



"""simple docstring"""





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							384
      UpperCAmelCase_    				=							7
      if "tiny" in model_name:
            UpperCAmelCase_    				=							96
            UpperCAmelCase_    				=							(2, 2, 6, 2)
            UpperCAmelCase_    				=							(3, 6, 12, 24)
      elif "small" in model_name:
            UpperCAmelCase_    				=							96
            UpperCAmelCase_    				=							(2, 2, 18, 2)
            UpperCAmelCase_    				=							(3, 6, 12, 24)
      elif "base" in model_name:
            UpperCAmelCase_    				=							128
            UpperCAmelCase_    				=							(2, 2, 18, 2)
            UpperCAmelCase_    				=							(4, 8, 16, 32)
            UpperCAmelCase_    				=							12
            UpperCAmelCase_    				=							512
      elif "large" in model_name:
            UpperCAmelCase_    				=							192
            UpperCAmelCase_    				=							(2, 2, 18, 2)
            UpperCAmelCase_    				=							(6, 12, 24, 48)
            UpperCAmelCase_    				=							12
            UpperCAmelCase_    				=							768

      # set label information
      UpperCAmelCase_    				=							150
      UpperCAmelCase_    				=							"huggingface/label-files"
      UpperCAmelCase_    				=							"ade20k-id2label.json"
      UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      UpperCAmelCase_    				=							{v: k for k, v in idalabel.items()}

      UpperCAmelCase_    				=							SwinConfig(
          embed_dim=lowerCAmelCase__			,    depths=lowerCAmelCase__			,    num_heads=lowerCAmelCase__			,    window_size=lowerCAmelCase__			,    out_features=["stage1", "stage2", "stage3", "stage4"]			,    )
      UpperCAmelCase_    				=							UperNetConfig(
          backbone_config=lowerCAmelCase__			,    auxiliary_in_channels=lowerCAmelCase__			,    num_labels=lowerCAmelCase__			,    idalabel=lowerCAmelCase__			,    labelaid=lowerCAmelCase__			,    )

      return config
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]

      # fmt: off
      # stem
      rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")   )
      rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")   )
      rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")   )
      rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")   )
      # stages
      for i in range(len(config.backbone_config.depths   )   ):
            for j in range(config.backbone_config.depths[i]   ):
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")   )

            if i < 3:
                  rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")   )
                  rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")   )
            rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")   )
            rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")   )

      # decode head
      rename_keys.extend(
          [
              ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
              ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
              ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
              ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
          ]   )
      # fmt: on

      return rename_keys
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
      for i in range(len(backbone_config.depths   )   ):
            UpperCAmelCase_    				=							num_features[i]
            for j in range(backbone_config.depths[i]   ):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""   )
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""   )
                  # next, add query, keys and values (in that order) to the state dict
                  UpperCAmelCase_    				=							in_proj_weight[:dim, :]
                  UpperCAmelCase_    				=							in_proj_bias[: dim]
                  UpperCAmelCase_    				=							in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_    				=							in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[-dim :]
                  # fmt: on
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							x.shape
      UpperCAmelCase_    				=							x.reshape(lowerCAmelCase__			,    4			,    in_channel // 4   )
      UpperCAmelCase_    				=							x[:, [0, 2, 1, 3], :].transpose(1			,    2   ).reshape(lowerCAmelCase__			,    lowerCAmelCase__   )
      return x
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							x.shape
      UpperCAmelCase_    				=							x.reshape(lowerCAmelCase__			,    in_channel // 4			,    4   )
      UpperCAmelCase_    				=							x[:, :, [0, 2, 1, 3]].transpose(1			,    2   ).reshape(lowerCAmelCase__			,    lowerCAmelCase__   )

      return x
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							x.shape[0]
      UpperCAmelCase_    				=							x.reshape(4			,    in_channel // 4   )
      UpperCAmelCase_    				=							x[[0, 2, 1, 3], :].transpose(0			,    1   ).reshape(lowerCAmelCase__   )
      return x
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							x.shape[0]
      UpperCAmelCase_    				=							x.reshape(in_channel // 4			,    4   )
      UpperCAmelCase_    				=							x[:, [0, 2, 1, 3]].transpose(0			,    1   ).reshape(lowerCAmelCase__   )
      return x
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							{
          "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
          "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
          "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
          "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
      }
      UpperCAmelCase_    				=							model_name_to_url[model_name]
      UpperCAmelCase_    				=							torch.hub.load_state_dict_from_url(lowerCAmelCase__			,    map_location="cpu"			,    file_name=lowerCAmelCase__   )[
          "state_dict"
      ]

      for name, param in state_dict.items():
            print(lowerCAmelCase__			,    param.shape   )

      UpperCAmelCase_    				=							get_upernet_config(lowerCAmelCase__   )
      UpperCAmelCase_    				=							UperNetForSemanticSegmentation(lowerCAmelCase__   )
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            UpperCAmelCase_    				=							state_dict.pop(lowerCAmelCase__   )
            if "bn" in key:
                  UpperCAmelCase_    				=							key.replace("bn"			,    "batch_norm"   )
            UpperCAmelCase_    				=							val

      # rename keys
      UpperCAmelCase_    				=							create_rename_keys(lowerCAmelCase__   )
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      read_in_q_k_v(lowerCAmelCase__			,    config.backbone_config   )

