


'''simple docstring'''






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()
__A :  List[Any]    							=				logging.get_logger(__name__)

__A :  List[str]    							=				{
    "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",
}
__A :  Tuple    							=				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       str							,   A__ :       Tuple							,   A__ :       Any							,   A__ :       Union[str, Any] ):
		'''simple docstring'''





		for attribute in key.split(""".""" ):
				lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   A__ )

		if weight_type is not None:
				lowerCAmelCase_      :				Union[str, Any]      =       getattr(A__							,   A__ ).shape
		else:
				lowerCAmelCase_      :				Any      =       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":
				lowerCAmelCase_      :				Tuple      =       value
		elif weight_type == "weight_g":
				lowerCAmelCase_      :				Optional[int]      =       value
		elif weight_type == "weight_v":
				lowerCAmelCase_      :				Tuple      =       value
		elif weight_type == "bias":
				lowerCAmelCase_      :				List[str]      =       value
		elif weight_type == "running_mean":
				lowerCAmelCase_      :				str      =       value
		elif weight_type == "running_var":
				lowerCAmelCase_      :				List[str]      =       value
		elif weight_type == "num_batches_tracked":
				lowerCAmelCase_      :				Union[str, Any]      =       value
		elif weight_type == "inv_freq":
				lowerCAmelCase_      :				Optional[Any]      =       value
		else:
				lowerCAmelCase_      :				int      =       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__ :       Any							,   A__ :       List[Any] ):
		'''simple docstring'''





		lowerCAmelCase_      :				List[Any]      =       []
		lowerCAmelCase_      :				Tuple      =       fairseq_model.state_dict()

		lowerCAmelCase_      :				Tuple      =       hf_model.wavaveca_conformer.feature_extractor

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





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







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Dict							,   A__ :       Dict=None							,   A__ :       List[Any]=None							,   A__ :       Any=True ):
		'''simple docstring'''





		if config_path is not None:
				lowerCAmelCase_      :				List[str]      =       WavaVecaConformerConfig.from_pretrained(A__							,   hidden_act="""swish""" )
		else:
				lowerCAmelCase_      :				int      =       WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				lowerCAmelCase_      :				str      =       """rotary"""

		if is_finetuned:
				if dict_path:
						lowerCAmelCase_      :				Optional[Any]      =       Dictionary.load(A__ )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowerCAmelCase_      :				Optional[int]      =       target_dict.pad_index
						lowerCAmelCase_      :				List[str]      =       target_dict.bos_index
						lowerCAmelCase_      :				List[Any]      =       target_dict.eos_index
						lowerCAmelCase_      :				Optional[Any]      =       len(target_dict.symbols )
						lowerCAmelCase_      :				Optional[int]      =       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__ )
						lowerCAmelCase_      :				Optional[Any]      =       target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowerCAmelCase_      :				str      =       0
						lowerCAmelCase_      :				List[str]      =       1
						with open(A__							,   """w"""							,   encoding="""utf-8""" ) as vocab_handle:
								json.dump(A__							,   A__ )
						lowerCAmelCase_      :				List[str]      =       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__							,   )
						lowerCAmelCase_      :				Optional[Any]      =       True if config.feat_extract_norm == """layer""" else False
						lowerCAmelCase_      :				str      =       WavaVecaFeatureExtractor(
						    feature_size=1							,   sampling_rate=1_60_00							,   padding_value=0							,   do_normalize=A__							,   return_attention_mask=A__							,   )
						lowerCAmelCase_      :				str      =       WavaVecaProcessor(feature_extractor=A__							,   tokenizer=A__ )
						processor.save_pretrained(A__ )

				lowerCAmelCase_      :				Dict      =       WavaVecaConformerForCTC(A__ )
		else:
				lowerCAmelCase_      :				List[Any]      =       WavaVecaConformerForPreTraining(A__ )

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

				lowerCAmelCase_      :				int      =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,   task=A__ )

		lowerCAmelCase_      :				int      =       model[0].eval()

		recursively_load_weights(A__							,   A__							,   not is_finetuned )

		hf_wavavec.save_pretrained(A__ )


if __name__ == "__main__":
		__A :  Union[str, 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"
		)
		__A :  int    							=				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
		)







'''simple docstring'''






from __future__ import annotations







def     UpperCamelCase_      (   A__ :       int | float | str							,   A__ :       int | float | str ):
  '''simple docstring'''





  if nth_term == "":
    return [""]
  lowerCAmelCase_      :				str      =       int(A__ )
  lowerCAmelCase_      :				Tuple      =       int(A__ )
  lowerCAmelCase_      :				list[str]      =       []
  for temp in range(int(A__ ) ):
    series.append(f'1 / {pow(temp + 1							,   int(A__ ) )}' if series else """1""" )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A :  str    							=				int(input("Enter the last number (nth term) of the P-Series"))
  __A :  Tuple    							=				int(input("Enter the power for  P-Series"))
  print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
  print(p_series(nth_term, power))



