






"""simple docstring"""


import datasets


_UpperCamelCase    :   List[Any]   			=						'''\
@InProceedings{conneau2018xnli,
  author = "Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin",
  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  location = "Brussels, Belgium",
}
'''

_UpperCamelCase    :   Tuple   			=						'''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''

_UpperCamelCase    :   Optional[int]   			=						'''
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    \'accuracy\': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric("xnli")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}
'''




def       snake_case  (A_				:List[str]     ,      A_				:Optional[int]				):
  return (preds == labels).mean()






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,			_KWARGS_DESCRIPTION	)
class 						snake_case  (  datasets.Metric	):





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      return datasets.MetricInfo(
          description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
              {
                  'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'      ),
                  'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'      ),
              }      )    ,		codebase_urls=[]    ,		reference_urls=[]    ,		format='numpy'    ,		)





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : Optional[Any]    ,		A     : Dict      ):


      '''simple docstring'''




      return {"accuracy": simple_accuracy(lowercase_    ,		lowercase_      )}












"""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       snake_case  (A_				:Dict				):







  '''simple docstring'''

  a					:      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       snake_case  (A_				:Any     ,      A_				:List[Any]				):







  '''simple docstring'''

  a					:      Union[str, 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       snake_case  (A_				:Dict				):







  '''simple docstring'''

  a					:      int    =       []
  token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')				)
  return token




def       snake_case  ():







  '''simple docstring'''

  a					:      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       snake_case  (A_				:int     ,      A_				:Optional[int]     ,      A_				:Dict     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      Optional[Any]    =       'imagenet-1k-id2label.json'
  a					:      Dict    =       1_0_0_0

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

  a					:      str    =       idalabel
  a					:      Optional[int]    =       {v: k for k, v in idalabel.items()}

  a					:      Tuple    =       CvtConfig(num_labels=A_     ,      idalabel=A_     ,      labelaid=A_				)

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

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

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

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

  a					:      int    =       OrderedDict()
  a					:      Any    =       []

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

  a					:      Any    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(A_				)
  for i in range(len(A_				)				):
    a					:      List[Any]    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(A_				)
  model.save_pretrained(A_				)
  image_processor.save_pretrained(A_				)


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

if __name__ == "__main__":
   _UpperCamelCase    :   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.'
   )

   _UpperCamelCase    :   int   			=						parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




