
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: int=False      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]

     # fmt: off
     # stem:
     rename_keys.append(("cls_token", "vit.embeddings.cls_token")      )
     rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")      )

     rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")      )
     rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")      )

     # backbone
     rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")      )
     rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")      )
     rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")      )

     for stage_idx in range(len(config.backbone_config.depths      )      ):
          for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')      )
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')      )

          rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')      )
          rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')      )
          rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')      )

     # transformer encoder
     for i in range(config.num_hidden_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')      )
          rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')      )
          rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')      )
          rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')      )
          rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')      )
          rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')      )
          rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')      )
          rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')      )
          rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')      )
          rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')      )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
                  ("pre_logits.fc.weight", "pooler.dense.weight"),
                  ("pre_logits.fc.bias", "pooler.dense.bias"),
              ]      )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          lowerCAmelCase__          =			[(pair[0], pair[1][4:]) if pair[1].startswith("vit"      ) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("norm.weight", "vit.layernorm.weight"),
                  ("norm.bias", "vit.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]      )
     # fmt: on

     return rename_keys



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Any=False      ):
     """simple docstring"""

     for i in range(config.num_hidden_layers      ):
          if base_model:
               lowerCAmelCase__          =			""
          else:
               lowerCAmelCase__          =			"vit."
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          lowerCAmelCase__          =			state_dict.pop(F'blocks.{i}.attn.qkv.weight'      )
          lowerCAmelCase__          =			state_dict.pop(F'blocks.{i}.attn.qkv.bias'      )
          # next, add query, keys and values (in that order) to the state dict
          lowerCAmelCase__          =			in_proj_weight[
              : config.hidden_size, :
          ]
          lowerCAmelCase__          =			in_proj_bias[: config.hidden_size]
          lowerCAmelCase__          =			in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowerCAmelCase__          =			in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          lowerCAmelCase__          =			in_proj_weight[
              -config.hidden_size :, :
          ]
          lowerCAmelCase__          =			in_proj_bias[-config.hidden_size :]



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(lowerCamelCase__       ,    lowerCamelCase__      )



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			dct.pop(lowerCamelCase__      )
     lowerCAmelCase__          =			val



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase__          =			Image.open(requests.get(lowerCamelCase__       ,    stream=lowerCamelCase__      ).raw      )
     return im



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Optional[int]=False      ):
     """simple docstring"""

     lowerCAmelCase__          =			BitConfig(
         global_padding="same"       ,    layer_type="bottleneck"       ,    depths=(3, 4, 9)       ,    out_features=["stage3"]       ,    embedding_dynamic_padding=lowerCamelCase__       ,    )
     lowerCAmelCase__          =			ViTHybridConfig(backbone_config=lowerCamelCase__       ,    image_size=384       ,    num_labels=1000      )
     lowerCAmelCase__          =			False

     # load original model from timm
     lowerCAmelCase__          =			timm.create_model(lowerCamelCase__       ,    pretrained=lowerCamelCase__      )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowerCAmelCase__          =			timm_model.state_dict()
     if base_model:
          remove_classification_head_(lowerCamelCase__      )
     lowerCAmelCase__          =			create_rename_keys(lowerCamelCase__       ,    lowerCamelCase__      )
     for src, dest in rename_keys:
          rename_key(lowerCamelCase__       ,    lowerCamelCase__       ,    lowerCamelCase__      )
     read_in_q_k_v(lowerCamelCase__       ,    lowerCamelCase__       ,    lowerCamelCase__      )

     lowerCAmelCase__          =			"huggingface/label-files"
     lowerCAmelCase__          =			"imagenet-1k-id2label.json"
     lowerCAmelCase__          =			json.load(open(hf_hub_download(lowerCamelCase__       ,    lowerCamelCase__       ,    repo_type="dataset"      )       ,    "r"      )      )
     lowerCAmelCase__          =			{int(lowerCamelCase__      ): v for k, v in idalabel.items()}
     lowerCAmelCase__          =			idalabel
     lowerCAmelCase__          =			{v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          lowerCAmelCase__          =			ViTHybridModel(lowerCamelCase__      ).eval()
     else:
          lowerCAmelCase__          =			ViTHybridForImageClassification(lowerCamelCase__      ).eval()
     model.load_state_dict(lowerCamelCase__      )

     # create image processor
     lowerCAmelCase__          =			create_transform(**resolve_data_config({}       ,    model=lowerCamelCase__      )      )
     lowerCAmelCase__          =			transform.transforms

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

     lowerCAmelCase__          =			ViTHybridImageProcessor(
         do_resize=lowerCamelCase__       ,    size={"shortest_edge": timm_transforms[0].size}       ,    resample=pillow_resamplings[timm_transforms[0].interpolation.value]       ,    do_center_crop=lowerCamelCase__       ,    crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}       ,    do_normalize=lowerCamelCase__       ,    image_mean=timm_transforms[-1].mean.tolist()       ,    image_std=timm_transforms[-1].std.tolist()       ,    )

     lowerCAmelCase__          =			prepare_img()
     lowerCAmelCase__          =			transform(lowerCamelCase__      ).unsqueeze(0      )
     lowerCAmelCase__          =			processor(lowerCamelCase__       ,    return_tensors="pt"      ).pixel_values

