



"""simple docstring"""





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       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """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        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):





      for attribute in key.split("."				):
            UpperCAmelCase_      			=							getattr(__lowerCAmelCase     ,      __lowerCAmelCase				)

      if weight_type is not None:
            UpperCAmelCase_      			=							getattr(__lowerCAmelCase     ,      __lowerCAmelCase				).shape
      else:
            UpperCAmelCase_      			=							hf_pointer.shape

      assert hf_shape == value.shape, (
          f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
          f""" {value.shape} for {full_name}"""
      )

      if weight_type == "weight":
            UpperCAmelCase_      			=							value
      elif weight_type == "weight_g":
            UpperCAmelCase_      			=							value
      elif weight_type == "weight_v":
            UpperCAmelCase_      			=							value
      elif weight_type == "bias":
            UpperCAmelCase_      			=							value
      else:
            UpperCAmelCase_      			=							value

      logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							fairseq_model.state_dict()

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

      for name, value in fairseq_dict.items():
            UpperCAmelCase_      			=							False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      hf_model.config.feat_extract_norm == "group"     ,      )
                  UpperCAmelCase_      			=							True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase_      			=							'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                        if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
                              UpperCAmelCase_      			=							True
                              if "*" in mapped_key:
                                    UpperCAmelCase_      			=							name.split(__lowerCAmelCase				)[0].split("."				)[-2]
                                    UpperCAmelCase_      			=							mapped_key.replace("*"     ,      __lowerCAmelCase				)
                              if "weight_g" in name:
                                    UpperCAmelCase_      			=							'''weight_g'''
                              elif "weight_v" in name:
                                    UpperCAmelCase_      			=							'''weight_v'''
                              elif "weight" in name:
                                    UpperCAmelCase_      			=							'''weight'''
                              elif "bias" in name:
                                    UpperCAmelCase_      			=							'''bias'''
                              else:
                                    UpperCAmelCase_      			=							None
                              set_recursively(__lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase				)
                        continue
            if not is_used:
                  unused_weights.append(__lowerCAmelCase				)

      logger.warning(f"""Unused weights: {unused_weights}"""				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							full_name.split("conv_layers."				)[-1]
      UpperCAmelCase_      			=							name.split("."				)
      UpperCAmelCase_      			=							int(items[0]				)
      UpperCAmelCase_      			=							int(items[1]				)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                  )
                  UpperCAmelCase_      			=							value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""				)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                  )
                  UpperCAmelCase_      			=							value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""				)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                      " found."
                  )
                  UpperCAmelCase_      			=							value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                  )
                  UpperCAmelCase_      			=							value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
      else:
            unused_weights.append(__lowerCAmelCase				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							SEWConfig()
      if is_finetuned:
            UpperCAmelCase_      			=							model.wav_encoder.wav_model.cfg
      else:
            UpperCAmelCase_      			=							model.cfg

      UpperCAmelCase_      			=							fs_config.conv_bias
      UpperCAmelCase_      			=							eval(fs_config.conv_feature_layers				)
      UpperCAmelCase_      			=							[x[0] for x in conv_layers]
      UpperCAmelCase_      			=							[x[1] for x in conv_layers]
      UpperCAmelCase_      			=							[x[2] for x in conv_layers]
      UpperCAmelCase_      			=							'''gelu'''
      UpperCAmelCase_      			=							'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      UpperCAmelCase_      			=							0.0
      UpperCAmelCase_      			=							fs_config.activation_fn.name
      UpperCAmelCase_      			=							fs_config.encoder_embed_dim
      UpperCAmelCase_      			=							0.02
      UpperCAmelCase_      			=							fs_config.encoder_ffn_embed_dim
      UpperCAmelCase_      			=							1e-5
      UpperCAmelCase_      			=							fs_config.encoder_layerdrop
      UpperCAmelCase_      			=							fs_config.encoder_attention_heads
      UpperCAmelCase_      			=							fs_config.conv_pos_groups
      UpperCAmelCase_      			=							fs_config.conv_pos
      UpperCAmelCase_      			=							len(__lowerCAmelCase				)
      UpperCAmelCase_      			=							fs_config.encoder_layers
      UpperCAmelCase_      			=							fs_config.squeeze_factor

