code: 
  
  
  import colorsys
  import io
  
  import cva
  import matplotlib as mpl
  import matplotlib.colors as mplc
  import matplotlib.figure as mplfigure
  import numpy as np
  import torch
  from matplotlib.backends.backend_agg import FigureCanvasAgg
  
  from utils import img_tensorize
  
  
  _SCREAMING_SNAKE_CASE    :				Tuple       =					10_00
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case=1.2    ,      __snake_case="g"    ,      __snake_case=0.5    ,      __snake_case="-"    ,      __snake_case="test_out.jpg"    ,      __snake_case=True    ,      __snake_case=False    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=0.7    ,      ):
           if isinstance(__snake_case    ,      torch.Tensor					):
                snake_case					=       img.numpy().astype('''np.uint8'''					)
           if isinstance(__snake_case    ,      __snake_case					):
                snake_case					=       img_tensorize(__snake_case					)
           assert isinstance(__snake_case    ,      np.ndarray					)
  
           snake_case				,						snake_case					=       img.shape[1], img.shape[0]
           snake_case					=       mplfigure.Figure(frameon=__snake_case					)
           snake_case					=       fig.get_dpi()
           snake_case					=       (width * scale + 1E-2) / dpi
           snake_case					=       (height * scale + 1E-2) / dpi
           fig.set_size_inches(__snake_case    ,      __snake_case					)
           snake_case					=       fig.add_axes([0.0, 0.0, 1.0, 1.0]					)
           ax.axis('''off'''					)
           ax.set_xlim(0.0    ,      __snake_case					)
           ax.set_ylim(__snake_case					)
  
           snake_case					=       saveas
           snake_case					=       rgb
           snake_case					=       pynb
           snake_case					=       img
           snake_case					=       edgecolor
           snake_case					=       0.5
           snake_case					=       linestyle
           snake_case					=       int(np.sqrt(min(__snake_case    ,      __snake_case					)					) * scale // 3					)
           snake_case					=       width
           snake_case					=       height
           snake_case					=       scale
           snake_case					=       fig
           snake_case					=       ax
           snake_case					=       pad
           snake_case					=       idaobj
           snake_case					=       idaattr
           snake_case					=       FigureCanvasAgg(__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None					):
           if color is None:
                snake_case					=       self.edgecolor
           ((snake_case)				,						(snake_case)				,						(snake_case)				,						(snake_case))					=       box
           snake_case					=       xa - xa
           snake_case					=       ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya)    ,      __snake_case    ,      __snake_case    ,      fill=__snake_case    ,      edgecolor=__snake_case    ,      linewidth=self.font_size // 3    ,      alpha=self.alpha    ,      linestyle=self.linestyle    ,      )					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None					):
           if len(boxes.shape					) > 2:
                snake_case					=       boxes[0]
           if len(obj_ids.shape					) > 1:
                snake_case					=       obj_ids[0]
           if len(obj_scores.shape					) > 1:
                snake_case					=       obj_scores[0]
           if len(attr_ids.shape					) > 1:
                snake_case					=       attr_ids[0]
           if len(attr_scores.shape					) > 1:
                snake_case					=       attr_scores[0]
           if isinstance(__snake_case    ,      torch.Tensor					):
                snake_case					=       boxes.numpy()
           if isinstance(__snake_case    ,      __snake_case					):
                snake_case					=       np.array(__snake_case					)
           assert isinstance(__snake_case    ,      np.ndarray					)
           snake_case					=       np.prod(boxes[:, 2:] - boxes[:, :2]    ,      axis=1					)
           snake_case					=       np.argsort(-areas					).tolist()
           snake_case					=       boxes[sorted_idxs] if boxes is not None else None
           snake_case					=       obj_ids[sorted_idxs] if obj_ids is not None else None
           snake_case					=       obj_scores[sorted_idxs] if obj_scores is not None else None
           snake_case					=       attr_ids[sorted_idxs] if attr_ids is not None else None
           snake_case					=       attr_scores[sorted_idxs] if attr_scores is not None else None
  
           snake_case					=       [self._random_color(maximum=1					) for _ in range(len(__snake_case					)					)]
           snake_case					=       [assigned_colors[idx] for idx in sorted_idxs]
           if obj_ids is not None:
                snake_case					=       self._create_text_labels_attr(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
                for i in range(len(__snake_case					)					):
                     snake_case					=       assigned_colors[i]
                     self.add_box(boxes[i]    ,      __snake_case					)
                     self.draw_labels(labels[i]    ,      boxes[i]    ,      __snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case				,						snake_case				,						snake_case				,						snake_case					=       box
           snake_case					=       (xa, ya)
           snake_case					=       (ya - ya) * (xa - xa)
           snake_case					=       _SMALL_OBJ * self.scale
           if instance_area < small or ya - ya < 4_0 * self.scale:
                if ya >= self.height - 5:
                     snake_case					=       (xa, ya)
                else:
                     snake_case					=       (xa, ya)
  
