

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

_SCREAMING_SNAKE_CASE  :						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        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     for attribute in key.split('''.'''       ):
          SCREAMING_SNAKE_CASE__					=					getattr(_A   ,	_A       )

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

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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     SCREAMING_SNAKE_CASE__					=					fairseq_model.state_dict()

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

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE__					=					False
          if "conv_layers" in name:
               load_conv_layer(
                   _A   ,	_A   ,	_A   ,	_A   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
               SCREAMING_SNAKE_CASE__					=					True
          else:
               for key, mapped_key in MAPPING.items():
                    SCREAMING_SNAKE_CASE__					=					'''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]:
                         SCREAMING_SNAKE_CASE__					=					True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE__					=					name.split(_A       )[0].split('''.'''       )[-2]
                              SCREAMING_SNAKE_CASE__					=					mapped_key.replace('''*'''   ,	_A       )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE__					=					'''weight_g'''
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE__					=					'''weight_v'''
                         elif "weight" in name:
                              SCREAMING_SNAKE_CASE__					=					'''weight'''
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE__					=					'''bias'''
                         else:
                              SCREAMING_SNAKE_CASE__					=					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        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					SEWConfig()
     if is_finetuned:
          SCREAMING_SNAKE_CASE__					=					model.wav_encoder.wav_model.cfg
     else:
          SCREAMING_SNAKE_CASE__					=					model.cfg

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

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

     SCREAMING_SNAKE_CASE__					=					'''Wav2Vec2FeatureExtractor'''
     SCREAMING_SNAKE_CASE__					=					'''Wav2Vec2CTCTokenizer'''

     return config
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A=None   ,	_A=None   ,	_A=True       ):





     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE__					=					True if config.feat_extract_norm == '''layer''' else False
     SCREAMING_SNAKE_CASE__					=					WavaVecaFeatureExtractor(
         feature_size=1   ,	sampling_rate=1_60_00   ,	padding_value=0   ,	do_normalize=_A   ,	return_attention_mask=_A   ,	)

     if is_finetuned:
          if dict_path:
               SCREAMING_SNAKE_CASE__					=					Dictionary.load(_A       )

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

          SCREAMING_SNAKE_CASE__					=					SEWForCTC(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					SEWModel(_A       )
          feature_extractor.save_pretrained(_A       )

     recursively_load_weights(_A   ,	_A   ,	_A       )

     hf_model.save_pretrained(_A       )


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





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


