






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()
lowerCamelCase			:					int         =	logging.get_logger(__name__)

lowerCamelCase			:					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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __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       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __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								=					0.02
     __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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case=None					, __snake_case=None					, __snake_case=True       ):

     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__":
 lowerCamelCase			:					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'''
 )
 lowerCamelCase			:					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
 )














def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     if not isinstance(__snake_case					, __snake_case       ):
          raise ValueError("iterations must be defined as integers"       )
     if not isinstance(__snake_case					, __snake_case       ) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"       )
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"       )

     __lowerCAmelCase								=					""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(__snake_case       )

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()