           snake_case					=       (ya - ya) / np.sqrt(self.height * self.width					)
           snake_case					=       self._change_color_brightness(__snake_case    ,      brightness_factor=0.7					)
           snake_case					=       np.clip((height_ratio - 0.02) / 0.08 + 1    ,      1.2    ,      2					)
           font_size *= 0.75 * self.font_size
  
           self.draw_text(
               text=__snake_case    ,      position=__snake_case    ,      color=__snake_case    ,      )
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case="g"    ,      __snake_case="left"    ,      ):
           snake_case					=       0
           snake_case					=       self.font_size
           snake_case					=       np.maximum(list(mplc.to_rgb(__snake_case					)					)    ,      0.2					)
           snake_case					=       max(0.8    ,      np.max(__snake_case					)					)
           snake_case					=       {
               '''facecolor''': '''black''',
               '''alpha''': self.alpha,
               '''pad''': self.pad,
               '''edgecolor''': '''none''',
           }
           snake_case				,						snake_case					=       position
           self.ax.text(
               __snake_case    ,      __snake_case    ,      __snake_case    ,      size=font_size * self.scale    ,      family='''sans-serif'''    ,      bbox=__snake_case    ,      verticalalignment='''top'''    ,      horizontalalignment=__snake_case    ,      color=__snake_case    ,      zorder=1_0    ,      rotation=__snake_case    ,      )
      def 	a_    ( self    ,      __snake_case=None					):
           if saveas is None:
                snake_case					=       self.saveas
           if saveas.lower().endswith('''.jpg'''					) or saveas.lower().endswith('''.png'''					):
                cva.imwrite(
                    __snake_case    ,      self._get_buffer()[:, :, ::-1]    ,      )
           else:
                self.fig.savefig(__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       [self.idaobj[i] for i in classes]
           snake_case					=       [self.idaattr[i] for i in attr_classes]
           snake_case					=       [
               F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
               for label, score, attr, attr_score in zip(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
           ]
           return labels
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           snake_case					=       [self.idaobj[i] for i in classes]
           if scores is not None:
                if labels is None:
                     snake_case					=       ['''{:.0f}%'''.format(s * 1_0_0					) for s in scores]
                else:
                     snake_case					=       ['''{} {:.0f}%'''.format(__snake_case    ,      s * 1_0_0					) for li, s in zip(__snake_case    ,      __snake_case					)]
           return labels
      def 	a_    ( self    ,      __snake_case=2_5_5					):
           snake_case					=       np.random.randint(0    ,      len(_COLORS					)					)
           snake_case					=       _COLORS[idx] * maximum
           if not self.rgb:
                snake_case					=       ret[::-1]
           return ret
      def 	a_    ( self					):
           if not self.pynb:
                snake_case				,						snake_case					=       self.canvas.print_to_buffer()
                if (width, height) != (self.width, self.height):
                     snake_case					=       cva.resize(self.img    ,      (width, height)					)
                else:
                     snake_case					=       self.img
           else:
                snake_case					=       io.BytesIO()  # works for cairo backend
                self.canvas.print_rgba(__snake_case					)
                snake_case				,						snake_case					=       self.width, self.height
                snake_case					=       buf.getvalue()
                snake_case					=       self.img
  
           snake_case					=       np.frombuffer(__snake_case    ,      dtype='''uint8'''					)
           snake_case					=       buffer.reshape(__snake_case    ,      __snake_case    ,      4					)
           snake_case				,						snake_case					=       np.split(__snake_case    ,      [3]    ,      axis=2					)
  
           try:
                import numexpr as ne  # fuse them with numexpr
  
                snake_case					=       ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''					)
           except ImportError:
                snake_case					=       alpha.astype('''float32'''					) / 255.0
                snake_case					=       img * (1 - alpha) + rgb * alpha
  
           return visualized_image.astype('''uint8'''					)
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           assert brightness_factor >= -1.0 and brightness_factor <= 1.0
           snake_case					=       mplc.to_rgb(__snake_case					)
           snake_case					=       colorsys.rgb_to_hls(*mplc.to_rgb(__snake_case					)					)
           snake_case					=       polygon_color[1] + (brightness_factor * polygon_color[1])
           snake_case					=       0.0 if modified_lightness < 0.0 else modified_lightness
           snake_case					=       1.0 if modified_lightness > 1.0 else modified_lightness
           snake_case					=       colorsys.hls_to_rgb(polygon_color[0]    ,      __snake_case    ,      polygon_color[2]					)
           return modified_color
  
