'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_A      :				Tuple 				=    logging.get_logger(__name__)

_A      :				Tuple 				=    {
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	str						,		snake_case_	:	str						,		snake_case_	:	Optional[Any]						,		snake_case_	:	Dict				) ->					Union[str, Any]:






	'''simple docstring'''

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

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

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







def 					UpperCamelCase_						( snake_case_	:	Dict						,		snake_case_	:	Dict				) ->					Dict:






	'''simple docstring'''

	__lowerCAmelCase    							=						[]
	__lowerCAmelCase    							=						fairseq_model.state_dict()

	__lowerCAmelCase    							=						hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__lowerCAmelCase    							=						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		hf_model.config.feat_extract_norm == """group"""						,		)
			__lowerCAmelCase    							=						True
		else:
			for key, mapped_key in MAPPING.items():
				__lowerCAmelCase    							=						mapped_key

				if key in name:
					__lowerCAmelCase    							=						True
					if "*" in mapped_key:
						__lowerCAmelCase    							=						name.split(snake_case_				)[0].split("""."""				)[-2]
						__lowerCAmelCase    							=						mapped_key.replace("""*"""						,		snake_case_				)
					if "weight_g" in name:
						__lowerCAmelCase    							=						"""weight_g"""
					elif "weight_v" in name:
						__lowerCAmelCase    							=						"""weight_v"""
					elif "weight" in name:
						__lowerCAmelCase    							=						"""weight"""
					elif "bias" in name:
						__lowerCAmelCase    							=						"""bias"""
					else:
						__lowerCAmelCase    							=						None
					set_recursively(snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_				)
				continue
		if not is_used:
			unused_weights.append(snake_case_				)

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







def 					UpperCamelCase_						( snake_case_	:	Any						,		snake_case_	:	List[str]						,		snake_case_	:	int						,		snake_case_	:	int						,		snake_case_	:	Union[str, Any]				) ->					str:






	'''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	Optional[Any]				) ->					Dict:






	'''simple docstring'''

	__lowerCAmelCase    							=						HubertConfig()
	__lowerCAmelCase    							=						model.config

	__lowerCAmelCase    							=						fs_config.activation_dropout
	__lowerCAmelCase    							=						False
	__lowerCAmelCase    							=						fs_config.attention_dropout
	__lowerCAmelCase    							=						False
	__lowerCAmelCase    							=						eval(fs_config.extractor_conv_feature_layers				)
	__lowerCAmelCase    							=						[x[0] for x in conv_layers]
	__lowerCAmelCase    							=						[x[1] for x in conv_layers]
	__lowerCAmelCase    							=						[x[2] for x in conv_layers]
	__lowerCAmelCase    							=						"""gelu"""
	__lowerCAmelCase    							=						"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	__lowerCAmelCase    							=						False
	__lowerCAmelCase    							=						0.0
	__lowerCAmelCase    							=						0.0
	__lowerCAmelCase    							=						fs_config.activation_fn
	__lowerCAmelCase    							=						fs_config.dropout
	__lowerCAmelCase    							=						fs_config.encoder_embed_dim
	__lowerCAmelCase    							=						0.0_2
	__lowerCAmelCase    							=						fs_config.encoder_ffn_embed_dim
	__lowerCAmelCase    							=						1E-5
	__lowerCAmelCase    							=						0.0
	__lowerCAmelCase    							=						fs_config.encoder_attention_heads
	__lowerCAmelCase    							=						fs_config.conv_pos_groups
	__lowerCAmelCase    							=						fs_config.conv_pos
	__lowerCAmelCase    							=						len(snake_case_				)
	__lowerCAmelCase    							=						fs_config.encoder_layers

	return config







@torch.no_grad()
def 					UpperCamelCase_						( snake_case_	:	Dict						,		snake_case_	:	Any=None				) ->					Optional[Any]:






	'''simple docstring'''

	__lowerCAmelCase    							=						distilhubert().model.model

	if config_path is not None:
		__lowerCAmelCase    							=						HubertConfig.from_pretrained(snake_case_				)
	else:
		__lowerCAmelCase    							=						convert_config(snake_case_				)
	__lowerCAmelCase    							=						model.eval()

	__lowerCAmelCase    							=						WavaVecaFeatureExtractor(
	    feature_size=1						,		sampling_rate=1_60_00						,		padding_value=0						,		do_normalize=snake_case_						,		return_attention_mask=snake_case_						,		)
	__lowerCAmelCase    							=						HubertModel(snake_case_				)

	recursively_load_weights(snake_case_						,		snake_case_				)

	feature_extractor.save_pretrained(snake_case_				)
	hf_model.save_pretrained(snake_case_				)


if __name__ == "__main__":
	_A      :				Tuple 				=    argparse.ArgumentParser()
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	_A      :				int 				=    parser.parse_args()
	convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''

from unittest.mock import patch

import datasets
from datasets import Dataset







def 					UpperCamelCase_						( ) ->					Dict:






 '''simple docstring'''

 __lowerCAmelCase    							=						Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}				)

 with patch("""tqdm.auto.tqdm"""				) as mock_tqdm:
  datasets.disable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_not_called()

  mock_tqdm.reset_mock()

  datasets.enable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_called()






