




"""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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowerCamelCase	:      str   =							{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    '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 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	for attribute in key.split('''.'''						):
		A_	:					Dict						 =		getattr(_UpperCAmelCase	,   _UpperCAmelCase						)

	if weight_type is not None:
		A_	:					Union[str, Any]						 =		getattr(_UpperCAmelCase	,   _UpperCAmelCase						).shape
	else:
		A_	:					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":
		A_	:					int						 =		value
	elif weight_type == "weight_g":
		A_	:					Tuple						 =		value
	elif weight_type == "weight_v":
		A_	:					Optional[Any]						 =		value
	elif weight_type == "bias":
		A_	:					Any						 =		value
	else:
		A_	:					int						 =		value

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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Dict						 =		[]
	A_	:					List[Any]						 =		fairseq_model.state_dict()

	A_	:					Tuple						 =		hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		A_	:					Dict						 =		False
		if "conv_layers" in name:
			load_conv_layer(
			    _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   hf_model.config.feat_extract_norm == '''group'''	,   )
			A_	:					Dict						 =		True
		else:
			for key, mapped_key in MAPPING.items():
				A_	:					List[Any]						 =		'''sew_d.''' + 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]:
					A_	:					Dict						 =		True
					if "*" in mapped_key:
						A_	:					Union[str, Any]						 =		name.split(_UpperCAmelCase						)[0].split('''.'''						)[-2]
						if not layer_index.isnumeric():
							continue
						A_	:					List[str]						 =		mapped_key.replace('''*'''	,   _UpperCAmelCase						)
					if "weight_g" in name:
						A_	:					str						 =		'''weight_g'''
					elif "weight_v" in name:
						A_	:					Optional[Any]						 =		'''weight_v'''
					elif "weight" in name:
						A_	:					int						 =		'''weight'''
					elif "bias" in name:
						A_	:					Tuple						 =		'''bias'''
					else:
						A_	:					Optional[Any]						 =		None
					set_recursively(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)
				continue
		if not is_used:
			unused_weights.append(_UpperCAmelCase						)

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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[Any]						 =		full_name.split('''conv_layers.'''						)[-1]
	A_	:					Optional[Any]						 =		name.split('''.'''						)
	A_	:					Optional[int]						 =		int(items[0]						)
	A_	:					Optional[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."""
			)
			A_	:					List[str]						 =		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."""
			)
			A_	:					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."
			)
			A_	:					Dict						 =		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."""
			)
			A_	:					List[str]						 =		value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
	else:
		unused_weights.append(_UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		SEWDConfig()
	if is_finetuned:
		A_	:					str						 =		model.wav_encoder.wav_model.cfg
	else:
		A_	:					Optional[int]						 =		model.cfg

	A_	:					List[Any]						 =		fs_config.conv_bias
	A_	:					Union[str, Any]						 =		eval(fs_config.conv_feature_layers						)
	A_	:					List[Any]						 =		[x[0] for x in conv_layers]
	A_	:					Dict						 =		[x[1] for x in conv_layers]
	A_	:					Dict						 =		[x[2] for x in conv_layers]
	A_	:					Optional[Any]						 =		'''gelu'''
	A_	:					Any						 =		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
	A_	:					int						 =		0.0
	A_	:					str						 =		fs_config.activation_fn.name
	A_	:					Optional[Any]						 =		fs_config.encoder_embed_dim
	A_	:					int						 =		0.02
	A_	:					List[Any]						 =		fs_config.encoder_ffn_embed_dim
	A_	:					List[str]						 =		1E-5
	A_	:					Union[str, Any]						 =		fs_config.encoder_layerdrop
	A_	:					Union[str, Any]						 =		fs_config.encoder_attention_heads
	A_	:					List[str]						 =		fs_config.conv_pos_groups
	A_	:					Optional[int]						 =		fs_config.conv_pos
	A_	:					List[Any]						 =		len(_UpperCAmelCase						)
	A_	:					Optional[int]						 =		fs_config.encoder_layers
	A_	:					Tuple						 =		fs_config.squeeze_factor
	# DeBERTa-specific parameters:
	A_	:					Dict						 =		fs_config.max_position_embeddings
	A_	:					Optional[Any]						 =		fs_config.position_buckets
	A_	:					Union[str, Any]						 =		fs_config.share_att_key
	A_	:					Any						 =		fs_config.relative_attention
	A_	:					Union[str, Any]						 =		fs_config.position_biased_input
	A_	:					Optional[int]						 =		tuple(fs_config.pos_att_type.split('''|'''						)						)
	A_	:					List[str]						 =		fs_config.norm_rel_ebd

