



"""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()
lowercase__       :    List[str]				       =      logging.get_logger(__name__)

lowercase__       :    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 __lowercase     (				_a						,     _a						,     _a						,     _a						,     _a       ):
    for attribute in key.split('''.'''       ):
        snake_case_					:	str							=      getattr(__A						,     __A       )

    if weight_type is not None:
        snake_case_					:	int							=      getattr(__A						,     __A       ).shape
    else:
        snake_case_					:	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":
        snake_case_					:	str							=      value
    elif weight_type == "weight_g":
        snake_case_					:	Dict							=      value
    elif weight_type == "weight_v":
        snake_case_					:	Tuple							=      value
    elif weight_type == "bias":
        snake_case_					:	str							=      value
    else:
        snake_case_					:	Dict							=      value

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




def __lowercase     (				_a						,     _a						,     _a       ):
    snake_case_					:	int							=      []
    snake_case_					:	Tuple							=      fairseq_model.state_dict()

    snake_case_					:	Tuple							=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_					:	Optional[int]							=      False
        if "conv_layers" in name:
            load_conv_layer(
                __A						,     __A						,     __A						,     __A						,     hf_model.config.feat_extract_norm == '''group'''						,     )
            snake_case_					:	List[str]							=      True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_					:	Optional[int]							=      '''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]:
                    snake_case_					:	Dict							=      True
                    if "*" in mapped_key:
                        snake_case_					:	Optional[int]							=      name.split(__A       )[0].split('''.'''       )[-2]
                        snake_case_					:	Optional[int]							=      mapped_key.replace('''*'''						,     __A       )
                    if "weight_g" in name:
                        snake_case_					:	str							=      '''weight_g'''
                    elif "weight_v" in name:
                        snake_case_					:	Optional[int]							=      '''weight_v'''
                    elif "weight" in name:
                        snake_case_					:	Optional[int]							=      '''weight'''
                    elif "bias" in name:
                        snake_case_					:	List[Any]							=      '''bias'''
                    else:
                        snake_case_					:	str							=      None
                    set_recursively(__A						,     __A						,     __A						,     __A						,     __A       )
                continue
        if not is_used:
            unused_weights.append(__A       )

    logger.warning(f"Unused weights: {unused_weights}"       )




def __lowercase     (				_a						,     _a						,     _a						,     _a						,     _a       ):
    snake_case_					:	str							=      full_name.split('''conv_layers.'''       )[-1]
    snake_case_					:	Optional[int]							=      name.split('''.'''       )
    snake_case_					:	List[Any]							=      int(items[0]       )
    snake_case_					:	Optional[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."
            )
            snake_case_					:	Any							=      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_					:	Optional[Any]							=      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_					:	int							=      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_					:	int							=      value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
    else:
        unused_weights.append(__A       )




def __lowercase     (				_a						,     _a       ):
    snake_case_					:	Dict							=      SEWConfig()
    if is_finetuned:
        snake_case_					:	Tuple							=      model.wav_encoder.wav_model.cfg
    else:
        snake_case_					:	Union[str, Any]							=      model.cfg

    snake_case_					:	List[Any]							=      fs_config.conv_bias
    snake_case_					:	str							=      eval(fs_config.conv_feature_layers       )
    snake_case_					:	int							=      [x[0] for x in conv_layers]
    snake_case_					:	str							=      [x[1] for x in conv_layers]
    snake_case_					:	Dict							=      [x[2] for x in conv_layers]
    snake_case_					:	Dict							=      '''gelu'''
    snake_case_					:	Union[str, Any]							=      '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    snake_case_					:	List[str]							=      0.0
    snake_case_					:	List[Any]							=      fs_config.activation_fn.name
    snake_case_					:	Optional[int]							=      fs_config.encoder_embed_dim
    snake_case_					:	Any							=      0.02
    snake_case_					:	Union[str, Any]							=      fs_config.encoder_ffn_embed_dim
    snake_case_					:	List[Any]							=      1E-5
    snake_case_					:	List[Any]							=      fs_config.encoder_layerdrop
    snake_case_					:	Union[str, Any]							=      fs_config.encoder_attention_heads
    snake_case_					:	Any							=      fs_config.conv_pos_groups
    snake_case_					:	Union[str, Any]							=      fs_config.conv_pos
    snake_case_					:	Union[str, Any]							=      len(__A       )
    snake_case_					:	Optional[int]							=      fs_config.encoder_layers
    snake_case_					:	Dict							=      fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        snake_case_					:	Optional[Any]							=      model.cfg
        snake_case_					:	Optional[int]							=      fs_config.final_dropout
        snake_case_					:	Optional[int]							=      fs_config.layerdrop
    snake_case_					:	Tuple							=      fs_config.activation_dropout
    snake_case_					:	Union[str, Any]							=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    snake_case_					:	List[Any]							=      fs_config.attention_dropout
    snake_case_					:	Any							=      fs_config.dropout_input
    snake_case_					:	Dict							=      fs_config.dropout
    snake_case_					:	Any							=      fs_config.mask_channel_length
    snake_case_					:	Any							=      fs_config.mask_channel_prob
    snake_case_					:	Tuple							=      fs_config.mask_length
    snake_case_					:	List[str]							=      fs_config.mask_prob

    snake_case_					:	str							=      '''Wav2Vec2FeatureExtractor'''
    snake_case_					:	Any							=      '''Wav2Vec2CTCTokenizer'''

    return config




@torch.no_grad()
def __lowercase     (				_a						,     _a						,     _a=None						,     _a=None						,     _a=True       ):

    if is_finetuned:
        snake_case_,      snake_case_,      snake_case_					:	Optional[Any]							=      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_					:	Dict							=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

    if config_path is not None:
        snake_case_					:	Optional[Any]							=      SEWConfig.from_pretrained(__A       )
    else:
        snake_case_					:	Tuple							=      convert_config(model[0]						,     __A       )
    snake_case_					:	Optional[int]							=      model[0].eval()

    snake_case_					:	List[Any]							=      True if config.feat_extract_norm == '''layer''' else False
    snake_case_					:	List[str]							=      WavaVecaFeatureExtractor(
        feature_size=1						,     sampling_rate=16_000						,     padding_value=0						,     do_normalize=__A						,     return_attention_mask=__A						,     )

    if is_finetuned:
        if dict_path:
            snake_case_					:	Tuple							=      Dictionary.load(__A       )

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

        snake_case_					:	Any							=      SEWForCTC(__A       )
    else:
        snake_case_					:	Tuple							=      SEWModel(__A       )
        feature_extractor.save_pretrained(__A       )

    recursively_load_weights(__A						,     __A						,     __A       )

    hf_model.save_pretrained(__A       )


if __name__ == "__main__":
     lowercase__       :    List[str]				       =      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'''
     )
     lowercase__       :    Dict				       =      parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )








from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