  
  
  
  
  
  _SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					(
      np.array(
          [
              0.000,
              0.447,
              0.741,
              0.850,
              0.325,
              0.098,
              0.929,
              0.694,
              0.125,
              0.494,
              0.184,
              0.556,
              0.466,
              0.674,
              0.188,
              0.301,
              0.745,
              0.933,
              0.635,
              0.078,
              0.184,
              0.300,
              0.300,
              0.300,
              0.600,
              0.600,
              0.600,
              1.000,
              0.000,
              0.000,
              1.000,
              0.500,
              0.000,
              0.749,
              0.749,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              1.000,
              0.667,
              0.000,
              1.000,
              0.333,
              0.333,
              0.000,
              0.333,
              0.667,
              0.000,
              0.333,
              1.000,
              0.000,
              0.667,
              0.333,
              0.000,
              0.667,
              0.667,
              0.000,
              0.667,
              1.000,
              0.000,
              1.000,
              0.333,
              0.000,
              1.000,
              0.667,
              0.000,
              1.000,
              1.000,
              0.000,
              0.000,
              0.333,
              0.500,
              0.000,
              0.667,
              0.500,
              0.000,
              1.000,
              0.500,
              0.333,
              0.000,
              0.500,
              0.333,
              0.333,
              0.500,
              0.333,
              0.667,
              0.500,
              0.333,
              1.000,
              0.500,
              0.667,
              0.000,
              0.500,
              0.667,
              0.333,
              0.500,
              0.667,
              0.667,
              0.500,
              0.667,
              1.000,
              0.500,
              1.000,
              0.000,
              0.500,
              1.000,
              0.333,
              0.500,
              1.000,
              0.667,
              0.500,
              1.000,
              1.000,
              0.500,
              0.000,
              0.333,
              1.000,
              0.000,
              0.667,
              1.000,
              0.000,
              1.000,
              1.000,
              0.333,
              0.000,
              1.000,
              0.333,
              0.333,
              1.000,
              0.333,
              0.667,
              1.000,
              0.333,
              1.000,
              1.000,
              0.667,
              0.000,
              1.000,
              0.667,
              0.333,
              1.000,
              0.667,
              0.667,
              1.000,
              0.667,
              1.000,
              1.000,
              1.000,
              0.000,
              1.000,
              1.000,
              0.333,
              1.000,
              1.000,
              0.667,
              1.000,
              0.333,
              0.000,
              0.000,
              0.500,
              0.000,
              0.000,
              0.667,
              0.000,
              0.000,
              0.833,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              0.167,
              0.000,
              0.000,
              0.333,
              0.000,
              0.000,
              0.500,
              0.000,
              0.000,
              0.667,
              0.000,
              0.000,
              0.833,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              0.167,
              0.000,
              0.000,
              0.333,
              0.000,
              0.000,
              0.500,
              0.000,
              0.000,
              0.667,
              0.000,
              0.000,
              0.833,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              0.143,
              0.143,
              0.143,
              0.857,
              0.857,
              0.857,
              1.000,
              1.000,
              1.000,
          ]
      )
      .astype(np.floataa)
      .reshape(-1, 3)
  )
  
  
  
  
  
style_context: 
  
  
  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()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for attribute in key.split('''.'''       ):
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )
  
       if weight_type is not None:
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       ).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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       fairseq_model.state_dict()
  
       snake_case					=       hf_model.sew_d.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(
                     UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,hf_model.config.feat_extract_norm == '''group'''							,)
                 snake_case					=       True
            else:
                 for key, mapped_key in MAPPING.items():
                      snake_case					=       '''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]:
                           snake_case					=       True
                           if "*" in mapped_key:
                                snake_case					=       name.split(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                snake_case					=       mapped_key.replace('''*'''							,UpperCamelCase_       )
                           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(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
                      continue
            if not is_used:
                 unused_weights.append(UpperCamelCase_       )
  
       logger.warning(F'''Unused weights: {unused_weights}'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       SEWDConfig()
       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					=       0.02
       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(UpperCamelCase_       )
       snake_case					=       fs_config.encoder_layers
       snake_case					=       fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       snake_case					=       fs_config.max_position_embeddings
       snake_case					=       fs_config.position_buckets
       snake_case					=       fs_config.share_att_key
       snake_case					=       fs_config.relative_attention
       snake_case					=       fs_config.position_biased_input
       snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
       snake_case					=       fs_config.norm_rel_ebd
  
       # 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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """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					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       snake_case					=       model[0].eval()
  
       snake_case					=       True if config.feat_extract_norm == '''layer''' else False
       snake_case					=       WavaVecaFeatureExtractor(
           feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # 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(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,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=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[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    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  