





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       :			int			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						{
    """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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   for attribute in key.split("."   ):
      a__	:						List[Any]      		=  getattr(_lowerCamelCase							, _lowerCamelCase   )

   if weight_type is not None:
      a__	:						str      		=  getattr(_lowerCamelCase							, _lowerCamelCase   ).shape
   else:
      a__	:						Optional[int]      		=  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":
      a__	:						str      		=  value
   elif weight_type == "weight_g":
      a__	:						Dict      		=  value
   elif weight_type == "weight_v":
      a__	:						List[str]      		=  value
   elif weight_type == "bias":
      a__	:						str      		=  value
   else:
      a__	:						Union[str, Any]      		=  value

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






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[Any]      		=  []
   a__	:						Optional[int]      		=  fairseq_model.state_dict()

   a__	:						Any      		=  hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      a__	:						int      		=  False
      if "conv_layers" in name:
         load_conv_layer(
             _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, hf_model.config.feat_extract_norm == "group"							, )
         a__	:						Optional[Any]      		=  True
      else:
         for key, mapped_key in MAPPING.items():
            a__	:						Dict      		=  "hubert." + 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] and not is_finetuned):
               a__	:						str      		=  True
               if "*" in mapped_key:
                  a__	:						str      		=  name.split(_lowerCamelCase   )[0].split("."   )[-2]
                  a__	:						Tuple      		=  mapped_key.replace("*"							, _lowerCamelCase   )
               if "weight_g" in name:
                  a__	:						List[str]      		=  "weight_g"
               elif "weight_v" in name:
                  a__	:						int      		=  "weight_v"
               elif "weight" in name:
                  a__	:						Tuple      		=  "weight"
               elif "bias" in name:
                  a__	:						int      		=  "bias"
               else:
                  a__	:						Dict      		=  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   ):
   a__	:						str      		=  full_name.split("conv_layers."   )[-1]
   a__	:						Union[str, Any]      		=  name.split("."   )
   a__	:						List[Any]      		=  int(items[0]   )
   a__	:						str      		=  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."""
         )
         a__	:						Any      		=  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."""
         )
         a__	:						Optional[Any]      		=  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."
         )
         a__	:						Optional[Any]      		=  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."""
         )
         a__	:						List[Any]      		=  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:
      a__	:						List[Any]      		=  HubertConfig.from_pretrained(_lowerCamelCase   )
   else:
      a__	:						List[Any]      		=  HubertConfig()

   if is_finetuned:
      if dict_path:
         a__	:						Optional[int]      		=  Dictionary.load(_lowerCamelCase   )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         a__	:						Dict      		=  target_dict.pad_index
         a__	:						Any      		=  target_dict.bos_index
         a__	:						Tuple      		=  target_dict.eos_index
         a__	:						int      		=  len(target_dict.symbols   )
         a__	:						Tuple      		=  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   )
         a__	:						Optional[Any]      		=  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							, )
         a__	:						Dict      		=  True if config.feat_extract_norm == "layer" else False
         a__	:						Tuple      		=  WavaVecaFeatureExtractor(
             feature_size=1							, sampling_rate=1_6000							, padding_value=0							, do_normalize=_lowerCamelCase							, return_attention_mask=_lowerCamelCase							, )
         a__	:						int      		=  WavaVecaProcessor(feature_extractor=_lowerCamelCase							, tokenizer=_lowerCamelCase   )
         processor.save_pretrained(_lowerCamelCase   )

      a__	:						str      		=  HubertForCTC(_lowerCamelCase   )
   else:
      a__	:						int      		=  HubertModel(_lowerCamelCase   )

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

   a__	:						Optional[Any]      		=  model[0].eval()

   recursively_load_weights(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase   )

   hf_wavavec.save_pretrained(_lowerCamelCase   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			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(
      """--not_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_hubert_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )








import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Dict      		=  old_name

   if "patch_embed" in old_name:
      a__		,     a__		,     a__	:						Union[str, Any]      		=  old_name.split("."   )

      if layer == "0":
         a__	:						Union[str, Any]      		=  old_name.replace("0"							, "convolution1"   )
      elif layer == "1":
         a__	:						Dict      		=  old_name.replace("1"							, "batchnorm_before"   )
      elif layer == "3":
         a__	:						List[str]      		=  old_name.replace("3"							, "convolution2"   )
      else:
         a__	:						Optional[Any]      		=  old_name.replace("4"							, "batchnorm_after"   )

