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()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    int:



      '''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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=False		)						->    Tuple:



      '''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       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  )						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)
      return im
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=False		)						->    int:



      '''simple docstring'''

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

      # load original model from timm
      _lowerCamelCase			    =				timm.create_model(__UpperCAmelCase ,						pretrained=__UpperCAmelCase		)
      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_(__UpperCAmelCase		)
      _lowerCamelCase			    =				create_rename_keys(__UpperCAmelCase ,						__UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      read_in_q_k_v(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      _lowerCamelCase			    =				'''huggingface/label-files'''
      _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
      _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
      _lowerCamelCase			    =				{int(__UpperCAmelCase		): 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(__UpperCAmelCase		).eval()
      else:
            _lowerCamelCase			    =				ViTHybridForImageClassification(__UpperCAmelCase		).eval()
      model.load_state_dict(__UpperCAmelCase		)

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

      _lowerCamelCase			    =				{
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

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

      _lowerCamelCase			    =				prepare_img()
      _lowerCamelCase			    =				transform(__UpperCAmelCase		).unsqueeze(0		)
      _lowerCamelCase			    =				processor(__UpperCAmelCase ,						return_tensors='''pt'''		).pixel_values

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

      # verify logits
      with torch.no_grad():
            _lowerCamelCase			    =				model(__UpperCAmelCase		)
            _lowerCamelCase			    =				outputs.logits

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

      if pytorch_dump_folder_path is not None:
            Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
            print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'		)
            model.save_pretrained(__UpperCAmelCase		)
            print(F'Saving processor to {pytorch_dump_folder_path}'		)
            processor.save_pretrained(__UpperCAmelCase		)

      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__":
     snake_case__				= 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.'
     )

     snake_case__				= 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 (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

snake_case__				= {
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
snake_case__				= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      for attribute in key.split('''.'''		):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        _lowerCamelCase			    =				'''lm_head'''

            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		)

      if weight_type is not None:
            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		).shape
      else:
            _lowerCamelCase			    =				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":
            _lowerCamelCase			    =				value
      elif weight_type == "weight_g":
            _lowerCamelCase			    =				value
      elif weight_type == "weight_v":
            _lowerCamelCase			    =				value
      elif weight_type == "bias":
            _lowerCamelCase			    =				value
      else:
            _lowerCamelCase			    =				value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				fairseq_model.state_dict()

      _lowerCamelCase			    =				hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCamelCase			    =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
                  _lowerCamelCase			    =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        _lowerCamelCase			    =				'''unispeech.''' + 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(__UpperCAmelCase		)[0].split('''.'''		)[-2]
                                    _lowerCamelCase			    =				mapped_key.replace('''*''' ,						__UpperCAmelCase		)
                              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
                              set_recursively(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(__UpperCAmelCase		)

      logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    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:
                  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.'
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _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:
                  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."
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(__UpperCAmelCase		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=True		)						->    Union[str, Any]:



      '''simple docstring'''

      if config_path is not None:
            _lowerCamelCase			    =				UniSpeechConfig.from_pretrained(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _lowerCamelCase			    =				Dictionary.load_from_json(__UpperCAmelCase		)

                  # 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(__UpperCAmelCase ,						'''vocab.json'''		)
                  if not os.path.isdir(__UpperCAmelCase		):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__UpperCAmelCase		)		)
                        return
                  os.makedirs(__UpperCAmelCase ,						exist_ok=__UpperCAmelCase		)
                  _lowerCamelCase			    =				target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase			    =				42
                  _lowerCamelCase			    =				43
                  with open(__UpperCAmelCase ,						'''w''' ,						encoding='''utf-8'''		) as vocab_handle:
                        json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)
                  _lowerCamelCase			    =				WavaVecaPhonemeCTCTokenizer(
                      __UpperCAmelCase ,						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=__UpperCAmelCase ,						)
                  _lowerCamelCase			    =				True if config.feat_extract_norm == '''layer''' else False
                  _lowerCamelCase			    =				WavaVecaFeatureExtractor(
                      feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=__UpperCAmelCase ,						return_attention_mask=__UpperCAmelCase ,						)
                  _lowerCamelCase			    =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase ,						tokenizer=__UpperCAmelCase		)
                  processor.save_pretrained(__UpperCAmelCase		)

            _lowerCamelCase			    =				UniSpeechForCTC(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechForPreTraining(__UpperCAmelCase		)

      if is_finetuned:
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path] ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		), '''w2v_path''': checkpoint_path}		)
      else:
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

      _lowerCamelCase			    =				model[0].eval()

      recursively_load_weights(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      hf_unispeech.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__				= 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'
     )
     snake_case__				= parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )