




'''simple docstring'''




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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_      :		str							      =       logging.get_logger(__name__)

a_      :		List[Any]							      =       {
    """post_extract_proj""": """feature_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.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      a_							(      __snake_case							:			Dict   ,       __snake_case							:			List[str]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Union[str, Any]   )							->						List[Any]:



       """simple docstring"""
       for attribute in key.split('''.'''   ):
              lowerCamelCase_							=getattr(__snake_case   ,       __snake_case   )

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

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





def      a_							(      __snake_case							:			int   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Optional[int]   )							->						Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=[]
       lowerCamelCase_							=fairseq_model.state_dict()

       lowerCamelCase_							=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCamelCase_							=False
              if "conv_layers" in name:
                     load_conv_layer(
                         __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       hf_model.config.feat_extract_norm == '''group'''   ,       )
                     lowerCamelCase_							=True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase_							='''sew.''' + 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]:
                                   lowerCamelCase_							=True
                                   if "*" in mapped_key:
                                          lowerCamelCase_							=name.split(__snake_case   )[0].split('''.'''   )[-2]
                                          lowerCamelCase_							=mapped_key.replace('''*'''   ,       __snake_case   )
                                   if "weight_g" in name:
                                          lowerCamelCase_							='''weight_g'''
                                   elif "weight_v" in name:
                                          lowerCamelCase_							='''weight_v'''
                                   elif "weight" in name:
                                          lowerCamelCase_							='''weight'''
                                   elif "bias" in name:
                                          lowerCamelCase_							='''bias'''
                                   else:
                                          lowerCamelCase_							=None
                                   set_recursively(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   )
                            continue
              if not is_used:
                     unused_weights.append(__snake_case   )

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





def      a_							(      __snake_case							:			Tuple   ,       __snake_case							:			Any   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Dict   )							->						str:



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





def      a_							(      __snake_case							:			List[str]   ,       __snake_case							:			List[Any]   )							->						Tuple:



       """simple docstring"""
       lowerCamelCase_							=SEWConfig()
       if is_finetuned:
              lowerCamelCase_							=model.wav_encoder.wav_model.cfg
       else:
              lowerCamelCase_							=model.cfg

       lowerCamelCase_							=fs_config.conv_bias
       lowerCamelCase_							=eval(fs_config.conv_feature_layers   )
       lowerCamelCase_							=[x[0] for x in conv_layers]
       lowerCamelCase_							=[x[1] for x in conv_layers]
       lowerCamelCase_							=[x[2] for x in conv_layers]
       lowerCamelCase_							='''gelu'''
       lowerCamelCase_							='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       lowerCamelCase_							=0.0
       lowerCamelCase_							=fs_config.activation_fn.name
       lowerCamelCase_							=fs_config.encoder_embed_dim
       lowerCamelCase_							=0.0_2
       lowerCamelCase_							=fs_config.encoder_ffn_embed_dim
       lowerCamelCase_							=1e-5
       lowerCamelCase_							=fs_config.encoder_layerdrop
       lowerCamelCase_							=fs_config.encoder_attention_heads
       lowerCamelCase_							=fs_config.conv_pos_groups
       lowerCamelCase_							=fs_config.conv_pos
       lowerCamelCase_							=len(__snake_case   )
       lowerCamelCase_							=fs_config.encoder_layers
       lowerCamelCase_							=fs_config.squeeze_factor

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

       lowerCamelCase_							='''Wav2Vec2FeatureExtractor'''
       lowerCamelCase_							='''Wav2Vec2CTCTokenizer'''

       return config





@torch.no_grad()
def      a_							(      __snake_case							:			Union[str, Any]   ,       __snake_case							:			Tuple   ,       __snake_case							:			str=None   ,       __snake_case							:			str=None   ,       __snake_case							:			Dict=True   )							->						List[str]:



       """simple docstring"""

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

       if config_path is not None:
              lowerCamelCase_							=SEWConfig.from_pretrained(__snake_case   )
       else:
              lowerCamelCase_							=convert_config(model[0]   ,       __snake_case   )
       lowerCamelCase_							=model[0].eval()

       lowerCamelCase_							=True if config.feat_extract_norm == '''layer''' else False
       lowerCamelCase_							=WavaVecaFeatureExtractor(
           feature_size=1   ,       sampling_rate=1_6000   ,       padding_value=0   ,       do_normalize=__snake_case   ,       return_attention_mask=__snake_case   ,       )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase_							=Dictionary.load(__snake_case   )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCamelCase_							=target_dict.pad_index
                     lowerCamelCase_							=target_dict.bos_index
                     lowerCamelCase_							=target_dict.pad_index
                     lowerCamelCase_							=target_dict.bos_index
                     lowerCamelCase_							=target_dict.eos_index
                     lowerCamelCase_							=len(target_dict.symbols   )
                     lowerCamelCase_							=os.path.join(__snake_case   ,       '''vocab.json'''   )
                     if not os.path.isdir(__snake_case   ):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case   )   )
                            return
                     os.makedirs(__snake_case   ,       exist_ok=__snake_case   )
                     with open(__snake_case   ,       '''w'''   ,       encoding='''utf-8'''   ) as vocab_handle:
                            json.dump(target_dict.indices   ,       __snake_case   )
                     lowerCamelCase_							=WavaVecaCTCTokenizer(
                         __snake_case   ,       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=__snake_case   ,       )
                     lowerCamelCase_							=WavaVecaProcessor(feature_extractor=__snake_case   ,       tokenizer=__snake_case   )
                     processor.save_pretrained(__snake_case   )

              lowerCamelCase_							=SEWForCTC(__snake_case   )
       else:
              lowerCamelCase_							=SEWModel(__snake_case   )
              feature_extractor.save_pretrained(__snake_case   )

       recursively_load_weights(__snake_case   ,       __snake_case   ,       __snake_case   )

       hf_model.save_pretrained(__snake_case   )


if __name__ == "__main__":
     a_      :		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"""
     )
     a_      :		List[Any]							      =       parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )










def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

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

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

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

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



