code: 
  
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
  from ....modeling_utils import ModuleUtilsMixin
  from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				List[Any]       =					"MMBTConfig"
  
  class 						A__      (					nn.Module			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           super().__init__()
           snake_case					=       config
           snake_case					=       encoder
           snake_case					=       nn.Linear(config.modal_hidden_size    ,      config.hidden_size					)
           snake_case					=       embeddings.position_embeddings
           snake_case					=       embeddings.token_type_embeddings
           snake_case					=       embeddings.word_embeddings
           snake_case					=       embeddings.LayerNorm
           snake_case					=       nn.Dropout(p=config.hidden_dropout_prob					)
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None					):
           snake_case					=       self.proj_embeddings(self.encoder(__snake_case					)					)
           snake_case					=       token_embeddings.size(1					)
  
           if start_token is not None:
                snake_case					=       self.word_embeddings(__snake_case					)
                seq_length += 1
                snake_case					=       torch.cat([start_token_embeds.unsqueeze(1					), token_embeddings]    ,      dim=1					)
  
           if end_token is not None:
                snake_case					=       self.word_embeddings(__snake_case					)
                seq_length += 1
                snake_case					=       torch.cat([token_embeddings, end_token_embeds.unsqueeze(1					)]    ,      dim=1					)
  
           if position_ids is None:
                snake_case					=       torch.arange(__snake_case    ,      dtype=torch.long    ,      device=input_modal.device					)
                snake_case					=       position_ids.unsqueeze(0					).expand(input_modal.size(0					)    ,      __snake_case					)
  
           if token_type_ids is None:
                snake_case					=       torch.zeros(
                    (input_modal.size(0					), seq_length)    ,      dtype=torch.long    ,      device=input_modal.device					)
  
           snake_case					=       self.position_embeddings(__snake_case					)
           snake_case					=       self.token_type_embeddings(__snake_case					)
           snake_case					=       token_embeddings + position_embeddings + token_type_embeddings
           snake_case					=       self.LayerNorm(__snake_case					)
           snake_case					=       self.dropout(__snake_case					)
           return embeddings
  
  
  
  
  
  
  _SCREAMING_SNAKE_CASE    :				Tuple       =					R"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					R"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
  
  @add_start_docstrings(
      'The bare MMBT Model outputting raw hidden-states without any specific head on top.' ,      snake_case__ ,      )
  class 						A__      (					nn.Module ,      snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           super().__init__()
           snake_case					=       config
           snake_case					=       transformer
           snake_case					=       ModalEmbeddings(__snake_case    ,      __snake_case    ,      transformer.embeddings					)
      @add_start_docstrings_to_model_forward(__snake_case					)
      @replace_return_docstrings(output_type=__snake_case    ,      config_class=_CONFIG_FOR_DOC					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      ):
           snake_case					=       output_attentions if output_attentions is not None else self.config.output_attentions
           snake_case					=       (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           snake_case					=       return_dict if return_dict is not None else self.config.use_return_dict
  
           if input_ids is not None and inputs_embeds is not None:
                raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''					)
           elif input_ids is not None:
                snake_case					=       input_ids.size()
           elif inputs_embeds is not None:
                snake_case					=       inputs_embeds.size()[:-1]
           else:
                raise ValueError('''You have to specify either input_ids or inputs_embeds'''					)
  
           snake_case					=       input_ids.device if input_ids is not None else inputs_embeds.device
  
           snake_case					=       self.modal_encoder(
               __snake_case    ,      start_token=__snake_case    ,      end_token=__snake_case    ,      position_ids=__snake_case    ,      token_type_ids=__snake_case    ,      )
  
           snake_case					=       modal_embeddings.size()[:-1]
  
           if token_type_ids is None:
                snake_case					=       torch.ones(__snake_case    ,      dtype=torch.long    ,      device=__snake_case					)
  
           snake_case					=       self.transformer.embeddings(
               input_ids=__snake_case    ,      position_ids=__snake_case    ,      token_type_ids=__snake_case    ,      inputs_embeds=__snake_case					)
  
