

'''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 						__lowerCamelCase       (   __snake_case     :		List[Any]       )       ->							Optional[int]:






						"""simple docstring"""





						A__     :   int      =[]
						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       (   __snake_case     :		List[Any],		__snake_case     :		Optional[int]       )       ->							Dict:






						"""simple docstring"""





						A__     :   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 						__lowerCamelCase       (   __snake_case     :		Any       )       ->							List[Any]:






						"""simple docstring"""





						A__     :   Any      =[]
						token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")       )
						return token
def 						__lowerCamelCase       (   )       ->							Optional[int]:






						"""simple docstring"""





						A__     :   Optional[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       (   __snake_case     :		Tuple,		__snake_case     :		Optional[int],		__snake_case     :		List[Any],		__snake_case     :		Union[str, Any]       )       ->							Optional[int]:






						"""simple docstring"""





						A__     :   Any      ="""imagenet-1k-id2label.json"""
						A__     :   Dict      =1_000

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

						A__     :   str      =idalabel
						A__     :   List[str]      ={v: k for k, v in idalabel.items()}

						A__     :   Dict      =CvtConfig(num_labels=__snake_case,		idalabel=__snake_case,		labelaid=__snake_case       )

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

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

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												A__     :   Optional[int]      =[2, 2, 20]
												A__     :   str      =[3, 12, 16]
												A__     :   Tuple      =[192, 768, 1_024]

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

						A__     :   List[str]      =OrderedDict()
						A__     :   Optional[Any]      =[]

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

						A__     :   List[str]      =list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__snake_case       )
						for i in range(len(__snake_case       )       ):
												A__     :   Optional[int]      =original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(__snake_case       )
						model.save_pretrained(__snake_case       )
						image_processor.save_pretrained(__snake_case       )


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

if __name__ == "__main__":
		__snake_case :	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 :	Union[str, Any]      						=			parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









from __future__ import annotations

import time

import numpy as np

UpperCAmelCase__				   =		[8, 5, 9, 7]
UpperCAmelCase__				   =		[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCAmelCase__				   =		[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						list[int] ,					__UpperCAmelCase      :						list[list[int]] ,					__UpperCAmelCase      :						list[list[int]] ,					)    ->None:




       """simple docstring"""


       a		  =      claim_vector
       a		  =      allocated_resources_table
       a		  =      maximum_claim_table







      def       __lowerCAmelCase					(      self      :						Any  )    ->list[int]:




       """simple docstring"""


       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table  )
           for i in range(len(self.__allocated_resources_table[0]  )  )
       ]







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->list[int]:




       """simple docstring"""


       return np.array(self.__claim_vector  ) - np.array(
           self.__processes_resource_summation()  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->list[list[int]]:




       """simple docstring"""


       return [
           list(np.array(self.__maximum_claim_table[i]  ) - np.array(__UpperCAmelCase  )  )
           for i, allocated_resource in enumerate(self.__allocated_resources_table  )
       ]







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->dict[int, list[int]]:




       """simple docstring"""


       return {self.__need().index(__UpperCAmelCase  ): i for i in self.__need()}







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					**__UpperCAmelCase      :						Any  )    ->None:




       """simple docstring"""


       a		  =      self.__need()
       a		  =      self.__allocated_resources_table
       a		  =      self.__available_resources()
       a		  =      self.__need_index_manager()
       for kw, val in kwargs.items():
        if kw and val is True:
         self.__pretty_data()
       print('''_''' * 50 + '''\n'''  )
       while need_list:
        a		  =      False
        for each_need in need_list:
         a		  =      True
         for index, need in enumerate(__UpperCAmelCase  ):
          if need > available_resources[index]:
           a		  =      False
           break
         if execution:
          a		  =      True
          # get the original index of the process from ind_ctrl db
          for original_need_index, need_clone in need_index_manager.items():
           if each_need == need_clone:
            a		  =      original_need_index
          print(F"""Process {process_number + 1} is executing."""  )
          # remove the process run from stack
          need_list.remove(__UpperCAmelCase  )
          # update available/freed resources stack
          a		  =      np.array(__UpperCAmelCase  ) + np.array(
              alloc_resources_table[process_number]  )
          print(
              '''Updated available resource stack for processes: '''
              + ''' '''.join([str(__UpperCAmelCase  ) for x in available_resources]  )  )
          break
        if safe:
         print('''The process is in a safe state.\n'''  )
        else:
         print('''System in unsafe state. Aborting...\n'''  )
         break







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Dict:




       """simple docstring"""


       print(''' ''' * 9 + '''Allocated Resource Table'''  )
       for item in self.__allocated_resources_table:
        print(
            F"""P{self.__allocated_resources_table.index(__UpperCAmelCase  ) + 1}"""
            + ''' '''.join(F"""{it:>8}""" for it in item  )
            + '''\n'''  )
       print(''' ''' * 9 + '''System Resource Table'''  )
       for item in self.__maximum_claim_table:
        print(
            F"""P{self.__maximum_claim_table.index(__UpperCAmelCase  ) + 1}"""
            + ''' '''.join(F"""{it:>8}""" for it in item  )
            + '''\n'''  )
       print(
           '''Current Usage by Active Processes: '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__claim_vector  )  )
       print(
           '''Initial Available Resources:       '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__available_resources()  )  )
       time.sleep(1  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


