
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowercase  :					List[Any]						    =							logging.get_logger(__name__)

lowercase  :					str						    =							{
    """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""",
}
lowercase  :					List[Any]						    =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       for attribute in key.split("""."""				):
              lowercase							:     Tuple       	=      getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       if weight_type is not None:
              lowercase							:     Any       	=      getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				).shape
       else:
              lowercase							:     Dict       	=      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":
              lowercase							:     int       	=      value
       elif weight_type == "weight_g":
              lowercase							:     Optional[int]       	=      value
       elif weight_type == "weight_v":
              lowercase							:     Optional[int]       	=      value
       elif weight_type == "bias":
              lowercase							:     List[Any]       	=      value
       else:
              lowercase							:     Tuple       	=      value

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     int       	=      []
       lowercase							:     str       	=      fairseq_model.state_dict()

       lowercase							:     List[Any]       	=      hf_model.feature_extractor
       lowercase							:     int       	=      hf_model.adapter

       for name, value in fairseq_dict.items():
              lowercase							:     Tuple       	=      False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	hf_model.config.feat_extract_norm == """group"""					,	)
                     lowercase							:     List[str]       	=      True
              elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]				):
                     load_adapter(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     lowercase							:     List[Any]       	=      True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
                                   lowercase							:     str       	=      True
                                   if "*" in mapped_key:
                                          lowercase							:     Optional[Any]       	=      name.split(SCREAMING_SNAKE_CASE__				)[0].split("""."""				)[-2]
                                          lowercase							:     Optional[int]       	=      mapped_key.replace("""*"""					,	SCREAMING_SNAKE_CASE__				)
                                   if "weight_g" in name:
                                          lowercase							:     str       	=      """weight_g"""
                                   elif "weight_v" in name:
                                          lowercase							:     str       	=      """weight_v"""
                                   elif "bias" in name:
                                          lowercase							:     int       	=      """bias"""
                                   elif "weight" in name:
                                          lowercase							:     Dict       	=      """weight"""
                                   else:
                                          lowercase							:     Optional[int]       	=      None
                                   set_recursively(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE__				)

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







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      full_name.split("""adaptor."""				)[-1]
       lowercase							:     int       	=      name.split("""."""				)

       if items[1].isdigit():
              lowercase							:     int       	=      int(items[1]				)
       else:
              lowercase							:     Optional[Any]       	=      None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
                            lowercase							:     Tuple       	=      value
                            logger.info(f"Adapter proj layer norm bias was initialized from {full_name}."				)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
                            lowercase							:     Tuple       	=      value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
                            lowercase							:     Union[str, Any]       	=      value
                            logger.info(f"Adapter proj layer bias was initialized from {full_name}."				)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
                            lowercase							:     Optional[int]       	=      value
                            logger.info(f"Adapter proj layer weight was initialized from {full_name}."				)
       elif isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
                     lowercase							:     int       	=      value
                     logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."				)
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
                     lowercase							:     str       	=      value
                     logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."				)
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       lowercase	,			lowercase							:     Any       	=      emb.weight.shape
       lowercase							:     Dict       	=      nn.Linear(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	bias=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      emb.weight.data
       return lin_layer







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      WavaVecaConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__					,	add_adapter=SCREAMING_SNAKE_CASE__					,	adapter_stride=SCREAMING_SNAKE_CASE__					,	adapter_kernel_size=SCREAMING_SNAKE_CASE__					,	use_auth_token=SCREAMING_SNAKE_CASE__					,	output_hidden_size=SCREAMING_SNAKE_CASE__					,	)
       lowercase							:     str       	=      MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)

       # load model
       lowercase	,			lowercase	,			lowercase							:     Optional[int]       	=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]					,	arg_overrides={
               """config_yaml""": config_yaml_path,
               """data""": """/""".join(dict_path.split("""/"""				)[:-1]				),
               """w2v_path""": checkpoint_path,
               """load_pretrained_decoder_from""": None,
           }					,	)
       lowercase							:     str       	=      model[0].eval()

       # load feature extractor
       lowercase							:     str       	=      WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					,	use_auth_token=SCREAMING_SNAKE_CASE__				)

       # set weights for wav2vec2 encoder
       lowercase							:     Any       	=      WavaVecaModel(SCREAMING_SNAKE_CASE__				)

       recursively_load_weights_wavaveca(model.encoder					,	SCREAMING_SNAKE_CASE__				)

