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()
__SCREAMING_SNAKE_CASE						:   Any         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    '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',
}
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						for attribute in key.split("."						):
												_snake_case						:					Tuple    =   getattr(__lowercase ,      __lowercase						)

						if weight_type is not None:
												_snake_case						:					str    =   getattr(__lowercase ,      __lowercase						).shape
						else:
												_snake_case						:					Optional[int]    =   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":
												_snake_case						:					Optional[Any]    =   value
						elif weight_type == "weight_g":
												_snake_case						:					Dict    =   value
						elif weight_type == "weight_v":
												_snake_case						:					Tuple    =   value
						elif weight_type == "bias":
												_snake_case						:					str    =   value
						elif weight_type == "running_mean":
												_snake_case						:					int    =   value
						elif weight_type == "running_var":
												_snake_case						:					Dict    =   value
						elif weight_type == "num_batches_tracked":
												_snake_case						:					Optional[int]    =   value
						elif weight_type == "inv_freq":
												_snake_case						:					Optional[Any]    =   value
						else:
												_snake_case						:					List[Any]    =   value

						logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   []
						_snake_case						:					Dict    =   fairseq_model.state_dict()

						_snake_case						:					List[str]    =   hf_model.wavaveca_conformer.feature_extractor

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

						logger.warning(F"""Unused weights: {unused_weights}"""						)
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						_snake_case						:					Tuple    =   full_name.split("conv_layers."						)[-1]
						_snake_case						:					Union[str, Any]    =   name.split("."						)
						_snake_case						:					List[Any]    =   int(items[0]						)
						_snake_case						:					Union[str, Any]    =   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."""						)
																		_snake_case						:					Tuple    =   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."""						)
																		_snake_case						:					List[str]    =   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."""						)
																		_snake_case						:					int    =   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."""						)
																		_snake_case						:					List[Any]    =   value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
						else:
												unused_weights.append(__lowercase						)
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase=None ,      __lowercase=None ,      __lowercase=True						)	->	List[Any]:

						'''simple docstring'''



						if config_path is not None:
												_snake_case						:					List[str]    =   WavaVecaConformerConfig.from_pretrained(__lowercase ,      hidden_act="swish"						)
						else:
												_snake_case						:					Any    =   WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												_snake_case						:					List[Any]    =   "rotary"

						if is_finetuned:
												if dict_path:
																		_snake_case						:					List[str]    =   Dictionary.load(__lowercase						)

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

																		# fairseq has the <pad> and <s> switched
																		_snake_case						:					List[Any]    =   0
																		_snake_case						:					List[str]    =   1
																		with open(__lowercase ,      "w" ,      encoding="utf-8"						) as vocab_handle:
																								json.dump(__lowercase ,      __lowercase						)
																		_snake_case						:					Optional[Any]    =   WavaVecaCTCTokenizer(
																		    __lowercase ,      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=__lowercase ,      )
																		_snake_case						:					int    =   True if config.feat_extract_norm == "layer" else False
																		_snake_case						:					str    =   WavaVecaFeatureExtractor(
																		    feature_size=1 ,      sampling_rate=16_000 ,      padding_value=0 ,      do_normalize=__lowercase ,      return_attention_mask=__lowercase ,      )
																		_snake_case						:					List[Any]    =   WavaVecaProcessor(feature_extractor=__lowercase ,      tokenizer=__lowercase						)
																		processor.save_pretrained(__lowercase						)

												_snake_case						:					List[str]    =   WavaVecaConformerForCTC(__lowercase						)
						else:
												_snake_case						:					List[str]    =   WavaVecaConformerForPreTraining(__lowercase						)

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

												_snake_case    ,_snake_case    ,_snake_case						:					Tuple    =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,      task=__lowercase						)

						_snake_case						:					List[Any]    =   model[0].eval()

						recursively_load_weights(__lowercase ,      __lowercase ,      not is_finetuned						)

						hf_wavavec.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[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(
				    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				__SCREAMING_SNAKE_CASE						:   Tuple         =			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
				)
def  snake_case						()	->	Dict:

						'''simple docstring'''



						_snake_case						:					List[str]    =   0
						for i in range(1 ,      1_001						):
												total += i**i
						return str(__lowercase						)[-10:]


if __name__ == "__main__":
				print(solution())