


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()
A      :    Dict		=					logging.get_logger(__name__)

def       lowercase_		(		_A			:    Dict    ,					_A			:    List[Any]=False						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  []

      # 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__		:				int    =  [(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       lowercase_		(		_A			:    Tuple    ,					_A			:    Optional[int]    ,					_A			:    Optional[Any]=False						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    Optional[Any]						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    Optional[Any]    ,					_A			:    Union[str, Any]    ,					_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  dct.pop(_lowerCAmelCase						)
      lowerCamelCase__		:				List[str]    =  val

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCamelCase__		:				Optional[Any]    =  Image.open(requests.get(_lowerCAmelCase    ,					stream=_lowerCAmelCase						).raw						)
      return im

@torch.no_grad()
def       lowercase_		(		_A			:    Dict    ,					_A			:    str    ,					_A			:    str=False						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  BitConfig(
          global_padding="same"    ,					layer_type="bottleneck"    ,					depths=(3, 4, 9)    ,					out_features=["stage3"]    ,					embedding_dynamic_padding=_lowerCAmelCase    ,					)
      lowerCamelCase__		:				Optional[Any]    =  ViTHybridConfig(backbone_config=_lowerCAmelCase    ,					image_size=384    ,					num_labels=1000						)
      lowerCamelCase__		:				Union[str, Any]    =  False

      # load original model from timm
      lowerCamelCase__		:				Union[str, Any]    =  timm.create_model(_lowerCAmelCase    ,					pretrained=_lowerCAmelCase						)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCamelCase__		:				Optional[int]    =  timm_model.state_dict()
      if base_model:
            remove_classification_head_(_lowerCAmelCase						)
      lowerCamelCase__		:				Any    =  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__		:				str    =  """huggingface/label-files"""
      lowerCamelCase__		:				str    =  """imagenet-1k-id2label.json"""
      lowerCamelCase__		:				List[str]    =  json.load(open(hf_hub_download(_lowerCAmelCase    ,					_lowerCAmelCase    ,					repo_type="dataset"						)    ,					"r"						)						)
      lowerCamelCase__		:				Dict    =  {int(_lowerCAmelCase						): v for k, v in idalabel.items()}
      lowerCamelCase__		:				Tuple    =  idalabel
      lowerCamelCase__		:				List[str]    =  {v: k for k, v in idalabel.items()}

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

      # create image processor
      lowerCamelCase__		:				int    =  create_transform(**resolve_data_config({}    ,					model=_lowerCAmelCase						)						)
      lowerCamelCase__		:				List[str]    =  transform.transforms

      lowerCamelCase__		:				List[str]    =  {
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      lowerCamelCase__		:				Optional[int]    =  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__		:				Union[str, Any]    =  prepare_img()
      lowerCamelCase__		:				Dict    =  transform(_lowerCAmelCase						).unsqueeze(0						)
      lowerCamelCase__		:				int    =  processor(_lowerCAmelCase    ,					return_tensors="pt"						).pixel_values

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

      # verify logits
      with torch.no_grad():
            lowerCamelCase__		:				Optional[Any]    =  model(_lowerCAmelCase						)
            lowerCamelCase__		:				List[Any]    =  outputs.logits

      print("Predicted class:"    ,					logits.argmax(-1						).item()						)
      if base_model:
            lowerCamelCase__		:				List[str]    =  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__		:				Any    =  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__":
    A      :    Union[str, Any]		=					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."
    )

    A      :    Dict		=					parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__a          =		{
    "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",
    "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",
}
__a          =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for attribute in key.split("""."""		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