   if "network" in old_name and re.search(r"\d\.\d"							, lowerCamelCase   ):
      a__	:						List[str]      		=  r"\b\d{2}\b"
      if bool(re.search(lowerCamelCase							, lowerCamelCase   )   ):
         a__	:						Optional[int]      		=  re.search(r"\d\.\d\d."							, lowerCamelCase   ).group()
      else:
         a__	:						Any      		=  re.search(r"\d\.\d."							, lowerCamelCase   ).group()
      if int(match[0]   ) < 6:
         a__	:						List[Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         a__	:						int      		=  trimmed_name.replace("network"							, match[0] + ".meta4D_layers.blocks." + match[2:-1]   )
         a__	:						List[Any]      		=  "intermediate_stages." + trimmed_name
      else:
         a__	:						Union[str, Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         if int(match[2]   ) < num_meta4D_last_stage:
            a__	:						Optional[Any]      		=  trimmed_name.replace("network"							, "meta4D_layers.blocks." + match[2]   )
         else:
            a__	:						Union[str, Any]      		=  str(int(match[2]   ) - num_meta4D_last_stage   )
            a__	:						str      		=  trimmed_name.replace("network"							, "meta3D_layers.blocks." + layer_index   )
            if "norm1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("norm1"							, "layernorm1"   )
            elif "norm2" in old_name:
               a__	:						Optional[int]      		=  trimmed_name.replace("norm2"							, "layernorm2"   )
            elif "fc1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("fc1"							, "linear_in"   )
            elif "fc2" in old_name:
               a__	:						Any      		=  trimmed_name.replace("fc2"							, "linear_out"   )

         a__	:						Any      		=  "last_stage." + trimmed_name

   elif "network" in old_name and re.search(r".\d."							, lowerCamelCase   ):
      a__	:						List[str]      		=  old_name.replace("network"							, "intermediate_stages"   )

   if "fc" in new_name:
      a__	:						str      		=  new_name.replace("fc"							, "convolution"   )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      a__	:						str      		=  new_name.replace("norm1"							, "batchnorm_before"   )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      a__	:						Any      		=  new_name.replace("norm2"							, "batchnorm_after"   )
   if "proj" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("proj"							, "projection"   )
   if "dist_head" in new_name:
      a__	:						Tuple      		=  new_name.replace("dist_head"							, "distillation_classifier"   )
   elif "head" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("head"							, "classifier"   )
   elif "patch_embed" in new_name:
      a__	:						Tuple      		=  "efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      a__	:						Union[str, Any]      		=  new_name.replace("norm"							, "layernorm"   )
      a__	:						Optional[int]      		=  "efficientformer." + new_name
   else:
      a__	:						List[Any]      		=  "efficientformer.encoder." + new_name

   return new_name






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for key in checkpoint.copy().keys():
      a__	:						Optional[Any]      		=  checkpoint.pop(lowerCamelCase   )
      a__	:						Dict      		=  val

   return checkpoint






def       _A  (      ):
   a__	:						Tuple      		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__	:						List[Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

   return image






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[str]      		=  torch.load(lowerCamelCase							, map_location="cpu"   )["model"]
   a__	:						str      		=  EfficientFormerConfig.from_json_file(lowerCamelCase   )
   a__	:						int      		=  EfficientFormerForImageClassificationWithTeacher(lowerCamelCase   )
   a__	:						Optional[Any]      		=  "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

   a__	:						Tuple      		=  config.depths[-1] - config.num_metaad_blocks + 1
   a__	:						Union[str, Any]      		=  convert_torch_checkpoint(lowerCamelCase							, lowerCamelCase   )

   model.load_state_dict(lowerCamelCase   )
   model.eval()

   a__	:						Dict      		=  {
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   a__	:						str      		=  prepare_img()
   a__	:						Dict      		=  256
   a__	:						Union[str, Any]      		=  224
   a__	:						List[str]      		=  EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}							, crop_size={"height": crop_size, "width": crop_size}							, resample=pillow_resamplings["bicubic"]							, )
   a__	:						List[str]      		=  processor(images=lowerCamelCase							, return_tensors="pt"   ).pixel_values

   # original processing pipeline
   a__	:						List[str]      		=  Compose(
       [
           Resize(lowerCamelCase							, interpolation=pillow_resamplings["bicubic"]   ),
           CenterCrop(lowerCamelCase   ),
           ToTensor(),
           Normalize(lowerCamelCase							, lowerCamelCase   ),
       ]   )
   a__	:						List[Any]      		=  image_transforms(lowerCamelCase   ).unsqueeze(0   )

   assert torch.allclose(lowerCamelCase							, lowerCamelCase   )

   a__	:						Optional[int]      		=  model(lowerCamelCase   )
   a__	:						Any      		=  outputs.logits

   a__	:						Optional[Any]      		=  (1, 1000)

   if "l1" in model_name:
      a__	:						Tuple      		=  torch.Tensor(
          [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      a__	:						int      		=  torch.Tensor(
          [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      a__	:						Optional[Any]      		=  torch.Tensor(
          [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]   )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""   )

   # Save Checkpoints
   Path(lowerCamelCase   ).mkdir(exist_ok=lowerCamelCase   )
   model.save_pretrained(lowerCamelCase   )
   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
   processor.save_pretrained(lowerCamelCase   )
   print(F"""Processor successfuly saved at {pytorch_dump_path}"""   )

   if push_to_hub:
      print("Pushing model to the hub..."   )

      model.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add model"							, use_temp_dir=lowerCamelCase							, )
      processor.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add image processor"							, use_temp_dir=lowerCamelCase							, )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			str			      =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )

