




"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





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

     return name





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     for key in orig_state_dict.copy().keys():
          __snake_case				=							orig_state_dict.pop(a_)

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

          else:
               __snake_case				=							val

     return orig_state_dict





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							ViTMAEConfig()
     if "large" in checkpoint_url:
          __snake_case				=							10_24
          __snake_case				=							40_96
          __snake_case				=							24
          __snake_case				=							16
     elif "huge" in checkpoint_url:
          __snake_case				=							14
          __snake_case				=							12_80
          __snake_case				=							51_20
          __snake_case				=							32
          __snake_case				=							16

     __snake_case				=							ViTMAEForPreTraining(a_)

     __snake_case				=							torch.hub.load_state_dict_from_url(a_,      map_location='''cpu''')['''model''']

     __snake_case				=							ViTMAEImageProcessor(size=config.image_size)

     __snake_case				=							convert_state_dict(a_,      a_)

     model.load_state_dict(a_)
     model.eval()

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

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

     # forward pass
     torch.manual_seed(2)
     __snake_case				=							model(**a_)
     __snake_case				=							outputs.logits

     if "large" in checkpoint_url:
          __snake_case				=							torch.tensor(
              [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]])
     elif "huge" in checkpoint_url:
          __snake_case				=							torch.tensor(
              [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]])
     else:
          __snake_case				=							torch.tensor(
              [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]])

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

     print(f"Saving model to {pytorch_dump_folder_path}")
     model.save_pretrained(a_)

     print(f"Saving image processor to {pytorch_dump_folder_path}")
     image_processor.save_pretrained(a_)


if __name__ == "__main__":
       __lowercase		:		List[Any]														=							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."
       )

       __lowercase		:		Optional[int]														=							parser.parse_args()
       convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
"""simple docstring"""



from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

         __snake_case				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							make_list_of_images(A_    )

         if not valid_images(A_    ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

         if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.'''    )

         if do_center_crop and crop_size is None:
              raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

         if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

         # All transformations expect numpy arrays.
         __snake_case				=							[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

         if do_center_crop:
              __snake_case				=							[self.center_crop(A_	,      A_    ) for image in images]

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )