





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)

UpperCamelCase__   : Optional[int]					  = {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase__   : Union[str, Any]					  = [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def    A_(			A				,      A				,      A				,      A				,      A   ):
	for attribute in key.split("""."""   ):
		UpperCAmelCase_								=     getattr(A				,      A   )

	if weight_type is not None:
		UpperCAmelCase_								=     getattr(A				,      A   ).shape
	else:
		UpperCAmelCase_								=     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":
		UpperCAmelCase_								=     value
	elif weight_type == "weight_g":
		UpperCAmelCase_								=     value
	elif weight_type == "weight_v":
		UpperCAmelCase_								=     value
	elif weight_type == "bias":
		UpperCAmelCase_								=     value
	elif weight_type == "running_mean":
		UpperCAmelCase_								=     value
	elif weight_type == "running_var":
		UpperCAmelCase_								=     value
	elif weight_type == "num_batches_tracked":
		UpperCAmelCase_								=     value
	elif weight_type == "inv_freq":
		UpperCAmelCase_								=     value
	else:
		UpperCAmelCase_								=     value

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




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     fairseq_model.state_dict()

	UpperCAmelCase_								=     hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCAmelCase_								=     False
		if "conv_layers" in name:
			load_conv_layer(
			    A				,      A				,      A				,      A				,      hf_model.config.feat_extract_norm == """group"""				,      )
			UpperCAmelCase_								=     True
		else:
			for key, mapped_key in MAPPING.items():
				UpperCAmelCase_								=     """wav2vec2_conformer.""" + 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]:
					UpperCAmelCase_								=     True
					if "*" in mapped_key:
						UpperCAmelCase_								=     name.split(A   )[0].split("""."""   )[-2]
						UpperCAmelCase_								=     mapped_key.replace("""*"""				,      A   )
					if "pos_bias_u" in name:
						UpperCAmelCase_								=     None
					elif "pos_bias_v" in name:
						UpperCAmelCase_								=     None
					elif "weight_g" in name:
						UpperCAmelCase_								=     """weight_g"""
					elif "weight_v" in name:
						UpperCAmelCase_								=     """weight_v"""
					elif "bias" in name:
						UpperCAmelCase_								=     """bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						UpperCAmelCase_								=     """weight"""
					elif "running_mean" in name:
						UpperCAmelCase_								=     """running_mean"""
					elif "inv_freq" in name:
						UpperCAmelCase_								=     """inv_freq"""
					elif "running_var" in name:
						UpperCAmelCase_								=     """running_var"""
					elif "num_batches_tracked" in name:
						UpperCAmelCase_								=     """num_batches_tracked"""
					else:
						UpperCAmelCase_								=     None
					set_recursively(A				,      A				,      A				,      A				,      A   )
				continue
		if not is_used:
			unused_weights.append(A   )

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




def    A_(			A				,      A				,      A				,      A				,      A   ):
	UpperCAmelCase_								=     full_name.split("""conv_layers."""   )[-1]
	UpperCAmelCase_								=     name.split("""."""   )
	UpperCAmelCase_								=     int(items[0]   )
	UpperCAmelCase_								=     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."""   )
			UpperCAmelCase_								=     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."""   )
			UpperCAmelCase_								=     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."""   )
			UpperCAmelCase_								=     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."""   )
			UpperCAmelCase_								=     value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
	else:
		unused_weights.append(A   )




@torch.no_grad()
def    A_(			A				,      A				,      A=None				,      A=None				,      A=True   ):
	if config_path is not None:
		UpperCAmelCase_								=     WavaVecaConformerConfig.from_pretrained(A				,      hidden_act="""swish"""   )
	else:
		UpperCAmelCase_								=     WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		UpperCAmelCase_								=     """rotary"""

	if is_finetuned:
		if dict_path:
			UpperCAmelCase_								=     Dictionary.load(A   )

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

			# fairseq has the <pad> and <s> switched
			UpperCAmelCase_								=     0
			UpperCAmelCase_								=     1
			with open(A				,      """w"""				,      encoding="""utf-8"""   ) as vocab_handle:
				json.dump(A				,      A   )
			UpperCAmelCase_								=     WavaVecaCTCTokenizer(
			    A				,      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=A				,      )
			UpperCAmelCase_								=     True if config.feat_extract_norm == """layer""" else False
			UpperCAmelCase_								=     WavaVecaFeatureExtractor(
			    feature_size=1				,      sampling_rate=16000				,      padding_value=0				,      do_normalize=A				,      return_attention_mask=A				,      )
			UpperCAmelCase_								=     WavaVecaProcessor(feature_extractor=A				,      tokenizer=A   )
			processor.save_pretrained(A   )

		UpperCAmelCase_								=     WavaVecaConformerForCTC(A   )
	else:
		UpperCAmelCase_								=     WavaVecaConformerForPreTraining(A   )

	if is_finetuned:
		UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]				,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
	else:
		UpperCAmelCase_								=     argparse.Namespace(task="""audio_pretraining"""   )
		UpperCAmelCase_								=     fairseq.tasks.setup_task(A   )

		UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,      task=A   )

	UpperCAmelCase_								=     model[0].eval()

	recursively_load_weights(A				,      A				,      not is_finetuned   )

	hf_wavavec.save_pretrained(A   )


if __name__ == "__main__":
				UpperCamelCase__   : 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"""
				)
				UpperCamelCase__   : Any					  = parser.parse_args()
				convert_wavaveca_conformer_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def    A_(			A				,      A   ):
	assert isinstance(A				,      A   )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader(A				,      features=A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      split=A   ).read()
	_check_text_dataset(A				,      A   )
	assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""				,      [str, list]   )
def    A_(			A				,      A				,      A   ):
	if issubclass(A				,      A   ):
		UpperCAmelCase_								=     text_path
	elif issubclass(A				,      A   ):
		UpperCAmelCase_								=     [text_path]
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




def    A_(			A				,      A				,      A=("train",)   ):
	assert isinstance(A				,      A   )
	for split in splits:
		UpperCAmelCase_								=     dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      features=A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	if split:
		UpperCAmelCase_								=     {split: text_path}
	else:
		UpperCAmelCase_								=     """train"""
		UpperCAmelCase_								=     {"""train""": text_path, """test""": text_path}
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A				,      splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )


