

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 							UpperCAmelCase_  (		_A							):





     '''simple docstring'''




     SCREAMING_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 							UpperCAmelCase_  (		_A ,						_A							):





     '''simple docstring'''




     SCREAMING_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 							UpperCAmelCase_  (		_A							):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__       =       []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')							)
     return token
def 							UpperCAmelCase_  (		):





     '''simple docstring'''




     SCREAMING_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 							UpperCAmelCase_  (		_A ,						_A ,						_A ,						_A							):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__       =       '''imagenet-1k-id2label.json'''
     SCREAMING_SNAKE_CASE__       =       10_00

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

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

     SCREAMING_SNAKE_CASE__       =       CvtConfig(num_labels=lowercase_ ,						idalabel=lowercase_ ,						labelaid=lowercase_							)

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

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

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          SCREAMING_SNAKE_CASE__       =       [2, 2, 20]
          SCREAMING_SNAKE_CASE__       =       [3, 12, 16]
          SCREAMING_SNAKE_CASE__       =       [1_92, 7_68, 10_24]

     SCREAMING_SNAKE_CASE__       =       CvtForImageClassification(lowercase_							)
     SCREAMING_SNAKE_CASE__       =       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							)
     SCREAMING_SNAKE_CASE__       =       image_size
     SCREAMING_SNAKE_CASE__       =       torch.load(lowercase_ ,						map_location=torch.device('''cpu'''							)							)

     SCREAMING_SNAKE_CASE__       =       OrderedDict()
     SCREAMING_SNAKE_CASE__       =       []

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

     SCREAMING_SNAKE_CASE__       =       list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(lowercase_							)
     for i in range(len(lowercase_							)							):
          SCREAMING_SNAKE_CASE__       =       original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(lowercase_							)
     model.save_pretrained(lowercase_							)
     image_processor.save_pretrained(lowercase_							)


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

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE	:    List[str]       =     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	:    Optional[int]       =     parser.parse_args()
       convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






import unittest

from knapsack import greedy_knapsack as kp




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
							__UpperCAmelCase			:							str	    =	[2, 4, 6, 8, 1_0, 1_2]
							__UpperCAmelCase			:							List[Any]	    =	1_0_0
							self.assertEqual(kp.calc_profit(lowercase__				,     lowercase__				,     lowercase__)				,     2_1_0)


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Weight can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Profit can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(
							    lowercase__				,     '''The length of profit and weight must be same.''')


if __name__ == "__main__":
				unittest.main()

