


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 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
       a__					=					[]
       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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[str]       ):
       a__					=					[]
       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 							__lowercase       (			__lowerCAmelCase							:    List[Any]       ):
       a__					=					[]
       token.append((F'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')       )
       return token






def 							__lowercase       (			):
       a__					=					[]
       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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Any       ):
       a__					=					"""imagenet-1k-id2label.json"""
       a__					=					1_0_0_0

       a__					=					"""huggingface/label-files"""
       a__					=					num_labels
       a__					=					json.load(open(cached_download(hf_hub_url(lowercase__ ,   lowercase__ ,   repo_type='dataset'       )       ) ,   'r'       )       )
       a__					=					{int(lowercase__       ): v for k, v in idalabel.items()}

       a__					=					idalabel
       a__					=					{v: k for k, v in idalabel.items()}

       a__					=					CvtConfig(num_labels=lowercase__ ,   idalabel=lowercase__ ,   labelaid=lowercase__       )

       # For depth size 13 (13 = 1+2+10)
       if cvt_model.rsplit('/' ,   1       )[-1][4:6] == "13":
              a__					=					[1, 2, 1_0]

       # For depth size 21 (21 = 1+4+16)
       elif cvt_model.rsplit('/' ,   1       )[-1][4:6] == "21":
              a__					=					[1, 4, 1_6]

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              a__					=					[2, 2, 2_0]
              a__					=					[3, 1_2, 1_6]
              a__					=					[1_9_2, 7_6_8, 1_0_2_4]

       a__					=					CvtForImageClassification(lowercase__       )
       a__					=					AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       )
       a__					=					image_size
       a__					=					torch.load(lowercase__ ,   map_location=torch.device('cpu'       )       )

       a__					=					OrderedDict()
       a__					=					[]

       for idx in range(len(config.depth       )       ):
              if config.cls_token[idx]:
                     a__					=					list_of_state_dict + cls_token(lowercase__       )
              a__					=					list_of_state_dict + embeddings(lowercase__       )
              for cnt in range(config.depth[idx]       ):
                     a__					=					list_of_state_dict + attention(lowercase__ ,   lowercase__       )

       a__					=					list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(lowercase__       )
       for i in range(len(lowercase__       )       ):
              a__					=					original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(lowercase__       )
       model.save_pretrained(lowercase__       )
       image_processor.save_pretrained(lowercase__       )


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

if __name__ == "__main__":
      snake_case							:	Dict					      =				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.'''
      )

      snake_case							:	str					      =				parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)

snake_case							:	List[Any]					      =				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      '''efficientformer'''
					def __init__(				self       :List[str]      ,__snake_case       :List[int] = [3, 2, 6, 4]      ,__snake_case       :List[int] = [48, 96, 2_24, 4_48]      ,__snake_case       :List[bool] = [True, True, True, True]      ,__snake_case       :int = 4_48      ,__snake_case       :int = 32      ,__snake_case       :int = 4      ,__snake_case       :int = 7      ,__snake_case       :int = 5      ,__snake_case       :int = 8      ,__snake_case       :int = 4      ,__snake_case       :float = 0.0      ,__snake_case       :int = 16      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 2      ,__snake_case       :int = 1      ,__snake_case       :float = 0.0      ,__snake_case       :int = 1      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,__snake_case       :float = 1E-5      ,__snake_case       :str = "gelu"      ,__snake_case       :float = 0.02      ,__snake_case       :float = 1E-12      ,__snake_case       :int = 2_24      ,__snake_case       :float = 1E-05      ,**__snake_case       :Dict      ,) ->   None:
												super().__init__(**__snake_case				)

												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					hidden_sizes
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					initializer_range
												a__					=					layer_norm_eps
												a__					=					patch_size
												a__					=					num_channels
												a__					=					depths
												a__					=					mlp_expansion_ratio
												a__					=					downsamples
												a__					=					dim
												a__					=					key_dim
												a__					=					attention_ratio
												a__					=					resolution
												a__					=					pool_size
												a__					=					downsample_patch_size
												a__					=					downsample_stride
												a__					=					downsample_pad
												a__					=					drop_path_rate
												a__					=					num_metaad_blocks
												a__					=					distillation
												a__					=					use_layer_scale
												a__					=					layer_scale_init_value
												a__					=					image_size
												a__					=					batch_norm_eps