     # verify pixel values
     assert torch.allclose(lowerCamelCase__       ,    lowerCamelCase__      )

     # verify logits
     with torch.no_grad():
          lowerCAmelCase__          =			model(lowerCamelCase__      )
          lowerCAmelCase__          =			outputs.logits

     print("Predicted class:"       ,    logits.argmax(-1      ).item()      )
     if base_model:
          lowerCAmelCase__          =			timm_model.forward_features(lowerCamelCase__      )
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(lowerCamelCase__       ,    outputs.pooler_output       ,    atol=1E-3      )
     else:
          lowerCAmelCase__          =			timm_model(lowerCamelCase__      )
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(lowerCamelCase__       ,    outputs.logits       ,    atol=1E-3      )
     print("Looks ok!"      )

     if pytorch_dump_folder_path is not None:
          Path(lowerCamelCase__      ).mkdir(exist_ok=lowerCamelCase__      )
          print(F'Saving model {vit_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 to the hub {vit_name}'      )
          model.push_to_hub(F'ybelkada/{vit_name}'      )
          processor.push_to_hub(F'ybelkada/{vit_name}'      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--vit_name',
         default='vit_base_r50_s16_384',
         type=str,
         help='Name of the hybrid ViT timm 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 to upload the model to the HuggingFace hub.'
     )

     UpperCamelCase								=					parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






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()
UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    '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',
}
UpperCamelCase								=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			{}
     with open(lowerCAmelCase_       ,    "r"      ) as file:
          for line_number, line in enumerate(lowerCAmelCase_      ):
               lowerCAmelCase__          =			line.strip()
               if line:
                    lowerCAmelCase__          =			line.split()
                    lowerCAmelCase__          =			line_number
                    lowerCAmelCase__          =			words[0]
                    lowerCAmelCase__          =			value
     return result



def 							_A       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     for attribute in key.split("."      ):
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      ).shape
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			hf_pointer
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			shape_pointer.shape

          # let's reduce dimension
          lowerCAmelCase__          =			value[0]
     else:
          lowerCAmelCase__          =			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":
          lowerCAmelCase__          =			value
     elif weight_type == "weight_g":
          lowerCAmelCase__          =			value
     elif weight_type == "weight_v":
          lowerCAmelCase__          =			value
     elif weight_type == "bias":
          lowerCAmelCase__          =			value
     elif weight_type == "param":
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			value
     else:
          lowerCAmelCase__          =			value

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



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

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

     lowerCAmelCase__          =			value if "lm_head" in full_key else value[0]


UpperCamelCase								=					{
    '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 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str=None       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     lowerCAmelCase__          =			False
     for key, mapped_key in MAPPING.items():
          lowerCAmelCase__          =			"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]:
               lowerCAmelCase__          =			True
               if "*" in mapped_key:
                    lowerCAmelCase__          =			name.split(lowerCAmelCase_      )[0].split("."      )[-2]
                    lowerCAmelCase__          =			mapped_key.replace("*"       ,    lowerCAmelCase_      )
               if "weight_g" in name:
                    lowerCAmelCase__          =			"weight_g"
               elif "weight_v" in name:
                    lowerCAmelCase__          =			"weight_v"
               elif "bias" in name:
                    lowerCAmelCase__          =			"bias"
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    lowerCAmelCase__          =			"weight"
               else:
                    lowerCAmelCase__          =			None
               if hf_dict is not None:
                    rename_dict(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               else:
                    set_recursively(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               return is_used
     return is_used



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			fairseq_model.state_dict()

     lowerCAmelCase__          =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          lowerCAmelCase__          =			False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    hf_model.config.feat_extract_norm == "group"       ,    )
               lowerCAmelCase__          =			True
          else:
               lowerCAmelCase__          =			load_wavaveca_layer(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
          if not is_used:
               unused_weights.append(lowerCAmelCase_      )

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



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			full_name.split("conv_layers."      )[-1]
     lowerCAmelCase__          =			name.split("."      )
     lowerCAmelCase__          =			int(items[0]      )
     lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: Optional[int]=True       ,    lowerCAmelCase_					: List[str]=False      ):
     """simple docstring"""

     if config_path is not None:
          lowerCAmelCase__          =			WavaVecaConfig.from_pretrained(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaConfig()

     if is_seq_class:
          lowerCAmelCase__          =			read_txt_into_dict(lowerCAmelCase_      )
          lowerCAmelCase__          =			idalabel
          lowerCAmelCase__          =			WavaVecaForSequenceClassification(lowerCAmelCase_      )
          lowerCAmelCase__          =			WavaVecaFeatureExtractor(
              feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
          feature_extractor.save_pretrained(lowerCAmelCase_      )

     elif is_finetuned:
          if dict_path:
               lowerCAmelCase__          =			Dictionary.load(lowerCAmelCase_      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowerCAmelCase__          =			target_dict.pad_index
               lowerCAmelCase__          =			target_dict.bos_index
               lowerCAmelCase__          =			target_dict.eos_index
               lowerCAmelCase__          =			len(target_dict.symbols      )
               lowerCAmelCase__          =			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_      )
               lowerCAmelCase__          =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase__          =			0
               lowerCAmelCase__          =			1
               with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"      ) as vocab_handle:
                    json.dump(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			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_       ,    )
               lowerCAmelCase__          =			True if config.feat_extract_norm == "layer" else False
               lowerCAmelCase__          =			WavaVecaFeatureExtractor(
                   feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,    tokenizer=lowerCAmelCase_      )
               processor.save_pretrained(lowerCAmelCase_      )

          lowerCAmelCase__          =			WavaVecaForCTC(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaForPreTraining(lowerCAmelCase_      )

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

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

     lowerCAmelCase__          =			model[0].eval()

     recursively_load_weights(lowerCAmelCase_       ,    lowerCAmelCase_       ,    not is_finetuned      )

     hf_wavavec.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					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',
     )
     UpperCamelCase								=					parser.parse_args()

     UpperCamelCase								=					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,
     )
