


'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 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      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Dict	):
 for key in orig_state_dict.copy().keys():
  lowerCamelCase_  				=						orig_state_dict.pop(UpperCAmelCase_	)

  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      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		List[Any]	):
 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(UpperCAmelCase_	)

 lowerCamelCase_  				=						torch.hub.load_state_dict_from_url(UpperCAmelCase_      ,       map_location="cpu"	)["model"]

 lowerCamelCase_  				=						ViTMAEImageProcessor(size=config.image_size	)

 lowerCamelCase_  				=						convert_state_dict(UpperCAmelCase_      ,       UpperCAmelCase_	)

 model.load_state_dict(UpperCAmelCase_	)
 model.eval()

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

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

 # forward pass
 torch.manual_seed(2	)
 lowerCamelCase_  				=						model(**UpperCAmelCase_	)
 lowerCamelCase_  				=						outputs.logits

 if "large" in checkpoint_url:
  lowerCamelCase_  				=						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:
  lowerCamelCase_  				=						torch.tensor(
      [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]	)
 else:
  lowerCamelCase_  				=						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]      ,       UpperCAmelCase_      ,       atol=1E-4	)

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

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


if __name__ == "__main__":
     a_						:       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."""
     )

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







'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DetrImageProcessor





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=7    ,						UpperCamelCase=3    ,						UpperCamelCase=30    ,						UpperCamelCase=400    ,						UpperCamelCase=True    ,						UpperCamelCase=None    ,						UpperCamelCase=True    ,						UpperCamelCase=1 / 255    ,						UpperCamelCase=True    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   lowerCamelCase_  				=						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						min_resolution
   lowerCamelCase_  				=						max_resolution
   lowerCamelCase_  				=						do_resize
   lowerCamelCase_  				=						size
   lowerCamelCase_  				=						do_rescale
   lowerCamelCase_  				=						rescale_factor
   lowerCamelCase_  				=						do_normalize
   lowerCamelCase_  				=						image_mean
   lowerCamelCase_  				=						image_std
   lowerCamelCase_  				=						do_pad






  def 						snake_case	(					self  ):







   """simple docstring"""
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_pad": self.do_pad,
   }







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   if not batched:
    lowerCamelCase_  				=						image_inputs[0]
    if isinstance(UpperCamelCase    ,						Image.Image  ):
     lowerCamelCase_				,lowerCamelCase_  				=						image.size
    else:
     lowerCamelCase_				,lowerCamelCase_  				=						image.shape[1], image.shape[2]
    if w < h:
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * h / w  )
     lowerCamelCase_  				=						self.size["shortest_edge"]
    elif w > h:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * w / h  )
    else:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						self.size["shortest_edge"]

   else:
    lowerCamelCase_  				=						[]
    for image in image_inputs:
     lowerCamelCase_				,lowerCamelCase_  				=						self.get_expected_values([image]  )
     expected_values.append((expected_height, expected_width)  )
    lowerCamelCase_  				=						max(UpperCamelCase    ,						key=lambda UpperCamelCase  : item[0]  )[0]
    lowerCamelCase_  				=						max(UpperCamelCase    ,						key=lambda UpperCamelCase  : item[1]  )[1]

   return expected_height, expected_width





@require_torch
@require_vision
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						DetrImageProcessor if is_vision_available() else None






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						DetrImageProcessingTester(self  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_mean"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_std"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_normalize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_rescale"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"rescale_factor"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_resize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"size"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_pad"  )  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class.from_dict(self.image_processor_dict  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 18, "longest_edge": 1333}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )

   lowerCamelCase_  				=						self.image_processing_class.from_dict(
       self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=UpperCamelCase  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 42, "longest_edge": 84}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PIL images
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						Image.Image  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values
   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random numpy tensors
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						numpify=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						np.ndarray  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PyTorch tensors
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						torchify=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						torch.Tensor  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image and target
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"image_id": 3_9769, "annotations": target}

   # encode them
   lowerCamelCase_  				=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"  )
   lowerCamelCase_  				=						image_processing(images=UpperCamelCase    ,						annotations=UpperCamelCase    ,						return_tensors="pt"  )

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([75, 75, 63, 65, 17, 17]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image, target and masks_path
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

   lowerCamelCase_  				=						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"  )

   # encode them
   lowerCamelCase_  				=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"  )
   lowerCamelCase_  				=						image_processing(images=UpperCamelCase    ,						annotations=UpperCamelCase    ,						masks_path=UpperCamelCase    ,						return_tensors="pt"  )

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([17, 17, 63, 75, 75, 93]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify masks
   lowerCamelCase_  				=						82_2873
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,						UpperCamelCase  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )



