'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def snake_case_      (					__snake_case  :							Dict)			->					Dict:
   if "cls_token" in name:
      lowerCAmelCase_			=      name.replace('''cls_token'''	,   '''vit.embeddings.cls_token''')
   if "mask_token" in name:
      lowerCAmelCase_			=      name.replace('''mask_token'''	,   '''decoder.mask_token''')
   if "decoder_pos_embed" in name:
      lowerCAmelCase_			=      name.replace('''decoder_pos_embed'''	,   '''decoder.decoder_pos_embed''')
   if "pos_embed" in name and "decoder" not in name:
      lowerCAmelCase_			=      name.replace('''pos_embed'''	,   '''vit.embeddings.position_embeddings''')
   if "patch_embed.proj" in name:
      lowerCAmelCase_			=      name.replace('''patch_embed.proj'''	,   '''vit.embeddings.patch_embeddings.projection''')
   if "patch_embed.norm" in name:
      lowerCAmelCase_			=      name.replace('''patch_embed.norm'''	,   '''vit.embeddings.norm''')
   if "decoder_blocks" in name:
      lowerCAmelCase_			=      name.replace('''decoder_blocks'''	,   '''decoder.decoder_layers''')
   if "blocks" in name:
      lowerCAmelCase_			=      name.replace('''blocks'''	,   '''vit.encoder.layer''')
   if "attn.proj" in name:
      lowerCAmelCase_			=      name.replace('''attn.proj'''	,   '''attention.output.dense''')
   if "attn" in name:
      lowerCAmelCase_			=      name.replace('''attn'''	,   '''attention.self''')
   if "norm1" in name:
      lowerCAmelCase_			=      name.replace('''norm1'''	,   '''layernorm_before''')
   if "norm2" in name:
      lowerCAmelCase_			=      name.replace('''norm2'''	,   '''layernorm_after''')
   if "mlp.fc1" in name:
      lowerCAmelCase_			=      name.replace('''mlp.fc1'''	,   '''intermediate.dense''')
   if "mlp.fc2" in name:
      lowerCAmelCase_			=      name.replace('''mlp.fc2'''	,   '''output.dense''')
   if "decoder_embed" in name:
      lowerCAmelCase_			=      name.replace('''decoder_embed'''	,   '''decoder.decoder_embed''')
   if "decoder_norm" in name:
      lowerCAmelCase_			=      name.replace('''decoder_norm'''	,   '''decoder.decoder_norm''')
   if "decoder_pred" in name:
      lowerCAmelCase_			=      name.replace('''decoder_pred'''	,   '''decoder.decoder_pred''')
   if "norm.weight" in name and "decoder" not in name:
      lowerCAmelCase_			=      name.replace('''norm.weight'''	,   '''vit.layernorm.weight''')
   if "norm.bias" in name and "decoder" not in name:
      lowerCAmelCase_			=      name.replace('''norm.bias'''	,   '''vit.layernorm.bias''')

   return name



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							Optional[Any])			->					int:
   for key in orig_state_dict.copy().keys():
      lowerCAmelCase_			=      orig_state_dict.pop(lowercase__)

      if "qkv" in key:
         lowerCAmelCase_			=      key.split('''.''')
         lowerCAmelCase_			=      int(key_split[1])
         if "decoder_blocks" in key:
            lowerCAmelCase_			=      config.decoder_hidden_size
            lowerCAmelCase_			=      "decoder.decoder_layers."
            if "weight" in key:
               lowerCAmelCase_			=      val[:dim, :]
               lowerCAmelCase_			=      val[dim : dim * 2, :]
               lowerCAmelCase_			=      val[-dim:, :]
            elif "bias" in key:
               lowerCAmelCase_			=      val[:dim]
               lowerCAmelCase_			=      val[dim : dim * 2]
               lowerCAmelCase_			=      val[-dim:]
         else:
            lowerCAmelCase_			=      config.hidden_size
            lowerCAmelCase_			=      "vit.encoder.layer."
            if "weight" in key:
               lowerCAmelCase_			=      val[:dim, :]
               lowerCAmelCase_			=      val[dim : dim * 2, :]
               lowerCAmelCase_			=      val[-dim:, :]
            elif "bias" in key:
               lowerCAmelCase_			=      val[:dim]
               lowerCAmelCase_			=      val[dim : dim * 2]
               lowerCAmelCase_			=      val[-dim:]

