


'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Optional[int]								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		Any       =			OrderedDict()
 for key, value in state_dict.items():
  if key.startswith('module.encoder'			):
   __A  :		Any       =			key.replace('module.encoder',				'glpn.encoder'			)
  if key.startswith('module.decoder'			):
   __A  :		Union[str, Any]       =			key.replace('module.decoder',				'decoder.stages'			)
  if "patch_embed" in key:
   # replace for example patch_embed1 by patch_embeddings.0
   __A  :		List[str]       =			key[key.find('patch_embed'			) + len('patch_embed'			)]
   __A  :		Any       =			key.replace(f"""patch_embed{idx}""",				f"""patch_embeddings.{int(UpperCamelCase__			)-1}"""			)
  if "norm" in key:
   __A  :		Tuple       =			key.replace('norm',				'layer_norm'			)
  if "glpn.encoder.layer_norm" in key:
   # replace for example layer_norm1 by layer_norm.0
   __A  :		Optional[int]       =			key[key.find('glpn.encoder.layer_norm'			) + len('glpn.encoder.layer_norm'			)]
   __A  :		Optional[int]       =			key.replace(f"""layer_norm{idx}""",				f"""layer_norm.{int(UpperCamelCase__			)-1}"""			)
  if "layer_norm1" in key:
   __A  :		Optional[int]       =			key.replace('layer_norm1',				'layer_norm_1'			)
  if "layer_norm2" in key:
   __A  :		Optional[Any]       =			key.replace('layer_norm2',				'layer_norm_2'			)
  if "block" in key:
   # replace for example block1 by block.0
   __A  :		str       =			key[key.find('block'			) + len('block'			)]
   __A  :		Optional[int]       =			key.replace(f"""block{idx}""",				f"""block.{int(UpperCamelCase__			)-1}"""			)
  if "attn.q" in key:
   __A  :		str       =			key.replace('attn.q',				'attention.self.query'			)
  if "attn.proj" in key:
   __A  :		Dict       =			key.replace('attn.proj',				'attention.output.dense'			)
  if "attn" in key:
   __A  :		Any       =			key.replace('attn',				'attention.self'			)
  if "fc1" in key:
   __A  :		Dict       =			key.replace('fc1',				'dense1'			)
  if "fc2" in key:
   __A  :		Tuple       =			key.replace('fc2',				'dense2'			)
  if "linear_pred" in key:
   __A  :		Union[str, Any]       =			key.replace('linear_pred',				'classifier'			)
  if "linear_fuse" in key:
   __A  :		Optional[int]       =			key.replace('linear_fuse.conv',				'linear_fuse'			)
   __A  :		List[str]       =			key.replace('linear_fuse.bn',				'batch_norm'			)
  if "linear_c" in key:
   # replace for example linear_c4 by linear_c.3
   __A  :		int       =			key[key.find('linear_c'			) + len('linear_c'			)]
   __A  :		Optional[int]       =			key.replace(f"""linear_c{idx}""",				f"""linear_c.{int(UpperCamelCase__			)-1}"""			)
  if "bot_conv" in key:
   __A  :		Dict       =			key.replace('bot_conv',				'0.convolution'			)
  if "skip_conv1" in key:
   __A  :		List[Any]       =			key.replace('skip_conv1',				'1.convolution'			)
  if "skip_conv2" in key:
   __A  :		Optional[int]       =			key.replace('skip_conv2',				'2.convolution'			)
  if "fusion1" in key:
   __A  :		Tuple       =			key.replace('fusion1',				'1.fusion'			)
  if "fusion2" in key:
   __A  :		Optional[int]       =			key.replace('fusion2',				'2.fusion'			)
  if "fusion3" in key:
   __A  :		Optional[int]       =			key.replace('fusion3',				'3.fusion'			)
  if "fusion" in key and "conv" in key:
   __A  :		List[str]       =			key.replace('conv',				'convolutional_layer'			)
  if key.startswith('module.last_layer_depth'			):
   __A  :		Optional[Any]       =			key.replace('module.last_layer_depth',				'head.head'			)
  __A  :		Dict       =			value

