
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_      (       A__		)							->							int:
    """simple docstring"""

    snake_case								= []
    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_      (       A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case								= []
    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_      (       A__		)							->							Dict:
    """simple docstring"""

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



def 						lowercase_      (       )							->							int:
    """simple docstring"""

    snake_case								= []
    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_      (       A__			,		A__			,		A__			,		A__		)							->							List[str]:
    """simple docstring"""

    snake_case								= "imagenet-1k-id2label.json"
    snake_case								= 1000

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

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

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

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

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

    snake_case								= CvtForImageClassification(A__		)
    snake_case								= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
    snake_case								= image_size
    snake_case								= torch.load(A__			,		map_location=torch.device("cpu"		)		)

    snake_case								= OrderedDict()
    snake_case								= []

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

    snake_case								= list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(A__		)
    for i in range(len(A__		)		):
        snake_case								= 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__":
   _A   =			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=3_84,
       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   =			parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Tuple       )						->						List[str]:
         snake_case								= ["a", "b", "c"]

         # Defaults to last layer if both are None
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   _A    ,   _A       )
         self.assertEqual(_A    ,   ["c"]       )
         self.assertEqual(_A    ,   [2]       )

         # Out indices set to match out features
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(["a", "c"]    ,   _A    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [0, 2]       )

         # Out features set to match out indices
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   [0, 2]    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [0, 2]       )

         # Out features selected from negative indices
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   [-3, -1]    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [-3, -1]       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         # Stage names must be set
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   _A       )

         # Out features must be a list
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(("a", "b")    ,   (0, 1)    ,   ["a", "b"]       )

         # Out features must be a subset of stage names
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   ["a"]       )

         # Out indices must be a list or tuple
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(_A    ,   0    ,   ["a", "b"]       )

         # Out indices must be a subset of stage names
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(_A    ,   (0, 1)    ,   ["a"]       )

         # Out features and out indices must be the same length
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0,)    ,   ["a", "b", "c"]       )

         # Out features should match out indices
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 2)    ,   ["a", "b", "c"]       )

         # Out features and out indices should be in order
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["b", "a"]    ,   (0, 1)    ,   ["a", "b"]       )

         # Check passes with valid inputs
         verify_out_features_out_indices(["a", "b", "d"]    ,   (0, 1, -1)    ,   ["a", "b", "c", "d"]       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						str:
         snake_case								= BackboneMixin()

         snake_case								= ["a", "b", "c"]
         snake_case								= ["a", "c"]
         snake_case								= [0, 2]

         # Check that the output features and indices are set correctly
         self.assertEqual(backbone.out_features    ,   ["a", "c"]       )
         self.assertEqual(backbone.out_indices    ,   [0, 2]       )

         # Check out features and indices are updated correctly
         snake_case								= ["a", "b"]
         self.assertEqual(backbone.out_features    ,   ["a", "b"]       )
         self.assertEqual(backbone.out_indices    ,   [0, 1]       )

         snake_case								= [-3, -1]
         self.assertEqual(backbone.out_features    ,   ["a", "c"]       )
         self.assertEqual(backbone.out_indices    ,   [-3, -1]       )

