




'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowercase      :   Union[str, Any]      =       logging.get_logger(__name__)

lowercase      :   Tuple      =       {
    """post_extract_proj""": """feature_projection.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.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
lowercase      :   Dict      =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      Dict        =			{}
     with open(__lowercase     ,   """r"""				) as file:
          for line_number, line in enumerate(__lowercase				):
               snake_case			:      List[str]        =			line.strip()
               if line:
                    snake_case			:      List[str]        =			line.split()
                    snake_case			:      Optional[Any]        =			line_number
                    snake_case			:      str        =			words[0]
                    snake_case			:      str        =			value
     return result



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     for attribute in key.split("""."""				):
          snake_case			:      Optional[int]        =			getattr(__lowercase     ,   __lowercase				)

     snake_case			:      Optional[int]        =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(__lowercase				):
               snake_case			:      List[str]        =			PARAM_MAPPING[full_name.split("""."""				)[-1]]
               snake_case			:      int        =			"""param"""

     if weight_type is not None and weight_type != "param":
          snake_case			:      List[Any]        =			getattr(__lowercase     ,   __lowercase				).shape
     elif weight_type is not None and weight_type == "param":
          snake_case			:      Dict        =			hf_pointer
          for attribute in hf_param_name.split("""."""				):
               snake_case			:      str        =			getattr(__lowercase     ,   __lowercase				)
          snake_case			:      Dict        =			shape_pointer.shape

          # let's reduce dimension
          snake_case			:      Union[str, Any]        =			value[0]
     else:
          snake_case			:      List[str]        =			hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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			:      List[str]        =			value
     elif weight_type == "weight_g":
          snake_case			:      Optional[Any]        =			value
     elif weight_type == "weight_v":
          snake_case			:      int        =			value
     elif weight_type == "bias":
          snake_case			:      Optional[int]        =			value
     elif weight_type == "param":
          for attribute in hf_param_name.split("""."""				):
               snake_case			:      Optional[Any]        =			getattr(__lowercase     ,   __lowercase				)
          snake_case			:      Tuple        =			value
     else:
          snake_case			:      Optional[int]        =			value

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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[Any]        =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(__lowercase				):
               snake_case			:      List[str]        =			PARAM_MAPPING[full_name.split("""."""				)[-1]]
               snake_case			:      str        =			"""param"""

     if weight_type is not None and weight_type != "param":
          snake_case			:      str        =			""".""".join([key, weight_type]				)
     elif weight_type is not None and weight_type == "param":
          snake_case			:      str        =			""".""".join([key, hf_param_name]				)
     else:
          snake_case			:      Dict        =			key

     snake_case			:      Optional[int]        =			value if """lm_head""" in full_key else value[0]


lowercase      :   Union[str, Any]      =       {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase=None     ,   __lowercase=None				):
     snake_case			:      Any        =			False
     for key, mapped_key in MAPPING.items():
          snake_case			:      Optional[Any]        =			"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
               snake_case			:      List[str]        =			True
               if "*" in mapped_key:
                    snake_case			:      Tuple        =			name.split(__lowercase				)[0].split("""."""				)[-2]
                    snake_case			:      Optional[int]        =			mapped_key.replace("""*"""     ,   __lowercase				)
               if "weight_g" in name:
                    snake_case			:      Dict        =			"""weight_g"""
               elif "weight_v" in name:
                    snake_case			:      List[str]        =			"""weight_v"""
               elif "bias" in name:
                    snake_case			:      List[Any]        =			"""bias"""
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    snake_case			:      List[str]        =			"""weight"""
               else:
                    snake_case			:      List[str]        =			None
               if hf_dict is not None:
                    rename_dict(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)
               else:
                    set_recursively(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)
               return is_used
     return is_used



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      List[str]        =			[]
     snake_case			:      Optional[Any]        =			fairseq_model.state_dict()

     snake_case			:      Optional[Any]        =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          snake_case			:      Tuple        =			False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
               snake_case			:      Optional[Any]        =			True
          else:
               snake_case			:      List[Any]        =			load_wavaveca_layer(__lowercase     ,   __lowercase     ,   __lowercase				)
          if not is_used:
               unused_weights.append(__lowercase				)

     logger.warning(F'''Unused weights: {unused_weights}'''				)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      str        =			full_name.split("""conv_layers."""				)[-1]
     snake_case			:      List[str]        =			name.split("""."""				)
     snake_case			:      Optional[Any]        =			int(items[0]				)
     snake_case			:      Any        =			int(items[1]				)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
               snake_case			:      Optional[int]        =			value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
               snake_case			:      Tuple        =			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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
               snake_case			:      Union[str, Any]        =			value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
               snake_case			:      Any        =			value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
     else:
          unused_weights.append(__lowercase				)



