"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]	)       ->      Optional[int]:
  _lowerCAmelCase						:       str  			=     []
  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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Optional[Any]	)       ->      Dict:
  _lowerCAmelCase						:       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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict	)       ->      int:
  _lowerCAmelCase						:       Tuple  			=     []
  token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")	)
  return token





def 							SCREAMING_SNAKE_CASE     (			)       ->      Tuple:
  _lowerCAmelCase						:       Optional[int]  			=     []
  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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       List[Any]	)       ->      Any:
  _lowerCAmelCase						:       List[Any]  			=     '''imagenet-1k-id2label.json'''
  _lowerCAmelCase						:       str  			=     1000

  _lowerCAmelCase						:       Tuple  			=     '''huggingface/label-files'''
  _lowerCAmelCase						:       List[Any]  			=     num_labels
  _lowerCAmelCase						:       List[Any]  			=     json.load(open(cached_download(hf_hub_url(snake_case__			,snake_case__			,repo_type="""dataset"""	)	)			,"""r"""	)	)
  _lowerCAmelCase						:       List[Any]  			=     {int(snake_case__	): v for k, v in idalabel.items()}

  _lowerCAmelCase						:       int  			=     idalabel
  _lowerCAmelCase						:       Any  			=     {v: k for k, v in idalabel.items()}

  _lowerCAmelCase						:       List[str]  			=     CvtConfig(num_labels=snake_case__			,idalabel=snake_case__			,labelaid=snake_case__	)

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

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

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

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

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

  for idx in range(len(config.depth	)	):
    if config.cls_token[idx]:
      _lowerCAmelCase						:       Optional[int]  			=     list_of_state_dict + cls_token(snake_case__	)
    _lowerCAmelCase						:       List[Any]  			=     list_of_state_dict + embeddings(snake_case__	)
    for cnt in range(config.depth[idx]	):
      _lowerCAmelCase						:       Dict  			=     list_of_state_dict + attention(snake_case__			,snake_case__	)

  _lowerCAmelCase						:       Optional[int]  			=     list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(snake_case__	)
  for i in range(len(snake_case__	)	):
    _lowerCAmelCase						:       List[str]  			=     original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(snake_case__	)
  model.save_pretrained(snake_case__	)
  image_processor.save_pretrained(snake_case__	)


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

if __name__ == "__main__":
     _a      :		Optional[Any]           =   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.'
     )

     _a      :		Union[str, Any]           =   parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




