


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

SCREAMING_SNAKE_CASE		:Dict											=       {
    '''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      _lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:int						,  lowerCAmelCase_							:int						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Optional[int]							)->Optional[Any]:
			'''simple docstring'''






			for attribute in key.split("."							):
						snake_case_         =   getattr(__UpperCamelCase						,  __UpperCamelCase							)

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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Optional[Any]							)->str:
			'''simple docstring'''






			snake_case_         =   []
			snake_case_         =   fairseq_model.state_dict()

			snake_case_         =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						snake_case_         =   False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  hf_model.config.feat_extract_norm == "group"						,  )
									snake_case_         =   True
						else:
									for key, mapped_key in MAPPING.items():
												snake_case_         =   "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]:
															snake_case_         =   True
															if "*" in mapped_key:
																		snake_case_         =   name.split(__UpperCamelCase							)[0].split("."							)[-2]
																		if not layer_index.isnumeric():
																					continue
																		snake_case_         =   mapped_key.replace("*"						,  __UpperCamelCase							)
															if "weight_g" in name:
																		snake_case_         =   "weight_g"
															elif "weight_v" in name:
																		snake_case_         =   "weight_v"
															elif "weight" in name:
																		snake_case_         =   "weight"
															elif "bias" in name:
																		snake_case_         =   "bias"
															else:
																		snake_case_         =   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      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Union[str, Any]							)->Dict:
			'''simple docstring'''






			snake_case_         =   full_name.split("conv_layers."							)[-1]
			snake_case_         =   name.split("."							)
			snake_case_         =   int(items[0]							)
			snake_case_         =   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.'''
									)
									snake_case_         =   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.'''
									)
									snake_case_         =   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."
									)
									snake_case_         =   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.'''
									)
									snake_case_         =   value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
			else:
						unused_weights.append(__UpperCamelCase							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:int							)->List[Any]:
			'''simple docstring'''






			snake_case_         =   SEWDConfig()
			if is_finetuned:
						snake_case_         =   model.wav_encoder.wav_model.cfg
			else:
						snake_case_         =   model.cfg

			snake_case_         =   fs_config.conv_bias
			snake_case_         =   eval(fs_config.conv_feature_layers							)
			snake_case_         =   [x[0] for x in conv_layers]
			snake_case_         =   [x[1] for x in conv_layers]
			snake_case_         =   [x[2] for x in conv_layers]
			snake_case_         =   "gelu"
			snake_case_         =   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
			snake_case_         =   0.0
			snake_case_         =   fs_config.activation_fn.name
			snake_case_         =   fs_config.encoder_embed_dim
			snake_case_         =   0.0_2
			snake_case_         =   fs_config.encoder_ffn_embed_dim
			snake_case_         =   1e-5
			snake_case_         =   fs_config.encoder_layerdrop
			snake_case_         =   fs_config.encoder_attention_heads
			snake_case_         =   fs_config.conv_pos_groups
			snake_case_         =   fs_config.conv_pos
			snake_case_         =   len(__UpperCamelCase							)
			snake_case_         =   fs_config.encoder_layers
			snake_case_         =   fs_config.squeeze_factor
			# DeBERTa-specific parameters:
			snake_case_         =   fs_config.max_position_embeddings
			snake_case_         =   fs_config.position_buckets
			snake_case_         =   fs_config.share_att_key
			snake_case_         =   fs_config.relative_attention
			snake_case_         =   fs_config.position_biased_input
			snake_case_         =   tuple(fs_config.pos_att_type.split("|"							)							)
			snake_case_         =   fs_config.norm_rel_ebd

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						snake_case_         =   model.cfg
						snake_case_         =   fs_config.final_dropout
						snake_case_         =   fs_config.layerdrop
			snake_case_         =   fs_config.activation_dropout
			snake_case_         =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			snake_case_         =   fs_config.attention_dropout
			snake_case_         =   fs_config.dropout_input
			snake_case_         =   fs_config.dropout
			snake_case_         =   fs_config.mask_channel_length
			snake_case_         =   fs_config.mask_channel_prob
			snake_case_         =   fs_config.mask_length
			snake_case_         =   fs_config.mask_prob

			snake_case_         =   "Wav2Vec2FeatureExtractor"
			snake_case_         =   "Wav2Vec2CTCTokenizer"

			return config



@torch.no_grad()
def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Optional[Any]=None						,  lowerCAmelCase_							:int=None						,  lowerCAmelCase_							:Any=True							)->Dict:
			'''simple docstring'''






