
"""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      lowercase__    (       lowerCAmelCase					:       int			)			->   List[Any]:






    """simple docstring"""




    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      lowercase__    (       lowerCAmelCase					:       Any			,					lowerCAmelCase					:       Optional[int]			)			->   Tuple:






    """simple docstring"""




    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      lowercase__    (       lowerCAmelCase					:       Optional[int]			)			->   Optional[Any]:






    """simple docstring"""




    UpperCAmelCase          =       []
    token.append((F"cvt.encoder.stages.{idx}.cls_token", 'stage2.cls_token')			)
    return token




def      lowercase__    (       )			->   Any:






    """simple docstring"""




    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      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       str			,					lowerCAmelCase					:       str			)			->   Dict:






    """simple docstring"""




    UpperCAmelCase          =       'imagenet-1k-id2label.json'
    UpperCAmelCase          =       1_000

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

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

    UpperCAmelCase          =       UpperCAmelCase          =       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          =       [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, 1_024]

    UpperCAmelCase          =       CvtForImageClassification(lowerCAmelCase			)
    UpperCAmelCase          =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'			)
    UpperCAmelCase          =       image_size
    UpperCAmelCase          =       torch.load(lowerCAmelCase			,					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(lowerCAmelCase			)
        UpperCAmelCase          =       list_of_state_dict + embeddings(lowerCAmelCase			)
        for cnt in range(config.depth[idx]			):
            UpperCAmelCase          =       list_of_state_dict + attention(lowerCAmelCase			,					lowerCAmelCase			)

    UpperCAmelCase          =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(lowerCAmelCase			)
    for i in range(len(lowerCAmelCase			)			):
        UpperCAmelCase          =       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__":
   SCREAMING_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.'''
   )

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





"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Tuple				   =						"ibert"




      def __init__(				self ,							lowercase_=3_0_5_2_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=1E-12 ,							lowercase_=1 ,							lowercase_=0 ,							lowercase_=2 ,							lowercase_="absolute" ,							lowercase_=False ,							lowercase_="none" ,							**lowercase_ ,							)    ->       Optional[int]:
          super().__init__(pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							**lowercase_				)

          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       position_embedding_type
          UpperCAmelCase          =       quant_mode
          UpperCAmelCase          =       force_dequant

class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      @property
      def      a_   (				self				)    ->       Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
              UpperCAmelCase          =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
              UpperCAmelCase          =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]				)



