"""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()
__SCREAMING_SNAKE_CASE 			=logging.get_logger(__name__)



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

     return new_state_dict



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     # 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)
               lowercase_			:       List[str]									=			state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''   )
               lowercase_			:       Union[str, Any]									=			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
               lowercase_			:       str									=			kv_weight[
                   : config.hidden_sizes[i], :
               ]
               lowercase_			:       Dict									=			kv_bias[: config.hidden_sizes[i]]
               lowercase_			:       Dict									=			kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               lowercase_			:       Tuple									=			kv_bias[config.hidden_sizes[i] :]



def     lowercase__( ):
     lowercase_			:       List[Any]									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_			:       Union[str, Any]									=			Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,    stream=__SCREAMING_SNAKE_CASE   ).raw   )

     return image



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  Dict=None   ):
     lowercase_			:       int									=			GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]  ,    decoder_hidden_size=64  ,    depths=[3, 8, 27, 3]   )

     # load image processor (only resize + rescale)
     lowercase_			:       List[str]									=			GLPNImageProcessor()

     # prepare image
     lowercase_			:       Union[str, Any]									=			prepare_img()
     lowercase_			:       Tuple									=			image_processor(images=__SCREAMING_SNAKE_CASE  ,    return_tensors='pt'   ).pixel_values

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

     # load original state dict
     lowercase_			:       List[str]									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location=torch.device('cpu'   )   )

     # rename keys
     lowercase_			:       Optional[Any]									=			rename_keys(__SCREAMING_SNAKE_CASE   )

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

     # create HuggingFace model and load state dict
     lowercase_			:       List[str]									=			GLPNForDepthEstimation(__SCREAMING_SNAKE_CASE   )
     model.load_state_dict(__SCREAMING_SNAKE_CASE   )
     model.eval()

     # forward pass
     lowercase_			:       Union[str, Any]									=			model(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Union[str, Any]									=			outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               lowercase_			:       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:
               lowercase_			:       List[Any]									=			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}'''   )

          lowercase_			:       str									=			torch.Size([1, 4_80, 6_40]   )

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


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


"""simple docstring"""







import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  UpperCamelCase	(    nn.Module						):
   def __init__(    self    )			->   Any:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       Dict									=			nn.Linear(3  ,4    )
        lowercase_			:       Union[str, Any]									=			nn.BatchNormad(4    )
        lowercase_			:       Union[str, Any]									=			nn.Linear(4  ,5    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase    )    )    )







class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			ModelForTest()
        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,model.state_dict()    )
             lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'index.json'    )
             self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
             # TODO: add tests on what is inside the index

             for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  lowercase_			:       int									=			os.path.join(__UpperCamelCase  ,f'''{key}.dat'''    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  # TODO: add tests on the fact weights are properly loaded
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
             lowercase_			:       Tuple									=			torch.randn(2  ,3  ,dtype=__UpperCamelCase    )
             with TemporaryDirectory() as tmp_dir:
                  lowercase_			:       List[str]									=			offload_weight(__UpperCamelCase  ,'weight'  ,__UpperCamelCase  ,{}    )
                  lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'weight.dat'    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  self.assertDictEqual(__UpperCamelCase  ,{'weight': {'shape': [2, 3], 'dtype': str(__UpperCamelCase    ).split('.'    )[1]}}    )

                  lowercase_			:       Optional[Any]									=			load_offloaded_weight(__UpperCamelCase  ,index['weight']    )
                  self.assertTrue(torch.equal(__UpperCamelCase  ,__UpperCamelCase    )    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			ModelForTest()
        lowercase_			:       Optional[int]									=			model.state_dict()
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'linear2' not in k}
        lowercase_			:       Optional[Any]									=			{k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' in k}
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       int									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             # Duplicates are removed
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )







   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			{'a.1': 0, 'a.10': 1, 'a.2': 2}
        lowercase_			:       int									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1': 0, 'a.2': 2}    )

        lowercase_			:       Union[str, Any]									=			{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        lowercase_			:       Optional[int]									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1.a': 0, 'a.2.a': 2}    )

