





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   (      _SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''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    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   List[Any]:


      '''simple docstring'''


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



def    __lowercase   (      ) ->   List[Any]:


      '''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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''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(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	)	) ,						"""r"""	)	)
      SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): 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		=						SCREAMING_SNAKE_CASE		=						CvtConfig(num_labels=_SCREAMING_SNAKE_CASE ,						idalabel=_SCREAMING_SNAKE_CASE ,						labelaid=_SCREAMING_SNAKE_CASE	)

      # 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(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""	)
      SCREAMING_SNAKE_CASE		=						image_size
      SCREAMING_SNAKE_CASE		=						torch.load(_SCREAMING_SNAKE_CASE ,						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(_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE		=						list_of_state_dict + embeddings(_SCREAMING_SNAKE_CASE	)
            for cnt in range(config.depth[idx]	):
                  SCREAMING_SNAKE_CASE		=						list_of_state_dict + attention(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

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

      model.load_state_dict(_SCREAMING_SNAKE_CASE	)
      model.save_pretrained(_SCREAMING_SNAKE_CASE	)
      image_processor.save_pretrained(_SCREAMING_SNAKE_CASE	)


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

if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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_8_4,
          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_						   =       parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









import math
import unittest



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   bool:


      '''simple docstring'''


      assert isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) and (
          number >= 0
      ), "'number' must been an int and positive"

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5 ,						int(math.sqrt(_SCREAMING_SNAKE_CASE	) + 1	) ,						6	):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Tuple:




           '''simple docstring'''


           self.assertTrue(is_prime(2       )       )
           self.assertTrue(is_prime(3       )       )
           self.assertTrue(is_prime(5       )       )
           self.assertTrue(is_prime(7       )       )
           self.assertTrue(is_prime(11       )       )
           self.assertTrue(is_prime(13       )       )
           self.assertTrue(is_prime(17       )       )
           self.assertTrue(is_prime(19       )       )
           self.assertTrue(is_prime(23       )       )
           self.assertTrue(is_prime(29       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           with self.assertRaises(lowerCamelCase__       ):
                 is_prime(-19       )
           self.assertFalse(
               is_prime(0       )						,"""Zero doesn't have any positive factors, primes must have exactly two."""						,)
           self.assertFalse(
               is_prime(1       )						,"""One only has 1 positive factor, primes must have exactly two."""						,)
           self.assertFalse(is_prime(2 * 2       )       )
           self.assertFalse(is_prime(2 * 3       )       )
           self.assertFalse(is_prime(3 * 3       )       )
           self.assertFalse(is_prime(3 * 5       )       )
           self.assertFalse(is_prime(3 * 5 * 7       )       )


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