 return new_state_dict


def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					List[Any]			):
 # for each of the encoder blocks:
 for i in range(config.num_encoder_blocks			):
  for j in range(config.depths[i]			):
   # read in weights + bias of keys and values (which is a single matrix in the original implementation)
   __A  :		Union[str, Any]       =			state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""			)
   __A  :		Optional[int]       =			state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""			)
   # next, add keys and values (in that order) to the state dict
   __A  :		List[Any]       =			kv_weight[
       : config.hidden_sizes[i], :
   ]
   __A  :		Any       =			kv_bias[: config.hidden_sizes[i]]
   __A  :		int       =			kv_weight[
       config.hidden_sizes[i] :, :
   ]
   __A  :		str       =			kv_bias[config.hidden_sizes[i] :]


def 			_lowercase       (				):
 __A  :		Tuple       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		str       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)

 return image


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Dict=False,				UpperCamelCase__       :					int=None			):
 __A  :		Any       =			GLPNConfig(hidden_sizes=[64, 128, 320, 512],				decoder_hidden_size=64,				depths=[3, 8, 27, 3]			)

 # load image processor (only resize + rescale)
 __A  :		Union[str, Any]       =			GLPNImageProcessor()

 # prepare image
 __A  :		List[Any]       =			prepare_img()
 __A  :		Any       =			image_processor(images=UpperCamelCase__,				return_tensors='pt'			).pixel_values

 logger.info('Converting model...'			)

 # load original state dict
 __A  :		Tuple       =			torch.load(UpperCamelCase__,				map_location=torch.device('cpu'			)			)

 # rename keys
 __A  :		Optional[int]       =			rename_keys(UpperCamelCase__			)

 # key and value matrices need special treatment
 read_in_k_v(UpperCamelCase__,				UpperCamelCase__			)

 # create HuggingFace model and load state dict
 __A  :		Optional[Any]       =			GLPNForDepthEstimation(UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)
 model.eval()

 # forward pass
 __A  :		Optional[Any]       =			model(UpperCamelCase__			)
 __A  :		Union[str, Any]       =			outputs.predicted_depth

 # verify output
 if model_name is not None:
  if "nyu" in model_name:
   __A  :		Optional[Any]       =			torch.tensor(
       [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]			)
  elif "kitti" in model_name:
   __A  :		Dict       =			torch.tensor(
       [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]			)
  else:
   raise ValueError(f"""Unknown model name: {model_name}"""			)

  __A  :		Any       =			torch.Size([1, 480, 640]			)

  assert predicted_depth.shape == expected_shape
  assert torch.allclose(predicted_depth[0, :3, :3],				UpperCamelCase__,				atol=1E-4			)
  print('Looks ok!'			)

 # finally, push to hub if required
 if push_to_hub:
  logger.info('Pushing model and image processor to the hub...'			)
  model.push_to_hub(
      repo_path_or_name=Path(UpperCamelCase__,				UpperCamelCase__			),				organization='nielsr',				commit_message='Add model',				use_temp_dir=UpperCamelCase__,				)
  image_processor.push_to_hub(
      repo_path_or_name=Path(UpperCamelCase__,				UpperCamelCase__			),				organization='nielsr',				commit_message='Add image processor',				use_temp_dir=UpperCamelCase__,				)


if __name__ == "__main__":
 UpperCAmelCase_ :     List[str]								=      argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_path',
     default=None,
     type=str,
     help='Path to the original PyTorch checkpoint (.pth file).',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
 )
 parser.add_argument(
     '--model_name',
     default='glpn-kitti',
     type=str,
     help='Name of the model in case you\'re pushing to the hub.',
 )
 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










'''simple docstring'''
import unittest

from transformers import DonutProcessor


UpperCAmelCase_ :     str								=      'naver-clova-ix/donut-base'

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			DonutProcessor.from_pretrained(__lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{
      'name': 'John Doe',
      'age': '99',
      'city': 'Atlanta',
      'state': 'GA',
      'zip': '30301',
      'phone': '123-4567',
      'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
  }

  __A  :		Optional[Any]       =			(
      '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
      '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
      '<s_nicknames><s_nickname>Johnny</s_nickname>'
      '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
  )
  __A  :		List[Any]       =			self.processor.tokenajson(__lowercase						)

  self.assertDictEqual(__lowercase      ,      __lowercase						)






