dict				=			hf_pointer
						for attribute in hf_param_name.split("."      ):
									__lowerCAmelCase :    str				=			getattr(_lowercase   ,_lowercase      )
						__lowerCAmelCase :    int				=			shape_pointer.shape

						# let's reduce dimension
						__lowerCAmelCase :    List[str]				=			value[0]
			else:
						__lowerCAmelCase :    Optional[Any]				=			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 :    List[Any]				=			value
			elif weight_type == "weight_g":
						__lowerCAmelCase :    int				=			value
			elif weight_type == "weight_v":
						__lowerCAmelCase :    List[str]				=			value
			elif weight_type == "bias":
						__lowerCAmelCase :    Optional[int]				=			value
			elif weight_type == "param":
						for attribute in hf_param_name.split("."      ):
									__lowerCAmelCase :    Dict				=			getattr(_lowercase   ,_lowercase      )
						__lowerCAmelCase :    Union[str, Any]				=			value
			else:
						__lowerCAmelCase :    int				=			value

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


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  List[Any]:
			__lowerCAmelCase :    Optional[Any]				=			None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_lowercase      ):
									__lowerCAmelCase :    Optional[Any]				=			PARAM_MAPPING[full_name.split("."      )[-1]]
									__lowerCAmelCase :    Union[str, Any]				=			"param"

			if weight_type is not None and weight_type != "param":
						__lowerCAmelCase :    str				=			".".join([key, weight_type]      )
			elif weight_type is not None and weight_type == "param":
						__lowerCAmelCase :    Any				=			".".join([key, hf_param_name]      )
			else:
						__lowerCAmelCase :    Optional[int]				=			key

			__lowerCAmelCase :    List[str]				=			value if "lm_head" in full_key else value[0]


__snake_case				:  Optional[int] =       {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=None   ,__snake_case=None      )		->  List[Any]:
			__lowerCAmelCase :    int				=			False
			for key, mapped_key in MAPPING.items():
						__lowerCAmelCase :    Tuple				=			"wav2vec2." + 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 :    Optional[Any]				=			True
									if "*" in mapped_key:
												__lowerCAmelCase :    List[str]				=			name.split(_lowercase      )[0].split("."      )[-2]
												__lowerCAmelCase :    str				=			mapped_key.replace("*"   ,_lowercase      )
									if "weight_g" in name:
												__lowerCAmelCase :    Union[str, Any]				=			"weight_g"
									elif "weight_v" in name:
												__lowerCAmelCase :    List[str]				=			"weight_v"
									elif "bias" in name:
												__lowerCAmelCase :    Optional[Any]				=			"bias"
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__lowerCAmelCase :    Tuple				=			"weight"
									else:
												__lowerCAmelCase :    Union[str, Any]				=			None
									if hf_dict is not None:
												rename_dict(_lowercase   ,_lowercase   ,_lowercase   ,_lowercase   ,_lowercase      )
									else:
												set_recursively(_lowercase   ,_lowercase   ,_lowercase   ,_lowercase   ,_lowercase      )
									return is_used
			return is_used


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Dict:
			__lowerCAmelCase :    List[Any]				=			[]
			__lowerCAmelCase :    Any				=			fairseq_model.state_dict()

			__lowerCAmelCase :    List[Any]				=			hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						__lowerCAmelCase :    int				=			False
						if "conv_layers" in name:
									load_conv_layer(
									    _lowercase   ,_lowercase   ,_lowercase   ,_lowercase   ,hf_model.config.feat_extract_norm == "group"   ,)
									__lowerCAmelCase :    List[str]				=			True
						else:
									__lowerCAmelCase :    Optional[Any]				=			load_wavaveca_layer(_lowercase   ,_lowercase   ,_lowercase      )
						if not is_used:
									unused_weights.append(_lowercase      )

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


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			__lowerCAmelCase :    Any				=			full_name.split("conv_layers."      )[-1]
			__lowerCAmelCase :    Dict				=			name.split("."      )
			__lowerCAmelCase :    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 :    int				=			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[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:
									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 :    List[str]				=			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 :    Dict				=			value
									logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
			else:
						unused_weights.append(_lowercase      )


@torch.no_grad()
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=None   ,__snake_case=None   ,__snake_case=True   ,__snake_case=False      )		->  Dict:


			if config_path is not None:
						__lowerCAmelCase :    Union[str, Any]				=			WavaVecaConfig.from_pretrained(_lowercase      )
			else:
						__lowerCAmelCase :    str				=			WavaVecaConfig()

			if is_seq_class:
						__lowerCAmelCase :    Union[str, Any]				=			read_txt_into_dict(_lowercase      )
						__lowerCAmelCase :    Optional[Any]				=			idalabel
						__lowerCAmelCase :    str				=			WavaVecaForSequenceClassification(_lowercase      )
						__lowerCAmelCase :    Optional[Any]				=			WavaVecaFeatureExtractor(
						    feature_size=1   ,sampling_rate=16_000   ,padding_value=0   ,do_normalize=_lowercase   ,return_attention_mask=_lowercase   ,)
						feature_extractor.save_pretrained(_lowercase      )

			elif is_finetuned:
						if dict_path:
									__lowerCAmelCase :    Union[str, Any]				=			Dictionary.load(_lowercase      )

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

									# fairseq has the <pad> and <s> switched
									__lowerCAmelCase :    Union[str, Any]				=			0
									__lowerCAmelCase :    Any				=			1
									with open(_lowercase   ,"w"   ,encoding="utf-8"      ) as vocab_handle:
												json.dump(_lowercase   ,_lowercase      )
									__lowerCAmelCase :    List[Any]				=			WavaVecaCTCTokenizer(
									    _lowercase   ,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=_lowercase   ,)
									__lowerCAmelCase :    Optional[Any]				=			True if config.feat_extract_norm == "layer" else False
									__lowerCAmelCase :    Union[str, Any]				=			WavaVecaFeatureExtractor(
									    feature_size=1   ,sampling_rate=16_000   ,padding_value=0   ,do_normalize=_lowercase   ,return_attention_mask=_lowercase   ,)
									__lowerCAmelCase :    Optional[int]				=			WavaVecaProcessor(feature_extractor=_lowercase   ,tokenizer=_lowercase      )
									processor.save_pretrained(_lowercase      )

						__lowerCAmelCase :    List[Any]				=			WavaVecaForCTC(_lowercase      )
			else:
						__lowerCAmelCase :    str				=			WavaVecaForPreTraining(_lowercase      )

			if is_finetuned or is_seq_class:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]   ,arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
			else:
						__lowerCAmelCase :    str				=			argparse.Namespace(task="audio_pretraining"      )
						__lowerCAmelCase :    Dict				=			fairseq.tasks.setup_task(_lowercase      )

						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,task=_lowercase      )

			__lowerCAmelCase :    Any				=			model[0].eval()

			recursively_load_weights(_lowercase   ,_lowercase   ,not is_finetuned      )

			hf_wavavec.save_pretrained(_lowercase      )


if __name__ == "__main__":
	__snake_case				:  int =       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'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	__snake_case				:  int =       parser.parse_args()

	__snake_case				:  str =       not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)



import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


