


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						:Optional[int]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:List[str]			=  {
    '''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    (				lowerCAmelCase_				:Any		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Any				)->Union[str, Any]:
   '''simple docstring'''






   for attribute in key.split("."				):
      snake_case_            =       getattr(lowerCAmelCase_		,    lowerCAmelCase_				)

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

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Optional[int]				)->Union[str, Any]:
   '''simple docstring'''






   snake_case_            =       []
   snake_case_            =       fairseq_model.state_dict()

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

   for name, value in fairseq_dict.items():
      snake_case_            =       False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    hf_model.config.feat_extract_norm == "group"		,    )
         snake_case_            =       True
      else:
         for key, mapped_key in MAPPING.items():
            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]:
               snake_case_            =       True
               if "*" in mapped_key:
                  snake_case_            =       name.split(lowerCAmelCase_				)[0].split("."				)[-2]
                  snake_case_            =       mapped_key.replace("*"		,    lowerCAmelCase_				)
               if "weight_g" in name:
                  snake_case_            =       "weight_g"
               elif "weight_v" in name:
                  snake_case_            =       "weight_v"
               elif "weight" in name:
                  snake_case_            =       "weight"
               elif "bias" in name:
                  snake_case_            =       "bias"
               else:
                  snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:str				)->Optional[Any]:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Tuple				)->Any:
   '''simple docstring'''






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

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

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

   snake_case_            =       "Wav2Vec2FeatureExtractor"
   snake_case_            =       "Wav2Vec2CTCTokenizer"

   return config



@torch.no_grad()
def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Tuple=None		,    lowerCAmelCase_				:Optional[Any]=None		,    lowerCAmelCase_				:int=True				)->Tuple:
   '''simple docstring'''






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

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

   snake_case_            =       True if config.feat_extract_norm == "layer" else False
   snake_case_            =       WavaVecaFeatureExtractor(
       feature_size=1		,    sampling_rate=16_000		,    padding_value=0		,    do_normalize=lowerCAmelCase_		,    return_attention_mask=lowerCAmelCase_		,    )

   if is_finetuned:
      if dict_path:
         snake_case_            =       Dictionary.load(lowerCAmelCase_				)

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

      snake_case_            =       SEWForCTC(lowerCAmelCase_				)
   else:
      snake_case_            =       SEWModel(lowerCAmelCase_				)
      feature_extractor.save_pretrained(lowerCAmelCase_				)

   recursively_load_weights(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

   hf_model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						: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'''
   )
   SCREAMING_SNAKE_CASE						: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
   )








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE						:List[Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							['pixel_values']



  def __init__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							PILImageResampling = PIL.Image.BICUBIC     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							Union[int, float] = 1 / 2_5_5     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					**_lowerCAmelCase							:							Optional[Any]     ,					) ->			None:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)
     snake_case_            =       size if size is not None else {"height": 2_5_6, "width": 2_5_6}
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     snake_case_            =       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
     snake_case_            =       get_size_dict(_lowerCAmelCase     ,					param_name="crop_size"	)

     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       resample
     snake_case_            =       do_center_crop
     snake_case_            =       crop_size
     snake_case_            =       do_rescale
     snake_case_            =       rescale_factor
     snake_case_            =       do_normalize
     snake_case_            =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     snake_case_            =       image_std if image_std is not None else IMAGENET_STANDARD_STD



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Dict[str, int]     ,					_lowerCAmelCase							:							PILImageResampling = PIL.Image.BICUBIC     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							int     ,					) ->			np.ndarray:




     """simple docstring"""
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     if "height" not in size or "width" not in size:
        raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
     return resize(
         _lowerCAmelCase     ,					size=(size["height"], size["width"])     ,					resample=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Dict[str, int]     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							int     ,					) ->			np.ndarray:




     """simple docstring"""
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     if "height" not in size or "width" not in size:
        raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
     return center_crop(_lowerCAmelCase     ,					size=(size["height"], size["width"])     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Union[int, float]     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							Union[str, Any]     ,					) ->			Optional[Any]:




     """simple docstring"""
     return rescale(_lowerCAmelCase     ,					scale=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Union[float, List[float]]     ,					_lowerCAmelCase							:							Union[float, List[float]]     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							Any     ,					) ->			np.ndarray:




     """simple docstring"""
     return normalize(_lowerCAmelCase     ,					mean=_lowerCAmelCase     ,					std=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							ImageInput     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							int=None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							float = None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[str, TensorType]] = None     ,					_lowerCAmelCase							:							ChannelDimension = ChannelDimension.FIRST     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			PIL.Image.Image:




     """simple docstring"""
     snake_case_            =       do_resize if do_resize is not None else self.do_resize
     snake_case_            =       resample if resample is not None else self.resample
     snake_case_            =       do_center_crop if do_center_crop is not None else self.do_center_crop
     snake_case_            =       do_rescale if do_rescale is not None else self.do_rescale
     snake_case_            =       rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_            =       do_normalize if do_normalize is not None else self.do_normalize
     snake_case_            =       image_mean if image_mean is not None else self.image_mean
     snake_case_            =       image_std if image_std is not None else self.image_std

     snake_case_            =       size if size is not None else self.size
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     snake_case_            =       crop_size if crop_size is not None else self.crop_size
     snake_case_            =       get_size_dict(_lowerCAmelCase     ,					param_name="crop_size"	)

     snake_case_            =       make_list_of_images(_lowerCAmelCase	)

     if not valid_images(_lowerCAmelCase	):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray."	)

     if do_resize and size is None or resample is None:
        raise ValueError("Size and resample must be specified if do_resize is True."	)

     if do_center_crop and crop_size is None:
        raise ValueError("Crop size must be specified if do_center_crop is True."	)

     if do_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True."	)

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True."	)

     # All transformations expect numpy arrays.
     snake_case_            =       [to_numpy_array(_lowerCAmelCase	) for image in images]

     if do_resize:
        snake_case_            =       [self.resize(image=_lowerCAmelCase     ,					size=_lowerCAmelCase     ,					resample=_lowerCAmelCase	) for image in images]

     if do_center_crop:
        snake_case_            =       [self.center_crop(image=_lowerCAmelCase     ,					size=_lowerCAmelCase	) for image in images]

     if do_rescale:
        snake_case_            =       [self.rescale(image=_lowerCAmelCase     ,					scale=_lowerCAmelCase	) for image in images]

     if do_normalize:
        snake_case_            =       [self.normalize(image=_lowerCAmelCase     ,					mean=_lowerCAmelCase     ,					std=_lowerCAmelCase	) for image in images]

     snake_case_            =       [to_channel_dimension_format(_lowerCAmelCase     ,					_lowerCAmelCase	) for image in images]

     snake_case_            =       {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase     ,					tensor_type=_lowerCAmelCase	)




