


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


def    __lowerCamelCase   (      )     ->    str:
   lowerCamelCase_						:   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    __lowerCamelCase   (      A__						: Any						,						A__						: Any						,						A__						: Any						,						A__						: Tuple					)     ->    int:
   lowerCamelCase_						:   List[str]		= """imagenet-1k-id2label.json"""
   lowerCamelCase_						:   Optional[int]		= 1000

   lowerCamelCase_						:   Union[str, Any]		= """huggingface/label-files"""
   lowerCamelCase_						:   Optional[Any]		= num_labels
   lowerCamelCase_						:   Optional[Any]		= json.load(open(cached_download(hf_hub_url(A__						,						A__						,						repo_type="""dataset"""					)					)						,						"""r"""					)					)
   lowerCamelCase_						:   str		= {int(A__					): v for k, v in idalabel.items()}

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

   lowerCamelCase_						:   Optional[Any]		= 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":
      lowerCamelCase_						:   Dict		= [1, 2, 10]

   # For depth size 21 (21 = 1+4+16)
   elif cvt_model.rsplit("""/"""						,						1					)[-1][4:6] == "21":
      lowerCamelCase_						:   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_						:   Optional[Any]		= [3, 12, 16]
      lowerCamelCase_						:   Dict		= [192, 768, 1024]

   lowerCamelCase_						:   Any		= CvtForImageClassification(A__					)
   lowerCamelCase_						:   Optional[int]		= AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""					)
   lowerCamelCase_						:   Tuple		= image_size
   lowerCamelCase_						:   Optional[Any]		= torch.load(A__						,						map_location=torch.device("""cpu"""					)					)

   lowerCamelCase_						:   List[Any]		= OrderedDict()
   lowerCamelCase_						:   Union[str, Any]		= []

   for idx in range(len(config.depth					)					):
      if config.cls_token[idx]:
         lowerCamelCase_						:   List[Any]		= list_of_state_dict + cls_token(A__					)
      lowerCamelCase_						:   Any		= list_of_state_dict + embeddings(A__					)
      for cnt in range(config.depth[idx]					):
         lowerCamelCase_						:   Union[str, Any]		= list_of_state_dict + attention(A__						,						A__					)

   lowerCamelCase_						:   Optional[Any]		= list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(A__					)
   for i in range(len(A__					)					):
      lowerCamelCase_						:   str		= 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__":
      snake_case__				:	List[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.'
      )

      snake_case__				:	Tuple					       =					parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case__				:	List[Any]					       =					'\nimport os\n'

snake_case__				:	List[str]					       =					'\ndef foo():\n    import os\n    return False\n'

snake_case__				:	List[Any]					       =					'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case__				:	List[str]					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case__				:	Tuple					       =					'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case__				:	str					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case__				:	Any					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Union[str, Any]					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case__				:	Any					       =					[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""						,						A__					)
def    __lowerCamelCase   (      A__						: Dict						,						A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   List[str]		= os.path.join(A__						,						"""test_file.py"""					)
   with open(A__						,						"""w"""					) as _tmp_file:
      _tmp_file.write(A__					)

   lowerCamelCase_						:   str		= get_imports(A__					)
   assert parsed_imports == ["os"]





