
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_snake_case      = logging.get_logger(__name__)

_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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
_snake_case      = [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]

def  __lowerCamelCase					( _lowercase			)       ->      str:
     UpperCamelCase						    =					{}
     with open(_lowercase						,     'r'			) as file:
          for line_number, line in enumerate(_lowercase			):
               UpperCamelCase						    =					line.strip()
               if line:
                    UpperCamelCase						    =					line.split()
                    UpperCamelCase						    =					line_number
                    UpperCamelCase						    =					words[0]
                    UpperCamelCase						    =					value
     return result

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Optional[Any]:
     for attribute in key.split('.'			):
          UpperCamelCase						    =					getattr(_lowercase						,     _lowercase			)

     UpperCamelCase						    =					None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowercase			):
               UpperCamelCase						    =					PARAM_MAPPING[full_name.split('.'			)[-1]]
               UpperCamelCase						    =					'param'

     if weight_type is not None and weight_type != "param":
          UpperCamelCase						    =					getattr(_lowercase						,     _lowercase			).shape
     elif weight_type is not None and weight_type == "param":
          UpperCamelCase						    =					hf_pointer
          for attribute in hf_param_name.split('.'			):
               UpperCamelCase						    =					getattr(_lowercase						,     _lowercase			)
          UpperCamelCase						    =					shape_pointer.shape

          # let's reduce dimension
          UpperCamelCase						    =					value[0]
     else:
          UpperCamelCase						    =					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						    =					value
     elif weight_type == "weight_g":
          UpperCamelCase						    =					value
     elif weight_type == "weight_v":
          UpperCamelCase						    =					value
     elif weight_type == "bias":
          UpperCamelCase						    =					value
     elif weight_type == "param":
          for attribute in hf_param_name.split('.'			):
               UpperCamelCase						    =					getattr(_lowercase						,     _lowercase			)
          UpperCamelCase						    =					value
     else:
          UpperCamelCase						    =					value

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

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowercase			):
               UpperCamelCase						    =					PARAM_MAPPING[full_name.split('.'			)[-1]]
               UpperCamelCase						    =					'param'

     if weight_type is not None and weight_type != "param":
          UpperCamelCase						    =					'.'.join([key, weight_type]			)
     elif weight_type is not None and weight_type == "param":
          UpperCamelCase						    =					'.'.join([key, hf_param_name]			)
     else:
          UpperCamelCase						    =					key

     UpperCamelCase						    =					value if 'lm_head' in full_key else value[0]


_snake_case      = {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=None						,     _lowercase=None			)       ->      Any:
     UpperCamelCase						    =					False
     for key, mapped_key in MAPPING.items():
          UpperCamelCase						    =					'wav2vec2.' + 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						    =					True
               if "*" in mapped_key:
                    UpperCamelCase						    =					name.split(_lowercase			)[0].split('.'			)[-2]
                    UpperCamelCase						    =					mapped_key.replace('*'						,     _lowercase			)
               if "weight_g" in name:
                    UpperCamelCase						    =					'weight_g'
               elif "weight_v" in name:
                    UpperCamelCase						    =					'weight_v'
               elif "bias" in name:
                    UpperCamelCase						    =					'bias'
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    UpperCamelCase						    =					'weight'
               else:
                    UpperCamelCase						    =					None
               if hf_dict is not None:
                    rename_dict(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)
               else:
                    set_recursively(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)
               return is_used
     return is_used

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     UpperCamelCase						    =					[]
     UpperCamelCase						    =					fairseq_model.state_dict()

     UpperCamelCase						    =					hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCamelCase						    =					False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     hf_model.config.feat_extract_norm == 'group'						,     )
               UpperCamelCase						    =					True
          else:
               UpperCamelCase						    =					load_wavaveca_layer(_lowercase						,     _lowercase						,     _lowercase			)
          if not is_used:
               unused_weights.append(_lowercase			)

     logger.warning(F'Unused weights: {unused_weights}'			)

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      int:
     UpperCamelCase						    =					full_name.split('conv_layers.'			)[-1]
     UpperCamelCase						    =					name.split('.'			)
     UpperCamelCase						    =					int(items[0]			)
     UpperCamelCase						    =					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						    =					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						    =					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						    =					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						    =					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						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=True						,     _lowercase=False			)       ->      Any:
     if config_path is not None:
          UpperCamelCase						    =					WavaVecaConfig.from_pretrained(_lowercase			)
     else:
          UpperCamelCase						    =					WavaVecaConfig()

     if is_seq_class:
          UpperCamelCase						    =					read_txt_into_dict(_lowercase			)
          UpperCamelCase						    =					idalabel
          UpperCamelCase						    =					WavaVecaForSequenceClassification(_lowercase			)
          UpperCamelCase						    =					WavaVecaFeatureExtractor(
              feature_size=1						,     sampling_rate=16000						,     padding_value=0						,     do_normalize=_lowercase						,     return_attention_mask=_lowercase						,     )
          feature_extractor.save_pretrained(_lowercase			)

     elif is_finetuned:
          if dict_path:
               UpperCamelCase						    =					Dictionary.load(_lowercase			)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               UpperCamelCase						    =					target_dict.pad_index
               UpperCamelCase						    =					target_dict.bos_index
               UpperCamelCase						    =					target_dict.eos_index
               UpperCamelCase						    =					len(target_dict.symbols			)
               UpperCamelCase						    =					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						    =					target_dict.indices

               # fairseq has the <pad> and <s> switched
               UpperCamelCase						    =					0
               UpperCamelCase						    =					1
               with open(_lowercase						,     'w'						,     encoding='utf-8'			) as vocab_handle:
                    json.dump(_lowercase						,     _lowercase			)
               UpperCamelCase						    =					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						    =					True if config.feat_extract_norm == 'layer' else False
               UpperCamelCase						    =					WavaVecaFeatureExtractor(
                   feature_size=1						,     sampling_rate=16000						,     padding_value=0						,     do_normalize=_lowercase						,     return_attention_mask=_lowercase						,     )
               UpperCamelCase						    =					WavaVecaProcessor(feature_extractor=_lowercase						,     tokenizer=_lowercase			)
               processor.save_pretrained(_lowercase			)

          UpperCamelCase						    =					WavaVecaForCTC(_lowercase			)
     else:
          UpperCamelCase						    =					WavaVecaForPreTraining(_lowercase			)

     if is_finetuned or is_seq_class:
          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]						,     arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
     else:
          UpperCamelCase						    =					argparse.Namespace(task='audio_pretraining'			)
          UpperCamelCase						    =					fairseq.tasks.setup_task(_lowercase			)

          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,     task=_lowercase			)

     UpperCamelCase						    =					model[0].eval()

     recursively_load_weights(_lowercase						,     _lowercase						,     not is_finetuned			)

     hf_wavavec.save_pretrained(_lowercase			)


if __name__ == "__main__":
 _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'''
 )
 parser.add_argument(
     '''--is_seq_class''',
     action='''store_true''',
     help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
 )
 _snake_case      = parser.parse_args()

 _snake_case      = not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )




def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


