





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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase     :			int     						=	{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """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      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       for attribute in key.split('''.'''       ):
              _UpperCamelCase														=    getattr(lowerCAmelCase     ,							lowerCAmelCase       )

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

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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    []
       _UpperCamelCase														=    fairseq_model.state_dict()

       _UpperCamelCase														=    hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _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:
                     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.'''
                     )
                     _UpperCamelCase														=    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.'''
                     )
                     _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:
                     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."
                     )
                     _UpperCamelCase														=    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.'''
                     )
                     _UpperCamelCase														=    value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
       else:
              unused_weights.append(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    SEWDConfig()
       if is_finetuned:
              _UpperCamelCase														=    model.wav_encoder.wav_model.cfg
       else:
              _UpperCamelCase														=    model.cfg

       _UpperCamelCase														=    fs_config.conv_bias
       _UpperCamelCase														=    eval(fs_config.conv_feature_layers       )
       _UpperCamelCase														=    [x[0] for x in conv_layers]
       _UpperCamelCase														=    [x[1] for x in conv_layers]
       _UpperCamelCase														=    [x[2] for x in conv_layers]
       _UpperCamelCase														=    '''gelu'''
       _UpperCamelCase														=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       _UpperCamelCase														=    0.0
       _UpperCamelCase														=    fs_config.activation_fn.name
       _UpperCamelCase														=    fs_config.encoder_embed_dim
       _UpperCamelCase														=    0.0_2
       _UpperCamelCase														=    fs_config.encoder_ffn_embed_dim
       _UpperCamelCase														=    1E-5
       _UpperCamelCase														=    fs_config.encoder_layerdrop
       _UpperCamelCase														=    fs_config.encoder_attention_heads
       _UpperCamelCase														=    fs_config.conv_pos_groups
       _UpperCamelCase														=    fs_config.conv_pos
       _UpperCamelCase														=    len(lowerCAmelCase       )
       _UpperCamelCase														=    fs_config.encoder_layers
       _UpperCamelCase														=    fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       _UpperCamelCase														=    fs_config.max_position_embeddings
       _UpperCamelCase														=    fs_config.position_buckets
       _UpperCamelCase														=    fs_config.share_att_key
       _UpperCamelCase														=    fs_config.relative_attention
       _UpperCamelCase														=    fs_config.position_biased_input
       _UpperCamelCase														=    tuple(fs_config.pos_att_type.split('''|'''       )       )
       _UpperCamelCase														=    fs_config.norm_rel_ebd

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

       _UpperCamelCase														=    '''Wav2Vec2FeatureExtractor'''
       _UpperCamelCase														=    '''Wav2Vec2CTCTokenizer'''

       return config


@torch.no_grad()
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=None     ,							lowerCAmelCase=None     ,							lowerCAmelCase=True       ):

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

       if config_path is not None:
              _UpperCamelCase														=    SEWDConfig.from_pretrained(lowerCAmelCase       )
       else:
              _UpperCamelCase														=    convert_config(model[0]     ,							lowerCAmelCase       )
       _UpperCamelCase														=    model[0].eval()

       _UpperCamelCase														=    True if config.feat_extract_norm == '''layer''' else False
       _UpperCamelCase														=    WavaVecaFeatureExtractor(
           feature_size=1     ,							sampling_rate=16_000     ,							padding_value=0     ,							do_normalize=lowerCAmelCase     ,							return_attention_mask=lowerCAmelCase     ,							)

       if is_finetuned:
              if dict_path:
                     _UpperCamelCase														=    Dictionary.load(lowerCAmelCase       )

                     # 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.pad_index
                     _UpperCamelCase														=    target_dict.bos_index
                     _UpperCamelCase														=    target_dict.eos_index
                     _UpperCamelCase														=    len(target_dict.symbols       )
                     _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							'''vocab.json'''       )
                     if not os.path.isdir(lowerCAmelCase       ):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase       )       )
                            return
                     os.makedirs(lowerCAmelCase     ,							exist_ok=lowerCAmelCase       )
                     with open(lowerCAmelCase     ,							'''w'''     ,							encoding='''utf-8'''       ) as vocab_handle:
                            json.dump(target_dict.indices     ,							lowerCAmelCase       )
                     _UpperCamelCase														=    WavaVecaCTCTokenizer(
                         lowerCAmelCase     ,							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=lowerCAmelCase     ,							)
                     _UpperCamelCase														=    WavaVecaProcessor(feature_extractor=lowerCAmelCase     ,							tokenizer=lowerCAmelCase       )
                     processor.save_pretrained(lowerCAmelCase       )

              _UpperCamelCase														=    SEWDForCTC(lowerCAmelCase       )
       else:
              _UpperCamelCase														=    SEWDModel(lowerCAmelCase       )
              feature_extractor.save_pretrained(lowerCAmelCase       )

       recursively_load_weights(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )

       hf_model.save_pretrained(lowerCAmelCase       )


if __name__ == "__main__":
       lowercase     :			Dict     						=	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     :			Union[str, Any]     						=	parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )













import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    BeautifulSoup(requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       ).text     ,							'''html.parser'''       )
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:title'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:image'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''p'''     ,							id='''description'''       ).get_text()
       _UpperCamelCase							,		_UpperCamelCase														=    os.path.splitext(os.path.basename(lowerCAmelCase       )       )
       _UpperCamelCase														=    title.strip().replace(''' '''     ,							'''_'''       )
       _UpperCamelCase														=    f'''{image_title}{image_extension}'''
       save_image(lowerCAmelCase     ,							lowerCAmelCase       )
       return (title, description, image_title)


if __name__ == "__main__":
       lowercase						,lowercase						,lowercase     :			str     						=	random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')