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

      UpperCAmelCase_      			=							'''Wav2Vec2FeatureExtractor'''
      UpperCAmelCase_      			=							'''Wav2Vec2CTCTokenizer'''

      return config
@torch.no_grad()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=True				):





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

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

      UpperCAmelCase_      			=							True if config.feat_extract_norm == '''layer''' else False
      UpperCAmelCase_      			=							WavaVecaFeatureExtractor(
          feature_size=1     ,      sampling_rate=16000     ,      padding_value=0     ,      do_normalize=__lowerCAmelCase     ,      return_attention_mask=__lowerCAmelCase     ,      )

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase_      			=							Dictionary.load(__lowerCAmelCase				)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase_      			=							target_dict.pad_index
                  UpperCAmelCase_      			=							target_dict.bos_index
                  UpperCAmelCase_      			=							target_dict.pad_index
                  UpperCAmelCase_      			=							target_dict.bos_index
                  UpperCAmelCase_      			=							target_dict.eos_index
                  UpperCAmelCase_      			=							len(target_dict.symbols				)
                  UpperCAmelCase_      			=							os.path.join(__lowerCAmelCase     ,      "vocab.json"				)
                  if not os.path.isdir(__lowerCAmelCase				):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCAmelCase				)				)
                        return
                  os.makedirs(__lowerCAmelCase     ,      exist_ok=__lowerCAmelCase				)
                  with open(__lowerCAmelCase     ,      "w"     ,      encoding="utf-8"				) as vocab_handle:
                        json.dump(target_dict.indices     ,      __lowerCAmelCase				)
                  UpperCAmelCase_      			=							WavaVecaCTCTokenizer(
                      __lowerCAmelCase     ,      unk_token=target_dict.unk_word     ,      pad_token=target_dict.pad_word     ,      bos_token=target_dict.bos_word     ,      eos_token=target_dict.eos_word     ,      word_delimiter_token="|"     ,      do_lower_case=__lowerCAmelCase     ,      )
                  UpperCAmelCase_      			=							WavaVecaProcessor(feature_extractor=__lowerCAmelCase     ,      tokenizer=__lowerCAmelCase				)
                  processor.save_pretrained(__lowerCAmelCase				)

            UpperCAmelCase_      			=							SEWForCTC(__lowerCAmelCase				)
      else:
            UpperCAmelCase_      			=							SEWModel(__lowerCAmelCase				)
            feature_extractor.save_pretrained(__lowerCAmelCase				)

      recursively_load_weights(__lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase				)

      hf_model.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
  lowerCamelCase       	=						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       	=						parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )









"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''ViTImageProcessor'''
       UpperCamelCase						=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')



       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[int]=None		,    **_UpperCAmelCase      :  List[str]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."		,    _UpperCAmelCase		,    )
                   UpperCAmelCase_      			=							kwargs.pop("feature_extractor"   )

             UpperCAmelCase_      			=							image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`."   )
             if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`."   )

             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )



       def __call__(							self      :  Optional[Any]		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  List[Any]=None		,    **_UpperCAmelCase      :  str   )			->						Optional[int]:


             '''simple docstring'''




             if text is None and visual_prompt is None and images is None:
                   raise ValueError("You have to specify either text, visual prompt or images."   )

             if text is not None and visual_prompt is not None:
                   raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

             if text is not None:
                   UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if images is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None and images is not None:
                   UpperCAmelCase_      			=							{
                       "pixel_values": image_features.pixel_values,
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             elif text is not None and images is not None:
                   UpperCAmelCase_      			=							image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             elif visual_prompt is not None:
                   UpperCAmelCase_      			=							{
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             else:
                   return BatchEncoding(data=dict(**_UpperCAmelCase   )		,    tensor_type=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]		,    *_UpperCAmelCase      :  List[str]		,    **_UpperCAmelCase      :  List[Any]   )			->						Any:


             '''simple docstring'''




             return self.tokenizer.batch_decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Dict		,    *_UpperCAmelCase      :  Tuple		,    **_UpperCAmelCase      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             return self.tokenizer.decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       @property
       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor_class







       @property
       def 					lowercase__    (							self      :  str   )			->						List[Any]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor




