

"""simple docstring"""
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 					a__						(						lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							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 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							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 					a__						(						lowerCAmelCase    )	-> Tuple:
      UpperCAmelCase__			:							Any							=    []
      token.append((F"""cvt.encoder.stages.{idx}.cls_token""", """stage2.cls_token""")    )
      return token




def 					a__						(						)	-> Optional[int]:
      UpperCAmelCase__			:							Tuple							=    []
      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 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> int:
      UpperCAmelCase__			:							Dict							=    """imagenet-1k-id2label.json"""
      UpperCAmelCase__			:							List[str]							=    10_00

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

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

      UpperCAmelCase__			:							Union[str, Any]							=    CvtConfig(num_labels=lowerCAmelCase			,	idalabel=lowerCAmelCase			,	labelaid=lowerCAmelCase    )

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

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

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            UpperCAmelCase__			:							Any							=    [2, 2, 20]
            UpperCAmelCase__			:							str							=    [3, 12, 16]
            UpperCAmelCase__			:							Dict							=    [1_92, 7_68, 10_24]

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

      UpperCAmelCase__			:							Optional[int]							=    OrderedDict()
      UpperCAmelCase__			:							Optional[Any]							=    []

      for idx in range(len(config.depth    )    ):
            if config.cls_token[idx]:
                  UpperCAmelCase__			:							Dict							=    list_of_state_dict + cls_token(lowerCAmelCase    )
            UpperCAmelCase__			:							Dict							=    list_of_state_dict + embeddings(lowerCAmelCase    )
            for cnt in range(config.depth[idx]    ):
                  UpperCAmelCase__			:							Any							=    list_of_state_dict + attention(lowerCAmelCase			,	lowerCAmelCase    )

      UpperCAmelCase__			:							Any							=    list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(lowerCAmelCase    )
      for i in range(len(lowerCAmelCase    )    ):
            UpperCAmelCase__			:							Any							=    original_weights[list_of_state_dict[i][1]]

      model.load_state_dict(lowerCAmelCase    )
      model.save_pretrained(lowerCAmelCase    )
      image_processor.save_pretrained(lowerCAmelCase    )


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

if __name__ == "__main__":
    _A							=  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=3_84,
        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."""
    )

    _A							=  parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




"""simple docstring"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

