





"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case     :		Dict       						= logging.get_logger(__name__)

_snake_case     :		Dict       						= {
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							for attribute in key.split("."       ):
														A		=  getattr(UpperCamelCase					,  UpperCamelCase       )

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

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






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  []
							A		=  fairseq_model.state_dict()

							A		=  hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														A		=  False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  hf_model.config.feat_extract_norm == "group"					,  )
																					A		=  True
														else:
																					for key, mapped_key in MAPPING.items():
																												A		=  "hubert." + 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] and not is_finetuned):
																																			A		=  True
																																			if "*" in mapped_key:
																																										A		=  name.split(UpperCamelCase       )[0].split("."       )[-2]
																																										A		=  mapped_key.replace("*"					,  UpperCamelCase       )
																																			if "weight_g" in name:
																																										A		=  "weight_g"
																																			elif "weight_v" in name:
																																										A		=  "weight_v"
																																			elif "weight" in name:
																																										A		=  "weight"
																																			elif "bias" in name:
																																										A		=  "bias"
																																			else:
																																										A		=  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 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  full_name.split("conv_layers."       )[-1]
							A		=  name.split("."       )
							A		=  int(items[0]       )
							A		=  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		=  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		=  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		=  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		=  value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
							else:
														unused_weights.append(UpperCamelCase       )






@torch.no_grad()
def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=None					,  UpperCamelCase=None					,  UpperCamelCase=True       ):
							if config_path is not None:
														A		=  HubertConfig.from_pretrained(UpperCamelCase       )
							else:
														A		=  HubertConfig()

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

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					A		=  target_dict.pad_index
																					A		=  target_dict.bos_index
																					A		=  target_dict.eos_index
																					A		=  len(target_dict.symbols       )
																					A		=  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		=  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		=  True if config.feat_extract_norm == "layer" else False
																					A		=  WavaVecaFeatureExtractor(
																					    feature_size=1					,  sampling_rate=16_000					,  padding_value=0					,  do_normalize=UpperCamelCase					,  return_attention_mask=UpperCamelCase					,  )
																					A		=  WavaVecaProcessor(feature_extractor=UpperCamelCase					,  tokenizer=UpperCamelCase       )
																					processor.save_pretrained(UpperCamelCase       )

														A		=  HubertForCTC(UpperCamelCase       )
							else:
														A		=  HubertModel(UpperCamelCase       )

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

							A		=  model[0].eval()

							recursively_load_weights(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )

							hf_wavavec.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Tuple       						= 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'
		)
		_snake_case     :		List[Any]       						= parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)









"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


