



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_		 =					logging.get_logger(__name__)

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

   return new_state_dict

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:Any				)  ->     str:
   # 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      =       state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
         _A      =       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      =       kv_weight[
             : config.hidden_sizes[i], :
         ]
         _A      =       kv_bias[: config.hidden_sizes[i]]
         _A      =       kv_weight[
             config.hidden_sizes[i] :, :
         ]
         _A      =       kv_bias[config.hidden_sizes[i] :]

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Optional[Any]:
   _A      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A      =       Image.open(requests.get(_snake_case	,   stream=_snake_case				).raw				)

   return image

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:Any	,   _snake_case	:str=False	,   _snake_case	:Tuple=None				)  ->     Any:
   _A      =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]	,   decoder_hidden_size=64	,   depths=[3, 8, 27, 3]				)

   # load image processor (only resize + rescale)
   _A      =       GLPNImageProcessor()

   # prepare image
   _A      =       prepare_img()
   _A      =       image_processor(images=_snake_case	,   return_tensors='''pt'''				).pixel_values

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

   # load original state dict
   _A      =       torch.load(_snake_case	,   map_location=torch.device('''cpu'''				)				)

   # rename keys
   _A      =       rename_keys(_snake_case				)

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

   # create HuggingFace model and load state dict
   _A      =       GLPNForDepthEstimation(_snake_case				)
   model.load_state_dict(_snake_case				)
   model.eval()

   # forward pass
   _A      =       model(_snake_case				)
   _A      =       outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         _A      =       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      =       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      =       torch.Size([1, 480, 640]				)

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3]	,   _snake_case	,   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(_snake_case	,   _snake_case				)	,   organization='''nielsr'''	,   commit_message='''Add model'''	,   use_temp_dir=_snake_case	,   )
      image_processor.push_to_hub(
          repo_path_or_name=Path(_snake_case	,   _snake_case				)	,   organization='''nielsr'''	,   commit_message='''Add image processor'''	,   use_temp_dir=_snake_case	,   )


if __name__ == "__main__":
   UpperCAmelCase_		 =					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_		 =					parser.parse_args()
   convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








import itertools
import math

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5	,   int(math.sqrt(_snake_case				) + 1				)	,   6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       2
   while True:
      if is_prime(_snake_case				):
         yield num
      num += 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 10_001				)  ->     int:
   return next(itertools.islice(prime_generator()	,   nth - 1	,   _snake_case				)				)


if __name__ == "__main__":
   print(f'{solution() = }')



