

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()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    '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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Dict					, lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					Union[str, Any]	)				->    List[str]:

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

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

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					Any	)				->    Optional[int]:
			_UpperCAmelCase    :						List[str]							=      []
			_UpperCAmelCase    :						Optional[Any]							=      fairseq_model.state_dict()

			_UpperCAmelCase    :						Optional[Any]							=      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCAmelCase    :						List[str]							=      False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCAmelCase					, __UpperCAmelCase					, __UpperCAmelCase					, __UpperCAmelCase					, hf_model.config.feat_extract_norm == "group"					, )
									_UpperCAmelCase    :						List[str]							=      True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCAmelCase    :						List[Any]							=      "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):
															_UpperCAmelCase    :						List[Any]							=      True
															if "*" in mapped_key:
																		_UpperCAmelCase    :						List[Any]							=      name.split(__UpperCAmelCase	)[0].split("."	)[-2]
																		_UpperCAmelCase    :						Tuple							=      mapped_key.replace("*"					, __UpperCAmelCase	)
															if "weight_g" in name:
																		_UpperCAmelCase    :						List[str]							=      "weight_g"
															elif "weight_v" in name:
																		_UpperCAmelCase    :						Union[str, Any]							=      "weight_v"
															elif "weight" in name:
																		_UpperCAmelCase    :						List[str]							=      "weight"
															elif "bias" in name:
																		_UpperCAmelCase    :						Optional[Any]							=      "bias"
															else:
																		_UpperCAmelCase    :						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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					Any					, lowerCAmelCase:					Tuple					, lowerCAmelCase:					List[Any]					, lowerCAmelCase:					str	)				->    Optional[int]:
			_UpperCAmelCase    :						Optional[int]							=      full_name.split("conv_layers."	)[-1]
			_UpperCAmelCase    :						str							=      name.split("."	)
			_UpperCAmelCase    :						List[Any]							=      int(items[0]	)
			_UpperCAmelCase    :						int							=      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    :						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.'
									)
									_UpperCAmelCase    :						Dict							=      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    :						Optional[Any]							=      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    :						List[Any]							=      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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Any					, lowerCAmelCase:					Tuple=None					, lowerCAmelCase:					Union[str, Any]=None					, lowerCAmelCase:					List[str]=True	)				->    Optional[int]:

			if config_path is not None:
						_UpperCAmelCase    :						Optional[Any]							=      HubertConfig.from_pretrained(__UpperCAmelCase	)
			else:
						_UpperCAmelCase    :						Any							=      HubertConfig()

			if is_finetuned:
						if dict_path:
									_UpperCAmelCase    :						Optional[Any]							=      Dictionary.load(__UpperCAmelCase	)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCAmelCase    :						Dict							=      target_dict.pad_index
									_UpperCAmelCase    :						str							=      target_dict.bos_index
									_UpperCAmelCase    :						str							=      target_dict.eos_index
									_UpperCAmelCase    :						Optional[Any]							=      len(target_dict.symbols	)
									_UpperCAmelCase    :						Union[str, Any]							=      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	)
									_UpperCAmelCase    :						str							=      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					, )
									_UpperCAmelCase    :						List[Any]							=      True if config.feat_extract_norm == "layer" else False
									_UpperCAmelCase    :						List[Any]							=      WavaVecaFeatureExtractor(
									    feature_size=1					, sampling_rate=1_6000					, padding_value=0					, do_normalize=__UpperCAmelCase					, return_attention_mask=__UpperCAmelCase					, )
									_UpperCAmelCase    :						List[Any]							=      WavaVecaProcessor(feature_extractor=__UpperCAmelCase					, tokenizer=__UpperCAmelCase	)
									processor.save_pretrained(__UpperCAmelCase	)

						_UpperCAmelCase    :						Any							=      HubertForCTC(__UpperCAmelCase	)
			else:
						_UpperCAmelCase    :						str							=      HubertModel(__UpperCAmelCase	)

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

			_UpperCAmelCase    :						Union[str, Any]							=      model[0].eval()

			recursively_load_weights(__UpperCAmelCase					, __UpperCAmelCase					, __UpperCAmelCase	)

			hf_wavavec.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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_            = parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