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

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Dict    						=      fairseq_model.state_dict()

      snake_case__  :    List[Any]    						=      hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      snake_case__  :    Optional[int]    						=      None

      for name, value in fairseq_dict.items():
            snake_case__  :    List[Any]    						=      False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
                  snake_case__  :    Union[str, Any]    						=      True
            elif name.split("""."""		)[0] == "proj":
                  snake_case__  :    Tuple    						=      fairseq_model.proj
                  snake_case__  :    int    						=      True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                              snake_case__  :    Optional[Any]    						=      True
                              if "*" in mapped_key:
                                    snake_case__  :    Optional[int]    						=      name.split(_lowerCAmelCase		)[0].split("""."""		)[-2]
                                    snake_case__  :    Tuple    						=      mapped_key.replace("""*"""     ,   _lowerCAmelCase		)
                              if "weight_g" in name:
                                    snake_case__  :    str    						=      """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__  :    int    						=      """weight_v"""
                              elif "bias" in name:
                                    snake_case__  :    Dict    						=      """bias"""
                              elif "weight" in name:
                                    snake_case__  :    Union[str, Any]    						=      """weight"""
                              else:
                                    snake_case__  :    Union[str, Any]    						=      None
                              set_recursively(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(_lowerCAmelCase		)

      logger.warning(f"Unused weights: {unused_weights}"		)

      return proj_weight





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    int    						=      full_name.split("""conv_layers."""		)[-1]
      snake_case__  :    Dict    						=      name.split("""."""		)
      snake_case__  :    Any    						=      int(items[0]		)
      snake_case__  :    Optional[Any]    						=      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."
                  )
                  snake_case__  :    int    						=      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."
                  )
                  snake_case__  :    str    						=      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."
                  )
                  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:
                  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."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__						,				snake_case__  :    str    						=      emb.weight.shape
      snake_case__  :    List[str]    						=      nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      emb.weight.data
      return lin_layer





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    int    						=      f.readlines()
            snake_case__  :    List[Any]    						=      [line.split(""" """		)[0] for line in lines]

      snake_case__  :    Union[str, Any]    						=      len(_lowerCAmelCase		)

      snake_case__  :    Any    						=      {
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(_lowerCAmelCase     ,   range(4     ,   num_words + 4		)		)		)		)
      return vocab_dict





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   )		-> int:
      snake_case__  :    Optional[Any]    						=      WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SpeechaTextaConfig.from_pretrained(
          _lowerCAmelCase     ,   vocab_size=_lowerCAmelCase     ,   decoder_layers=_lowerCAmelCase     ,   do_stable_layer_norm=_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_lowerCAmelCase     ,   return_attention_mask=_lowerCAmelCase     ,   )

      snake_case__						,				snake_case__						,				snake_case__  :    Union[str, Any]    						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]     ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
      snake_case__  :    Tuple    						=      model[0].eval()

      # set weights for wav2vec2 encoder
      snake_case__  :    Optional[Any]    						=      WavaVecaModel(_lowerCAmelCase		)
      snake_case__  :    Dict    						=      recursively_load_weights_wavaveca(model.encoder     ,   _lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      SpeechaTextaForCausalLM(_lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Tuple    						=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,   strict=_lowerCAmelCase		)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""		)
      snake_case__  :    Tuple    						=      nn.Parameter(model.decoder.embed_out.detach()		)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"		)
      logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"		)

      snake_case__  :    List[Any]    						=      SpeechEncoderDecoderModel(encoder=_lowerCAmelCase     ,   decoder=_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      False

      # add projection layer
      snake_case__  :    Union[str, Any]    						=      nn.Parameter(projection_layer.weight		)
      snake_case__  :    int    						=      nn.Parameter(projection_layer.bias		)

      snake_case__  :    Tuple    						=      create_vocab_dict(_lowerCAmelCase		)

      with open(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)     ,   """w"""		) as fp:
            json.dump(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Tuple    						=      SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)		)
      tokenizer.save_pretrained(_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      hf_wavavec.config.to_dict()
      snake_case__  :    Tuple    						=      tokenizer.pad_token_id
      snake_case__  :    Optional[Any]    						=      tokenizer.bos_token_id
      snake_case__  :    int    						=      tokenizer.eos_token_id
      snake_case__  :    str    						=      """speech_to_text_2"""
      snake_case__  :    List[Any]    						=      """wav2vec2"""

      snake_case__  :    List[str]    						=      SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase		)

      hf_wavavec.save_pretrained(_lowerCAmelCase		)
      feature_extractor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		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(
           "--encoder_config_path",
           default="facebook/wav2vec2-large-lv60",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/s2t-small-mustc-en-fr-st",
           type=str,
           help="Path to hf decoder s2t checkpoint config",
       )
       parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       __a          =		parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )