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		A_	:					int						 =		model.cfg
		A_	:					Tuple						 =		fs_config.final_dropout
		A_	:					Tuple						 =		fs_config.layerdrop
	A_	:					int						 =		fs_config.activation_dropout
	A_	:					List[str]						 =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	A_	:					Tuple						 =		fs_config.attention_dropout
	A_	:					Dict						 =		fs_config.dropout_input
	A_	:					Dict						 =		fs_config.dropout
	A_	:					Union[str, Any]						 =		fs_config.mask_channel_length
	A_	:					Any						 =		fs_config.mask_channel_prob
	A_	:					Tuple						 =		fs_config.mask_length
	A_	:					Union[str, Any]						 =		fs_config.mask_prob

	A_	:					Union[str, Any]						 =		'''Wav2Vec2FeatureExtractor'''
	A_	:					Optional[Any]						 =		'''Wav2Vec2CTCTokenizer'''

	return config

@torch.no_grad()
def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=True						):




	"""simple docstring"""






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

	if config_path is not None:
		A_	:					str						 =		SEWDConfig.from_pretrained(_UpperCAmelCase						)
	else:
		A_	:					List[str]						 =		convert_config(model[0]	,   _UpperCAmelCase						)
	A_	:					Optional[int]						 =		model[0].eval()

	A_	:					Optional[Any]						 =		True if config.feat_extract_norm == '''layer''' else False
	A_	:					Any						 =		WavaVecaFeatureExtractor(
	    feature_size=1	,   sampling_rate=1_6000	,   padding_value=0	,   do_normalize=_UpperCAmelCase	,   return_attention_mask=_UpperCAmelCase	,   )

	if is_finetuned:
		if dict_path:
			A_	:					Union[str, Any]						 =		Dictionary.load(_UpperCAmelCase						)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A_	:					List[str]						 =		target_dict.pad_index
			A_	:					Optional[Any]						 =		target_dict.bos_index
			A_	:					List[str]						 =		target_dict.pad_index
			A_	:					List[Any]						 =		target_dict.bos_index
			A_	:					str						 =		target_dict.eos_index
			A_	:					List[str]						 =		len(target_dict.symbols						)
			A_	:					Optional[int]						 =		os.path.join(_UpperCAmelCase	,   '''vocab.json'''						)
			if not os.path.isdir(_UpperCAmelCase						):
				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCAmelCase						)						)
				return
			os.makedirs(_UpperCAmelCase	,   exist_ok=_UpperCAmelCase						)
			with open(_UpperCAmelCase	,   '''w'''	,   encoding='''utf-8'''						) as vocab_handle:
				json.dump(target_dict.indices	,   _UpperCAmelCase						)
			A_	:					Optional[Any]						 =		WavaVecaCTCTokenizer(
			    _UpperCAmelCase	,   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=_UpperCAmelCase	,   )
			A_	:					List[str]						 =		WavaVecaProcessor(feature_extractor=_UpperCAmelCase	,   tokenizer=_UpperCAmelCase						)
			processor.save_pretrained(_UpperCAmelCase						)

		A_	:					int						 =		SEWDForCTC(_UpperCAmelCase						)
	else:
		A_	:					Any						 =		SEWDModel(_UpperCAmelCase						)
		feature_extractor.save_pretrained(_UpperCAmelCase						)

	recursively_load_weights(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)

	hf_model.save_pretrained(_UpperCAmelCase						)


if __name__ == "__main__":
		_lowerCamelCase	:      Optional[Any]   =							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'
		)
		_lowerCamelCase	:      str   =							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"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





