'''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()
UpperCAmelCase_			    =							logging.get_logger(__name__)

UpperCAmelCase_			    =							{
    """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""",
}
UpperCAmelCase_			    =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def       lowerCAmelCase_     (		lowercase:							Any  ,					lowercase:							Union[str, Any]  ,					lowercase:							Optional[int]  ,					lowercase:							str  ,					lowercase:							Union[str, Any]		)    ->				Optional[Any]:







 '''simple docstring'''

 for attribute in key.split('''.'''		):
  _UpperCamelCase:     Dict							= getattr(lowercase  ,					lowercase		)

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

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




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							Optional[int]  ,					lowercase:							str		)    ->				Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= []
 _UpperCamelCase:     List[Any]							= fairseq_model.state_dict()

 _UpperCamelCase:     str							= hf_model.wavaveca_conformer.feature_extractor

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







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= full_name.split('''conv_layers.'''		)[-1]
 _UpperCamelCase:     Optional[int]							= name.split('''.'''		)
 _UpperCamelCase:     Any							= int(items[0]		)
 _UpperCamelCase:     Tuple							= 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."""		)
   _UpperCamelCase:     List[str]							= 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."""		)
   _UpperCamelCase:     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."""		)
   _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:
   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."""		)
   _UpperCamelCase:     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       lowerCAmelCase_     (		lowercase:							Union[str, Any]  ,					lowercase:							str  ,					lowercase:							Dict=None  ,					lowercase:							Optional[int]=None  ,					lowercase:							int=True		)    ->				Any:







 '''simple docstring'''

 if config_path is not None:
  _UpperCamelCase:     str							= WavaVecaConformerConfig.from_pretrained(lowercase  ,					hidden_act='''swish'''		)
 else:
  _UpperCamelCase:     str							= WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  _UpperCamelCase:     int							= '''rotary'''

 if is_finetuned:
  if dict_path:
   _UpperCamelCase:     List[Any]							= Dictionary.load(lowercase		)

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

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

  _UpperCamelCase:     Dict							= WavaVecaConformerForCTC(lowercase		)
 else:
  _UpperCamelCase:     int							= WavaVecaConformerForPreTraining(lowercase		)

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

  _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     Tuple							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,					task=lowercase		)

 _UpperCamelCase:     str							= model[0].eval()

 recursively_load_weights(lowercase  ,					lowercase  ,					not is_finetuned		)

 hf_wavavec.save_pretrained(lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							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'''
  )
  UpperCAmelCase_			    =							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
  )
from __future__ import annotations




def       lowerCAmelCase_     (		lowercase:							int = 4		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= abs(lowercase		) or 4
 return [[1 + x + y * row_size for x in range(lowercase		)] for y in range(lowercase		)]




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(transpose(lowercase		)		)
 # OR.. transpose(reverse_column(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(reverse_column(lowercase		)		)
 # OR.. reverse_column(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_column(transpose(lowercase		)		)
 # OR.. transpose(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     int							= [list(lowercase		) for x in zip(*lowercase		)]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= matrix[::-1]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= [x[::-1] for x in matrix]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				None:







 '''simple docstring'''
 for i in matrix:
  print(*lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 90 counterclockwise:\n''')
  print_matrix(rotate_aa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 180:\n''')
  print_matrix(rotate_aaa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 270 counterclockwise:\n''')
  print_matrix(rotate_aaa(matrix))