

'''simple docstring'''






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase__											=	logging.get_logger(__name__)


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = R'''\w+[.]\d+'''
 __lowercase							  = re.findall(A__     ,				A__      )
 for pat in pats:
  __lowercase							  = key.replace(A__     ,				'''_'''.join(pat.split('''.'''      )      )      )
 return key


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = pt_tuple_key[:-1] + ('''scale''',)
 if (
     any('''norm''' in str_ for str_ in pt_tuple_key      )
     and (pt_tuple_key[-1] == "bias")
     and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
     and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
 ):
  __lowercase							  = pt_tuple_key[:-1] + ('''scale''',)
  return renamed_pt_tuple_key, pt_tensor
 elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
  __lowercase							  = pt_tuple_key[:-1] + ('''scale''',)
  return renamed_pt_tuple_key, pt_tensor

 # embedding
 if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
  __lowercase							  = pt_tuple_key[:-1] + ('''embedding''',)
  return renamed_pt_tuple_key, pt_tensor

 # conv layer
 __lowercase							  = pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
  __lowercase							  = pt_tensor.transpose(2     ,				3     ,				1     ,				0      )
  return renamed_pt_tuple_key, pt_tensor

 # linear layer
 __lowercase							  = pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight":
  __lowercase							  = pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm weight
 __lowercase							  = pt_tuple_key[:-1] + ('''weight''',)
 if pt_tuple_key[-1] == "gamma":
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm bias
 __lowercase							  = pt_tuple_key[:-1] + ('''bias''',)
 if pt_tuple_key[-1] == "beta":
  return renamed_pt_tuple_key, pt_tensor

 return pt_tuple_key, pt_tensor






def 		_A     (    A__     ,				A__     ,				A__=42      ):






 """simple docstring"""



 __lowercase							  = {k: v.numpy() for k, v in pt_state_dict.items()}

 # Step 2: Since the model is stateless, get random Flax params
 __lowercase							  = flax_model.init_weights(PRNGKey(A__      )      )

 __lowercase							  = flatten_dict(A__      )
 __lowercase							  = {}

 # Need to change some parameters name to match Flax names
 for pt_key, pt_tensor in pt_state_dict.items():
  __lowercase							  = rename_key(A__      )
  __lowercase							  = tuple(renamed_pt_key.split('''.'''      )      )

  # Correctly rename weight parameters
  __lowercase  ,	__lowercase							  = rename_key_and_reshape_tensor(A__     ,				A__     ,				A__      )

  if flax_key in random_flax_state_dict:
   if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
    raise ValueError(
        F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
        F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."      )

        # also add unexpected weight so that warning is thrown
  __lowercase							  = jnp.asarray(A__      )

 return unflatten_dict(A__      )





'''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()
__snake_case    						=logging.get_logger(__name__)





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

   return new_state_dict





def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     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)
         lowerCAmelCase     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
         lowerCAmelCase     =			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
         lowerCAmelCase     =			kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCAmelCase     =			kv_bias[: config.hidden_sizes[i]]
         lowerCAmelCase     =			kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCAmelCase     =			kv_bias[config.hidden_sizes[i] :]





def 	a_					( ):
   lowerCAmelCase     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)

   return image





@torch.no_grad()
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]=False	, lowerCamelCase	:     List[str]=None				):
   lowerCAmelCase     =			GLPNConfig(hidden_sizes=[64, 128, 320, 512]	, decoder_hidden_size=64	, depths=[3, 8, 27, 3]				)

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

   # prepare image
   lowerCAmelCase     =			prepare_img()
   lowerCAmelCase     =			image_processor(images=lowerCamelCase	, return_tensors='pt'				).pixel_values

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

   # load original state dict
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location=torch.device('cpu'				)				)

   # rename keys
   lowerCAmelCase     =			rename_keys(lowerCamelCase				)

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

   # create HuggingFace model and load state dict
   lowerCAmelCase     =			GLPNForDepthEstimation(lowerCamelCase				)
   model.load_state_dict(lowerCamelCase				)
   model.eval()

   # forward pass
   lowerCAmelCase     =			model(lowerCamelCase				)
   lowerCAmelCase     =			outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCAmelCase     =			torch.tensor(
             [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]				)
      elif "kitti" in model_name:
         lowerCAmelCase     =			torch.tensor(
             [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]				)
      else:
         raise ValueError(f'''Unknown model name: {model_name}'''				)

      lowerCAmelCase     =			torch.Size([1, 480, 640]				)

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


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