      else:
         lowerCAmelCase_			=      val

   return orig_state_dict



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Optional[int])			->					Optional[int]:
   lowerCAmelCase_			=      ViTMAEConfig()
   if "large" in checkpoint_url:
      lowerCAmelCase_			=      1024
      lowerCAmelCase_			=      4096
      lowerCAmelCase_			=      24
      lowerCAmelCase_			=      16
   elif "huge" in checkpoint_url:
      lowerCAmelCase_			=      14
      lowerCAmelCase_			=      1280
      lowerCAmelCase_			=      5120
      lowerCAmelCase_			=      32
      lowerCAmelCase_			=      16

   lowerCAmelCase_			=      ViTMAEForPreTraining(lowercase__)

   lowerCAmelCase_			=      torch.hub.load_state_dict_from_url(lowercase__	,   map_location='''cpu''')["model"]

   lowerCAmelCase_			=      ViTMAEImageProcessor(size=config.image_size)

   lowerCAmelCase_			=      convert_state_dict(lowercase__	,   lowercase__)

   model.load_state_dict(lowercase__)
   model.eval()

   lowerCAmelCase_			=      "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

   lowerCAmelCase_			=      Image.open(requests.get(lowercase__	,   stream=lowercase__).raw)
   lowerCAmelCase_			=      ViTMAEImageProcessor(size=config.image_size)
   lowerCAmelCase_			=      image_processor(images=lowercase__	,   return_tensors='''pt''')

   # forward pass
   torch.manual_seed(2)
   lowerCAmelCase_			=      model(**lowercase__)
   lowerCAmelCase_			=      outputs.logits

   if "large" in checkpoint_url:
      lowerCAmelCase_			=      torch.tensor(
          [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]])
   elif "huge" in checkpoint_url:
      lowerCAmelCase_			=      torch.tensor(
          [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]])
   else:
      lowerCAmelCase_			=      torch.tensor(
          [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]])

   # verify logits
   assert torch.allclose(logits[0, :3, :3]	,   lowercase__	,   atol=1E-4)

   print(F'''Saving model to {pytorch_dump_folder_path}''')
   model.save_pretrained(lowercase__)

   print(F'''Saving image processor to {pytorch_dump_folder_path}''')
   image_processor.save_pretrained(lowercase__)


if __name__ == "__main__":
 A_     :						Dict    							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
     type=str,
     help='''URL of the checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 A_     :						List[Any]    							=parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE     =       {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""DeiTFeatureExtractor"""]
      SCREAMING_SNAKE_CASE     =       ["""DeiTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       [
          """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DeiTForImageClassification""",
          """DeiTForImageClassificationWithTeacher""",
          """DeiTForMaskedImageModeling""",
          """DeiTModel""",
          """DeiTPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       [
          """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFDeiTForImageClassification""",
          """TFDeiTForImageClassificationWithTeacher""",
          """TFDeiTForMaskedImageModeling""",
          """TFDeiTModel""",
          """TFDeiTPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_deit import DeiTFeatureExtractor
            from .image_processing_deit import DeiTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deit import (
                DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeiTForImageClassification,
                DeiTForImageClassificationWithTeacher,
                DeiTForMaskedImageModeling,
                DeiTModel,
                DeiTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deit import (
                TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDeiTForImageClassification,
                TFDeiTForImageClassificationWithTeacher,
                TFDeiTForMaskedImageModeling,
                TFDeiTModel,
                TFDeiTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



