


'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




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

     return name




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          __a       =   orig_state_dict.pop(__SCREAMING_SNAKE_CASE		)

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

          else:
               __a       =   val

     return orig_state_dict




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					List[Any]:
     """simple docstring"""




     __a       =   ViTMAEConfig()
     if "large" in checkpoint_url:
          __a       =   1024
          __a       =   4096
          __a       =   24
          __a       =   16
     elif "huge" in checkpoint_url:
          __a       =   14
          __a       =   1280
          __a       =   5120
          __a       =   32
          __a       =   16

     __a       =   ViTMAEForPreTraining(__SCREAMING_SNAKE_CASE		)

     __a       =   torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,  map_location="""cpu"""		)["""model"""]

     __a       =   ViTMAEImageProcessor(size=config.image_size		)

     __a       =   convert_state_dict(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     model.load_state_dict(__SCREAMING_SNAKE_CASE		)
     model.eval()

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

     __a       =   Image.open(requests.get(__SCREAMING_SNAKE_CASE				,  stream=__SCREAMING_SNAKE_CASE		).raw		)
     __a       =   ViTMAEImageProcessor(size=config.image_size		)
     __a       =   image_processor(images=__SCREAMING_SNAKE_CASE				,  return_tensors="""pt"""		)

     # forward pass
     torch.manual_seed(2		)
     __a       =   model(**__SCREAMING_SNAKE_CASE		)
     __a       =   outputs.logits

     if "large" in checkpoint_url:
          __a       =   torch.tensor(
              [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]		)
     elif "huge" in checkpoint_url:
          __a       =   torch.tensor(
              [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]		)
     else:
          __a       =   torch.tensor(
              [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]		)

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			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.'
 )

 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''







import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_      =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_      =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_      =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_      =			len(train_data)
SCREAMING_SNAKE_CASE_      =			0.0_0_9




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="train"		)				->					Optional[int]:
     """simple docstring"""




     return calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) - output(
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     __a       =   0
     for i in range(len(__SCREAMING_SNAKE_CASE		) - 1		):
          hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
     hyp_val += parameter_vector[0]
     return hyp_val




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """simple docstring"""




     if data_set == "train":
          return train_data[example_no][1]
     elif data_set == "test":
          return test_data[example_no][1]
     return None




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     if data_set == "train":
          return _hypothesis_value(train_data[example_no][0]		)
     elif data_set == "test":
          return _hypothesis_value(test_data[example_no][0]		)
     return None




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=m		)				->					Optional[Any]:
     """simple docstring"""




     __a       =   0
     for i in range(__SCREAMING_SNAKE_CASE		):
          if index == -1:
               summation_value += _error(__SCREAMING_SNAKE_CASE		)
          else:
               summation_value += _error(__SCREAMING_SNAKE_CASE		) * train_data[i][0][index]
     return summation_value




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   summation_of_cost_derivative(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) / m
     return cost_derivative_value




def        __lowercase						(      )				->					str:
     """simple docstring"""




     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     __a       =   0.000_002
     __a       =   0
     __a       =   0
     while True:
          j += 1
          __a       =   [0, 0, 0, 0]
          for i in range(0				,  len(__SCREAMING_SNAKE_CASE		)		):
               __a       =   get_cost_derivative(i - 1		)
               __a       =   (
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  atol=__SCREAMING_SNAKE_CASE				,  rtol=__SCREAMING_SNAKE_CASE				,  ):
               break
          __a       =   temp_parameter_vector
     print(("""Number of iterations:""", j)		)




def        __lowercase						(      )				->					List[Any]:
     """simple docstring"""




     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          print(("""Actual output value:""", output(__SCREAMING_SNAKE_CASE				,  """test"""		))		)
          print(("""Hypothesis output:""", calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  """test"""		))		)


if __name__ == "__main__":
 run_gradient_descent()
 print('\nTesting gradient descent for a linear hypothesis function.\n')
 test_gradient_descent()






