


'''simple docstring'''




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


__snake_case    						=get_tests_dir("""fixtures""")

class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[int]:
        # A mock response for an HTTP head request to emulate server down
        lowerCAmelCase     =			mock.Mock()
        lowerCAmelCase     =			5_0_0
        lowerCAmelCase     =			{}
        lowerCAmelCase     =			HTTPError
        lowerCAmelCase     =			{}

        # Download this model to make sure it's in the cache.
        lowerCAmelCase     =			ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'    )
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch('requests.Session.request'    ,      return_value=UpperCAmelCase__    ) as mock_head:
           lowerCAmelCase     =			ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'    )
           # This check we did call the fake head request
           mock_head.assert_called()





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        # This test is for deprecated behavior and can be removed in v5
        lowerCAmelCase     =			ViTImageProcessor.from_pretrained(
            'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        with self.assertRaises(UpperCAmelCase__    ):
           # config is in subfolder, the following should not work without specifying the subfolder
           lowerCAmelCase     =			AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'    )

        lowerCAmelCase     =			AutoImageProcessor.from_pretrained(
            'hf-internal-testing/stable-diffusion-all-variants'    ,      subfolder='feature_extractor'    )

        self.assertIsNotNone(UpperCAmelCase__    )




@is_staging_test
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      List[str]    )				->  Union[str, Any]:
        lowerCAmelCase     =			TOKEN
        HfFolder.save_token(UpperCAmelCase__    )





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      Union[str, Any]    )				->  Any:
        try:
           delete_repo(token=cls._token    ,      repo_id='test-image-processor'    )
        except HTTPError:
           pass

        try:
           delete_repo(token=cls._token    ,      repo_id='valid_org/test-image-processor-org'    )
        except HTTPError:
           pass

        try:
           delete_repo(token=cls._token    ,      repo_id='test-dynamic-image-processor'    )
        except HTTPError:
           pass





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        lowerCAmelCase     =			ViTImageProcessor.from_pretrained(UpperCAmelCase__    )
        image_processor.push_to_hub('test-image-processor'    ,      use_auth_token=self._token    )

        lowerCAmelCase     =			ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''    )
        for k, v in image_processor.__dict__.items():
           self.assertEqual(UpperCAmelCase__    ,      getattr(UpperCAmelCase__    ,      UpperCAmelCase__    )    )

        # Reset repo
        delete_repo(token=self._token    ,      repo_id='test-image-processor'    )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               UpperCAmelCase__    ,      repo_id='test-image-processor'    ,      push_to_hub=UpperCAmelCase__    ,      use_auth_token=self._token    )

        lowerCAmelCase     =			ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''    )
        for k, v in image_processor.__dict__.items():
           self.assertEqual(UpperCAmelCase__    ,      getattr(UpperCAmelCase__    ,      UpperCAmelCase__    )    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  str:
        lowerCAmelCase     =			ViTImageProcessor.from_pretrained(UpperCAmelCase__    )
        image_processor.push_to_hub('valid_org/test-image-processor'    ,      use_auth_token=self._token    )

        lowerCAmelCase     =			ViTImageProcessor.from_pretrained('valid_org/test-image-processor'    )
        for k, v in image_processor.__dict__.items():
           self.assertEqual(UpperCAmelCase__    ,      getattr(UpperCAmelCase__    ,      UpperCAmelCase__    )    )

        # Reset repo
        delete_repo(token=self._token    ,      repo_id='valid_org/test-image-processor'    )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               UpperCAmelCase__    ,      repo_id='valid_org/test-image-processor-org'    ,      push_to_hub=UpperCAmelCase__    ,      use_auth_token=self._token    )

        lowerCAmelCase     =			ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'    )
        for k, v in image_processor.__dict__.items():
           self.assertEqual(UpperCAmelCase__    ,      getattr(UpperCAmelCase__    ,      UpperCAmelCase__    )    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        CustomImageProcessor.register_for_auto_class()
        lowerCAmelCase     =			CustomImageProcessor.from_pretrained(UpperCAmelCase__    )

        image_processor.push_to_hub('test-dynamic-image-processor'    ,      use_auth_token=self._token    )

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            image_processor.auto_map    ,      {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'}    ,      )

        lowerCAmelCase     =			AutoImageProcessor.from_pretrained(
            F'''{USER}/test-dynamic-image-processor'''    ,      trust_remote_code=UpperCAmelCase__    )
        # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
        self.assertEqual(new_image_processor.__class__.__name__    ,      'CustomImageProcessor'    )









'''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)