      # fix downsample parameters
      for key, value in state_dict.items():
            if "downsample" in key:
                  if "reduction" in key:
                        UpperCAmelCase_    				=							reverse_correct_unfold_reduction_order(lowerCAmelCase__   )
                  if "norm" in key:
                        UpperCAmelCase_    				=							reverse_correct_unfold_norm_order(lowerCAmelCase__   )

      model.load_state_dict(lowerCAmelCase__   )

      # verify on image
      UpperCAmelCase_    				=							"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   ).convert("RGB"   )

      UpperCAmelCase_    				=							SegformerImageProcessor()
      UpperCAmelCase_    				=							processor(lowerCAmelCase__			,    return_tensors="pt"   ).pixel_values

      with torch.no_grad():
            UpperCAmelCase_    				=							model(lowerCAmelCase__   )
            UpperCAmelCase_    				=							outputs.logits

      print(logits.shape   )
      print("First values of logits:"			,    logits[0, 0, :3, :3]   )
      # assert values
      if model_name == "upernet-swin-tiny":
            UpperCAmelCase_    				=							torch.tensor(
                [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]   )
      elif model_name == "upernet-swin-small":
            UpperCAmelCase_    				=							torch.tensor(
                [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]   )
      elif model_name == "upernet-swin-base":
            UpperCAmelCase_    				=							torch.tensor(
                [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]   )
      elif model_name == "upernet-swin-large":
            UpperCAmelCase_    				=							torch.tensor(
                [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]   )
      print("Logits:"			,    outputs.logits[0, 0, :3, :3]   )
      assert torch.allclose(outputs.logits[0, 0, :3, :3]			,    lowerCAmelCase__			,    atol=1e-4   )
      print("Looks ok!"   )

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
            model.save_pretrained(lowerCAmelCase__   )
            print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
            processor.save_pretrained(lowerCAmelCase__   )

      if push_to_hub:
            print(f"""Pushing model and processor for {model_name} to hub"""   )
            model.push_to_hub(f"""openmmlab/{model_name}"""   )
            processor.push_to_hub(f"""openmmlab/{model_name}"""   )


if __name__ == "__main__":
  lowerCamelCase      =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""upernet-swin-tiny""",
      type=str,
      choices=[F"upernet-swin-{size}" for size in ["""tiny""", """small""", """base""", """large"""]],
      help="""Name of the Swin + UperNet model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  lowerCamelCase      =		parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """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""",
}
lowerCamelCase      =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
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

      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":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_g":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_v":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_mean":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_var":
            UpperCAmelCase_    				=							value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase_    				=							value
      elif weight_type == "inv_freq":
            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.wavaveca_conformer.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_    				=							"wav2vec2_conformer." + 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]:
                              UpperCAmelCase_    				=							True
                              if "*" in mapped_key:
                                    UpperCAmelCase_    				=							name.split(lowerCAmelCase__   )[0].split("."   )[-2]
                                    UpperCAmelCase_    				=							mapped_key.replace("*"			,    lowerCAmelCase__   )
                              if "pos_bias_u" in name:
                                    UpperCAmelCase_    				=							None
                              elif "pos_bias_v" in name:
                                    UpperCAmelCase_    				=							None
                              elif "weight_g" in name:
                                    UpperCAmelCase_    				=							"weight_g"
                              elif "weight_v" in name:
                                    UpperCAmelCase_    				=							"weight_v"
                              elif "bias" in name:
                                    UpperCAmelCase_    				=							"bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase_    				=							"weight"
                              elif "running_mean" in name:
                                    UpperCAmelCase_    				=							"running_mean"
                              elif "inv_freq" in name:
                                    UpperCAmelCase_    				=							"inv_freq"
                              elif "running_var" in name:
                                    UpperCAmelCase_    				=							"running_var"
                              elif "num_batches_tracked" in name:
                                    UpperCAmelCase_    				=							"num_batches_tracked"
                              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:
                  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."""   )
                  UpperCAmelCase_    				=							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."""   )
                  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:
                  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."""   )
                  UpperCAmelCase_    				=							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."""   )
                  UpperCAmelCase_    				=							value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      else:
            unused_weights.append(lowerCAmelCase__   )
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None			,    lowerCAmelCase__=True   ):
      if config_path is not None:
            UpperCAmelCase_    				=							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase__			,    hidden_act="swish"   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            UpperCAmelCase_    				=							"rotary"

      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.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__   )
                  UpperCAmelCase_    				=							target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCAmelCase_    				=							0
                  UpperCAmelCase_    				=							1
                  with open(lowerCAmelCase__			,    "w"			,    encoding="utf-8"   ) as vocab_handle:
                        json.dump(lowerCAmelCase__			,    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_    				=							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__			,    )
                  UpperCAmelCase_    				=							WavaVecaProcessor(feature_extractor=lowerCAmelCase__			,    tokenizer=lowerCAmelCase__   )
                  processor.save_pretrained(lowerCAmelCase__   )

            UpperCAmelCase_    				=							WavaVecaConformerForCTC(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerForPreTraining(lowerCAmelCase__   )

      if is_finetuned:
            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,    arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   )}   )
      else:
            UpperCAmelCase_    				=							argparse.Namespace(task="audio_pretraining"   )
            UpperCAmelCase_    				=							fairseq.tasks.setup_task(lowerCAmelCase__   )

            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,    task=lowerCAmelCase__   )

      UpperCAmelCase_    				=							model[0].eval()

      recursively_load_weights(lowerCAmelCase__			,    lowerCAmelCase__			,    not is_finetuned   )

      hf_wavavec.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(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  lowerCamelCase      =		parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )




