
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  __lowerCamelCase					( _lowercase			)       ->      Tuple:
     UpperCamelCase						    =					[]
     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  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Dict:
     UpperCamelCase						    =					[]
     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  __lowerCamelCase					( _lowercase			)       ->      List[str]:
     UpperCamelCase						    =					[]
     token.append((F'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')			)
     return token

def  __lowerCamelCase					( )       ->      Dict:
     UpperCamelCase						    =					[]
     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  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      int:
     UpperCamelCase						    =					'imagenet-1k-id2label.json'
     UpperCamelCase						    =					1000

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

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

     UpperCamelCase						    =					UpperCamelCase						    =					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":
          UpperCamelCase						    =					[1, 2, 10]

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

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          UpperCamelCase						    =					[2, 2, 20]
          UpperCamelCase						    =					[3, 12, 16]
          UpperCamelCase						    =					[192, 768, 1024]

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

     UpperCamelCase						    =					OrderedDict()
     UpperCamelCase						    =					[]

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

     UpperCamelCase						    =					list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(_lowercase			)
     for i in range(len(_lowercase			)			):
          UpperCamelCase						    =					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      = 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.'''
 )

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




import logging

from transformers import PretrainedConfig


_snake_case      = logging.getLogger(__name__)


_snake_case      = {
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					int       						="bertabs"

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=3_05_22					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=5_12					, SCREAMING_SNAKE_CASE__			:			List[Any]=6					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=8					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			List[Any]=0.2					, SCREAMING_SNAKE_CASE__			:			Tuple=6					, SCREAMING_SNAKE_CASE__			:			Any=7_68					, SCREAMING_SNAKE_CASE__			:			str=8					, SCREAMING_SNAKE_CASE__			:			Dict=20_48					, SCREAMING_SNAKE_CASE__			:			List[str]=0.2					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					max_pos

      UpperCamelCase						    =					enc_layers
      UpperCamelCase						    =					enc_hidden_size
      UpperCamelCase						    =					enc_heads
      UpperCamelCase						    =					enc_ff_size
      UpperCamelCase						    =					enc_dropout

      UpperCamelCase						    =					dec_layers
      UpperCamelCase						    =					dec_hidden_size
      UpperCamelCase						    =					dec_heads
      UpperCamelCase						    =					dec_ff_size
      UpperCamelCase						    =					dec_dropout