           snake_case					=       torch.cat([modal_embeddings, txt_embeddings]    ,      1					)
  
           snake_case					=       embedding_output.size()[:-1]
  
           if attention_mask is None:
                snake_case					=       torch.ones(__snake_case    ,      device=__snake_case					)
           else:
                snake_case					=       torch.cat(
                    [torch.ones(__snake_case    ,      device=__snake_case    ,      dtype=torch.long					), attention_mask]    ,      dim=1					)
           if encoder_attention_mask is None:
                snake_case					=       torch.ones(__snake_case    ,      device=__snake_case					)
           else:
                snake_case					=       torch.cat(
                    [torch.ones(__snake_case    ,      device=__snake_case					), encoder_attention_mask]    ,      dim=1					)
  
           snake_case					=       self.get_extended_attention_mask(__snake_case    ,      __snake_case					)
           snake_case					=       self.invert_attention_mask(__snake_case					)
           snake_case					=       self.get_head_mask(__snake_case    ,      self.config.num_hidden_layers					)
  
           snake_case					=       self.transformer.encoder(
               __snake_case    ,      attention_mask=__snake_case    ,      head_mask=__snake_case    ,      encoder_hidden_states=__snake_case    ,      encoder_attention_mask=__snake_case    ,      output_attentions=__snake_case    ,      output_hidden_states=__snake_case    ,      return_dict=__snake_case    ,      )
  
           snake_case					=       encoder_outputs[0]
           snake_case					=       self.transformer.pooler(__snake_case					)
  
           if not return_dict:
                return (sequence_output, pooled_output) + encoder_outputs[1:]
  
           return BaseModelOutputWithPooling(
               last_hidden_state=__snake_case    ,      pooler_output=__snake_case    ,      hidden_states=encoder_outputs.hidden_states    ,      attentions=encoder_outputs.attentions    ,      )
      def 	a_    ( self					):
           return self.embeddings.word_embeddings
  
  
  
  
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       value
  
  
  
  
  
  
  @add_start_docstrings(
      '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    ' ,      snake_case__ ,      snake_case__ ,      )
  class 						A__      (					nn.Module			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           super().__init__()
           snake_case					=       config.num_labels
  
           snake_case					=       MMBTModel(__snake_case    ,      __snake_case    ,      __snake_case					)
           snake_case					=       nn.Dropout(config.hidden_dropout_prob					)
           snake_case					=       nn.Linear(config.hidden_size    ,      config.num_labels					)
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      ):
           snake_case					=       return_dict if return_dict is not None else self.config.use_return_dict
  
           snake_case					=       self.mmbt(
               input_modal=__snake_case    ,      input_ids=__snake_case    ,      modal_start_tokens=__snake_case    ,      modal_end_tokens=__snake_case    ,      attention_mask=__snake_case    ,      token_type_ids=__snake_case    ,      modal_token_type_ids=__snake_case    ,      position_ids=__snake_case    ,      modal_position_ids=__snake_case    ,      head_mask=__snake_case    ,      inputs_embeds=__snake_case    ,      return_dict=__snake_case    ,      )
  
           snake_case					=       outputs[1]
  
           snake_case					=       self.dropout(__snake_case					)
           snake_case					=       self.classifier(__snake_case					)
  
           snake_case					=       None
           if labels is not None:
                if self.num_labels == 1:
                     #  We are doing regression
                     snake_case					=       MSELoss()
                     snake_case					=       loss_fct(logits.view(-1					)    ,      labels.view(-1					)					)
                else:
                     snake_case					=       CrossEntropyLoss()
                     snake_case					=       loss_fct(logits.view(-1    ,      self.num_labels					)    ,      labels.view(-1					)					)
  
           if not return_dict:
                snake_case					=       (logits,) + outputs[2:]
                return ((loss,) + output) if loss is not None else output
  
           return SequenceClassifierOutput(
               loss=__snake_case    ,      logits=__snake_case    ,      hidden_states=outputs.hidden_states    ,      attentions=outputs.attentions    ,      )
  
  
  
  
  
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
   )
  
  
  
  
  