



"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''post_extract_proj''': '''feature_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.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						Optional[Any]     ,				A	:						str     ,				A	:						Optional[int]     ,				A	:						Dict       ):

		'''simple docstring'''


		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

		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							 =		value
		elif weight_type == "weight_g":
				_UpperCAmelCase							 =		value
		elif weight_type == "weight_v":
				_UpperCAmelCase							 =		value
		elif weight_type == "bias":
				_UpperCAmelCase							 =		value
		else:
				_UpperCAmelCase							 =		value

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




def 	UpperCAmelCase     (		A	:						Optional[Any]     ,				A	:						str     ,				A	:						List[Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]
		_UpperCAmelCase							 =		fairseq_model.state_dict()

		_UpperCAmelCase							 =		hf_model.sew.feature_extractor if is_finetuned else hf_model.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							 =		'sew.' + 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]:
										_UpperCAmelCase							 =		True
										if "*" in mapped_key:
												_UpperCAmelCase							 =		name.split(A       )[0].split('.'       )[-2]
												_UpperCAmelCase							 =		mapped_key.replace('*'     ,				A       )
										if "weight_g" in name:
												_UpperCAmelCase							 =		'weight_g'
										elif "weight_v" in name:
												_UpperCAmelCase							 =		'weight_v'
										elif "weight" in name:
												_UpperCAmelCase							 =		'weight'
										elif "bias" in name:
												_UpperCAmelCase							 =		'bias'
										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 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						List[str]     ,				A	:						List[str]     ,				A	:						List[str]     ,				A	:						List[str]       ):

		'''simple docstring'''


		_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:
						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							 =		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							 =		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							 =		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							 =		value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
		else:
				unused_weights.append(A       )




def 	UpperCAmelCase     (		A	:						Dict     ,				A	:						Any       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		SEWConfig()
		if is_finetuned:
				_UpperCAmelCase							 =		model.wav_encoder.wav_model.cfg
		else:
				_UpperCAmelCase							 =		model.cfg

		_UpperCAmelCase							 =		fs_config.conv_bias
		_UpperCAmelCase							 =		eval(fs_config.conv_feature_layers       )
		_UpperCAmelCase							 =		[x[0] for x in conv_layers]
		_UpperCAmelCase							 =		[x[1] for x in conv_layers]
		_UpperCAmelCase							 =		[x[2] for x in conv_layers]
		_UpperCAmelCase							 =		'gelu'
		_UpperCAmelCase							 =		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		_UpperCAmelCase							 =		0.0
		_UpperCAmelCase							 =		fs_config.activation_fn.name
		_UpperCAmelCase							 =		fs_config.encoder_embed_dim
		_UpperCAmelCase							 =		0.02
		_UpperCAmelCase							 =		fs_config.encoder_ffn_embed_dim
		_UpperCAmelCase							 =		1e-5
		_UpperCAmelCase							 =		fs_config.encoder_layerdrop
		_UpperCAmelCase							 =		fs_config.encoder_attention_heads
		_UpperCAmelCase							 =		fs_config.conv_pos_groups
		_UpperCAmelCase							 =		fs_config.conv_pos
		_UpperCAmelCase							 =		len(A       )
		_UpperCAmelCase							 =		fs_config.encoder_layers
		_UpperCAmelCase							 =		fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				_UpperCAmelCase							 =		model.cfg
				_UpperCAmelCase							 =		fs_config.final_dropout
				_UpperCAmelCase							 =		fs_config.layerdrop
		_UpperCAmelCase							 =		fs_config.activation_dropout
		_UpperCAmelCase							 =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		_UpperCAmelCase							 =		fs_config.attention_dropout
		_UpperCAmelCase							 =		fs_config.dropout_input
		_UpperCAmelCase							 =		fs_config.dropout
		_UpperCAmelCase							 =		fs_config.mask_channel_length
		_UpperCAmelCase							 =		fs_config.mask_channel_prob
		_UpperCAmelCase							 =		fs_config.mask_length
		_UpperCAmelCase							 =		fs_config.mask_prob

		_UpperCAmelCase							 =		'Wav2Vec2FeatureExtractor'
		_UpperCAmelCase							 =		'Wav2Vec2CTCTokenizer'

		return config




@torch.no_grad()
def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						Any     ,				A	:						Optional[Any]=None     ,				A	:						Tuple=None     ,				A	:						Union[str, Any]=True       ):

		'''simple docstring'''


		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 ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

		if config_path is not None:
				_UpperCAmelCase							 =		SEWConfig.from_pretrained(A       )
		else:
				_UpperCAmelCase							 =		convert_config(model[0]     ,				A       )
		_UpperCAmelCase							 =		model[0].eval()

		_UpperCAmelCase							 =		True if config.feat_extract_norm == 'layer' else False
		_UpperCAmelCase							 =		WavaVecaFeatureExtractor(
		    feature_size=1     ,				sampling_rate=1_6000     ,				padding_value=0     ,				do_normalize=A     ,				return_attention_mask=A     ,				)

		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.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       )
						with open(A     ,				'w'     ,				encoding='utf-8'       ) as vocab_handle:
								json.dump(target_dict.indices     ,				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							 =		WavaVecaProcessor(feature_extractor=A     ,				tokenizer=A       )
						processor.save_pretrained(A       )

				_UpperCAmelCase							 =		SEWForCTC(A       )
		else:
				_UpperCAmelCase							 =		SEWModel(A       )
				feature_extractor.save_pretrained(A       )

		recursively_load_weights(A     ,				A     ,				A       )

		hf_model.save_pretrained(A       )


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(
					    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
					)
					lowercase  							=					parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)







"""simple docstring"""





def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[[0 for _ in range(A       )] for _ in range(m + 1       )]
		for i in range(m + 1       ):
				_UpperCAmelCase							 =		1

		for n in range(m + 1       ):
				for k in range(1     ,				A       ):
						memo[n][k] += memo[n][k - 1]
						if n - k > 0:
								memo[n][k] += memo[n - k - 1][k]

		return memo[m][m - 1]


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										try:
															lowercase  							=					int(input('''Enter a number: ''').strip())
															print(partition(n))
										except ValueError:
															print('''Please enter a number.''')
					else:
										try:
															lowercase  							=					int(sys.argv[1])
															print(partition(n))
										except ValueError:
															print('''Please pass a number.''')