       # load decoder weights
       lowercase							:     List[Any]       	=      MBartForCausalLM(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase							:     List[str]       	=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,	strict=SCREAMING_SNAKE_CASE__				)
       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}"				)

       lowercase							:     Tuple       	=      SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__					,	decoder=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      False

       lowercase							:     List[Any]       	=      MBartaaTokenizer(SCREAMING_SNAKE_CASE__				)
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Union[str, Any]       	=      hf_wavavec.config.to_dict()
       lowercase							:     Any       	=      tokenizer.pad_token_id
       lowercase							:     Dict       	=      tokenizer.bos_token_id
       lowercase							:     List[str]       	=      tokenizer.eos_token_id
       lowercase							:     List[str]       	=      """mbart50"""
       lowercase							:     Tuple       	=      """wav2vec2"""

       lowercase							:     int       	=      tokenizer.eos_token_id
       lowercase							:     Optional[Any]       	=      250_004
       lowercase							:     Optional[int]       	=      tokenizer.eos_token_id

       lowercase							:     Any       	=      SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__				)

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__				)
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
  parser.add_argument(
      """--encoder_config_path""",
      default="""facebook/wav2vec2-xls-r-1b""",
      type=str,
      help="""Path to hf encoder wav2vec2 checkpoint config""",
  )
  parser.add_argument(
      """--decoder_config_path""",
      default="""facebook/mbart-large-50-one-to-many-mmt""",
      type=str,
      help="""Path to hf decoder checkpoint config""",
  )
  parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
  parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
  parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
  parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
  parser.add_argument("""--start_token_id""", default=250004, type=int, help="""`decoder_start_token_id` of model config""")

  lowercase  :					Union[str, Any]						    =							parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.dict_path,
      args.config_yaml_path,
      encoder_config_path=args.encoder_config_path,
      decoder_config_path=args.decoder_config_path,
      add_adapter=args.add_adapter,
      adapter_kernel_size=args.adapter_kernel_size,
      adapter_stride=args.adapter_stride,
      decoder_start_token_id=args.start_token_id,
      encoder_output_dim=args.encoder_output_dim,
  )


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      []
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
               f"stage{idx}.patch_embed.proj.weight",
           )				)
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
               f"stage{idx}.patch_embed.proj.bias",
           )				)
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
               f"stage{idx}.patch_embed.norm.weight",
           )				)
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
               f"stage{idx}.patch_embed.norm.bias",
           )				)
       return embed







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Optional[Any]       	=      []
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj.bias",
           )				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")				)
       return attention_weights







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      []
       token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")				)
       return token







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Optional[Any]       	=      []
       head.append(("""layernorm.weight""", """norm.weight""")				)
       head.append(("""layernorm.bias""", """norm.bias""")				)
       head.append(("""classifier.weight""", """head.weight""")				)
       head.append(("""classifier.bias""", """head.bias""")				)
       return head







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      """imagenet-1k-id2label.json"""
       lowercase							:     List[str]       	=      1_000

       lowercase							:     int       	=      """huggingface/label-files"""
       lowercase							:     Union[str, Any]       	=      num_labels
       lowercase							:     Optional[Any]       	=      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="""dataset"""				)				)					,	"""r"""				)				)
       lowercase							:     List[Any]       	=      {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}

       lowercase							:     Dict       	=      idalabel
       lowercase							:     List[str]       	=      {v: k for k, v in idalabel.items()}

       lowercase							:     List[str]       	=      CvtConfig(num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__				)

       # For depth size 13 (13 = 1+2+10)
       if cvt_model.rsplit("""/"""					,	1				)[-1][4:6] == "13":
              lowercase							:     Tuple       	=      [1, 2, 10]

       # For depth size 21 (21 = 1+4+16)
       elif cvt_model.rsplit("""/"""					,	1				)[-1][4:6] == "21":
              lowercase							:     Dict       	=      [1, 4, 16]

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              lowercase							:     int       	=      [2, 2, 20]
              lowercase							:     Optional[int]       	=      [3, 12, 16]
              lowercase							:     str       	=      [192, 768, 1_024]

       lowercase							:     Union[str, Any]       	=      CvtForImageClassification(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				)
       lowercase							:     Optional[Any]       	=      image_size
       lowercase							:     Union[str, Any]       	=      torch.load(SCREAMING_SNAKE_CASE__					,	map_location=torch.device("""cpu"""				)				)

       lowercase							:     Optional[Any]       	=      OrderedDict()
       lowercase							:     Tuple       	=      []

       for idx in range(len(config.depth				)				):
              if config.cls_token[idx]:
                     lowercase							:     Optional[Any]       	=      list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE__				)
              lowercase							:     str       	=      list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE__				)
              for cnt in range(config.depth[idx]				):
                     lowercase							:     List[str]       	=      list_of_state_dict + attention(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     Optional[Any]       	=      original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  lowercase  :					Tuple						    =							argparse.ArgumentParser()
  parser.add_argument(
      """--cvt_model""",
      default="""cvt-w24""",
      type=str,
      help="""Name of the cvt model you'd like to convert.""",
  )
  parser.add_argument(
      """--image_size""",
      default=384,
      type=int,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--cvt_file_name""",
      default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
      type=str,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowercase  :					Optional[int]						    =							parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