@torch.no_grad()
def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=True     ,   __lowercase=False				):
     if config_path is not None:
          snake_case			:      Union[str, Any]        =			WavaVecaConfig.from_pretrained(__lowercase				)
     else:
          snake_case			:      List[Any]        =			WavaVecaConfig()

     if is_seq_class:
          snake_case			:      Dict        =			read_txt_into_dict(__lowercase				)
          snake_case			:      Any        =			idalabel
          snake_case			:      Tuple        =			WavaVecaForSequenceClassification(__lowercase				)
          snake_case			:      List[str]        =			WavaVecaFeatureExtractor(
              feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=__lowercase     ,   return_attention_mask=__lowercase     ,   )
          feature_extractor.save_pretrained(__lowercase				)

     elif is_finetuned:
          if dict_path:
               snake_case			:      Tuple        =			Dictionary.load(__lowercase				)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               snake_case			:      Optional[Any]        =			target_dict.pad_index
               snake_case			:      Optional[Any]        =			target_dict.bos_index
               snake_case			:      Union[str, Any]        =			target_dict.eos_index
               snake_case			:      Optional[Any]        =			len(target_dict.symbols				)
               snake_case			:      Optional[int]        =			os.path.join(__lowercase     ,   """vocab.json"""				)
               if not os.path.isdir(__lowercase				):
                    logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowercase				)				)
                    return
               os.makedirs(__lowercase     ,   exist_ok=__lowercase				)
               snake_case			:      List[Any]        =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               snake_case			:      List[str]        =			0
               snake_case			:      Optional[int]        =			1
               with open(__lowercase     ,   """w"""     ,   encoding="""utf-8"""				) as vocab_handle:
                    json.dump(__lowercase     ,   __lowercase				)
               snake_case			:      List[str]        =			WavaVecaCTCTokenizer(
                   __lowercase     ,   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=__lowercase     ,   )
               snake_case			:      Optional[Any]        =			True if config.feat_extract_norm == """layer""" else False
               snake_case			:      List[str]        =			WavaVecaFeatureExtractor(
                   feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=__lowercase     ,   return_attention_mask=__lowercase     ,   )
               snake_case			:      Optional[int]        =			WavaVecaProcessor(feature_extractor=__lowercase     ,   tokenizer=__lowercase				)
               processor.save_pretrained(__lowercase				)

          snake_case			:      Dict        =			WavaVecaForCTC(__lowercase				)
     else:
          snake_case			:      Any        =			WavaVecaForPreTraining(__lowercase				)

     if is_finetuned or is_seq_class:
          snake_case ,		snake_case ,		snake_case			:      List[Any]        =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]     ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
     else:
          snake_case			:      int        =			argparse.Namespace(task="""audio_pretraining"""				)
          snake_case			:      Dict        =			fairseq.tasks.setup_task(__lowercase				)

          snake_case ,		snake_case ,		snake_case			:      List[Any]        =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,   task=__lowercase				)

     snake_case			:      List[str]        =			model[0].eval()

     recursively_load_weights(__lowercase     ,   __lowercase     ,   not is_finetuned				)

     hf_wavavec.save_pretrained(__lowercase				)


if __name__ == "__main__":
      lowercase      :   Tuple      =       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(
          """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      parser.add_argument(
          """--is_seq_class""",
          action="""store_true""",
          help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
      )
      lowercase      :   Tuple      =       parser.parse_args()

      lowercase      :   Tuple      =       not args.not_finetuned and not args.is_seq_class
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.dict_path,
          is_finetuned,
          args.is_seq_class,
      )









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :   Optional[int]      =       {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   int      =       [
          """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PegasusXForConditionalGeneration""",
          """PegasusXModel""",
          """PegasusXPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_pegasus_x import (
                PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                PegasusXForConditionalGeneration,
                PegasusXModel,
                PegasusXPreTrainedModel,
            )


else:
      import sys

      lowercase      :   Optional[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