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

			if config_path is not None:
						snake_case_         =   SEWDConfig.from_pretrained(__UpperCamelCase							)
			else:
						snake_case_         =   convert_config(model[0]						,  __UpperCamelCase							)
			snake_case_         =   model[0].eval()

			snake_case_         =   True if config.feat_extract_norm == "layer" else False
			snake_case_         =   WavaVecaFeatureExtractor(
			    feature_size=1						,  sampling_rate=16_000						,  padding_value=0						,  do_normalize=__UpperCamelCase						,  return_attention_mask=__UpperCamelCase						,  )

			if is_finetuned:
						if dict_path:
									snake_case_         =   Dictionary.load(__UpperCamelCase							)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									snake_case_         =   target_dict.pad_index
									snake_case_         =   target_dict.bos_index
									snake_case_         =   target_dict.pad_index
									snake_case_         =   target_dict.bos_index
									snake_case_         =   target_dict.eos_index
									snake_case_         =   len(target_dict.symbols							)
									snake_case_         =   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							)
									snake_case_         =   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						,  )
									snake_case_         =   WavaVecaProcessor(feature_extractor=__UpperCamelCase						,  tokenizer=__UpperCamelCase							)
									processor.save_pretrained(__UpperCamelCase							)

						snake_case_         =   SEWDForCTC(__UpperCamelCase							)
			else:
						snake_case_         =   SEWDModel(__UpperCamelCase							)
						feature_extractor.save_pretrained(__UpperCamelCase							)

			recursively_load_weights(__UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase							)

			hf_model.save_pretrained(__UpperCamelCase							)


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(
			    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			SCREAMING_SNAKE_CASE		:int											=       parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)








from typing import Any

import numpy as np



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray							)->bool:
   '''simple docstring'''






   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[1]

   # If matrix not symmetric, exit right away.
   if np.allclose(lowerCAmelCase_						,  matrix.T							) is False:
      return False

   # Get eigenvalues and eignevectors for a symmetric matrix.
   snake_case_      ,      snake_case_         =   np.linalg.eigh(lowerCAmelCase_							)

   # Check sign of all eigenvalues.
   # np.all returns a value of type np.bool_
   return bool(np.all(eigen_values > 0							)							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->Any:
   '''simple docstring'''






   snake_case_         =   np.random.randn(lowerCAmelCase_						,  lowerCAmelCase_							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  random_matrix.T							)
   assert _is_matrix_spd(lowerCAmelCase_							)
   return spd_matrix



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:int = 1_000						,  lowerCAmelCase_							:float = 1e-8						,  )->Any:
   '''simple docstring'''






   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[1]
   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[0]
   assert _is_matrix_spd(lowerCAmelCase_							)

   # Initialize solution guess, residual, search direction.
   snake_case_         =   np.zeros((np.shape(lowerCAmelCase_							)[0], 1)							)
   snake_case_         =   np.copy(lowerCAmelCase_							)
   snake_case_         =   np.copy(lowerCAmelCase_							)

   # Set initial errors in solution guess and residual.
   snake_case_         =   1e9
   snake_case_         =   1e9
   snake_case_         =   1e9

   # Set iteration counter to threshold number of iterations.
   snake_case_         =   0

   while error > tol:
      # Save this value so we only calculate the matrix-vector product once.
      snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

      # The main algorithm.

      # Update search direction magnitude.
      snake_case_         =   np.dot(ra.T						,  lowerCAmelCase_							) / np.dot(pa.T						,  lowerCAmelCase_							)
      # Update solution guess.
      snake_case_         =   xa + alpha * pa
      # Calculate new residual.
      snake_case_         =   ra - alpha * w
      # Calculate new Krylov subspace scale.
      snake_case_         =   np.dot(r.T						,  lowerCAmelCase_							) / np.dot(ra.T						,  lowerCAmelCase_							)
      # Calculate new A conjuage search direction.
      snake_case_         =   r + beta * pa

      # Calculate errors.
      snake_case_         =   np.linalg.norm(r - ra							)
      snake_case_         =   np.linalg.norm(x - xa							)
      snake_case_         =   np.maximum(lowerCAmelCase_						,  lowerCAmelCase_							)

      # Update variables.
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)

      # Update number of iterations.
      iterations += 1
      if iterations > max_iterations:
         break

   return x



def      _lowerCAmelCase  (							)->None:
   '''simple docstring'''






   snake_case_         =   3
   snake_case_         =   _create_spd_matrix(lowerCAmelCase_							)
   snake_case_         =   np.random.randn(lowerCAmelCase_						,  1							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Numpy solution.
   snake_case_         =   np.linalg.solve(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Our implementation.
   snake_case_         =   conjugate_gradient(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Ensure both solutions are close to x_true (and therefore one another).
   assert np.linalg.norm(x_numpy - x_true							) <= 1e-6
   assert np.linalg.norm(x_conjugate_gradient - x_true							) <= 1e-6


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   test_conjugate_gradient()




