






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()
A_						:						Tuple						      =					logging.get_logger(__name__)

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

  return new_state_dict

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[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)
      UpperCamelCase_:   Tuple        =							state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''    )
      UpperCamelCase_:   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
      UpperCamelCase_:   str        =							kv_weight[
          : config.hidden_sizes[i], :
      ]
      UpperCamelCase_:   Union[str, Any]        =							kv_bias[: config.hidden_sizes[i]]
      UpperCamelCase_:   Any        =							kv_weight[
          config.hidden_sizes[i] :, :
      ]
      UpperCamelCase_:   Any        =							kv_bias[config.hidden_sizes[i] :]

def     snake_case   ()			->							Union[str, Any]:
  UpperCamelCase_:   Dict        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_:   int        =							Image.open(requests.get(UpperCAmelCase__      ,       stream=UpperCAmelCase__    ).raw    )

  return image

@torch.no_grad()
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=False      ,       UpperCAmelCase__=None    )			->							Optional[Any]:
  UpperCamelCase_:   Dict        =							GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,       decoder_hidden_size=6_4      ,       depths=[3, 8, 2_7, 3]    )

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

  # prepare image
  UpperCamelCase_:   str        =							prepare_img()
  UpperCamelCase_:   int        =							image_processor(images=UpperCAmelCase__      ,       return_tensors='pt'    ).pixel_values

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

  # load original state dict
  UpperCamelCase_:   Dict        =							torch.load(UpperCAmelCase__      ,       map_location=torch.device('cpu'    )    )

  # rename keys
  UpperCamelCase_:   str        =							rename_keys(UpperCAmelCase__    )

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

  # create HuggingFace model and load state dict
  UpperCamelCase_:   Dict        =							GLPNForDepthEstimation(UpperCAmelCase__    )
  model.load_state_dict(UpperCAmelCase__    )
  model.eval()

  # forward pass
  UpperCamelCase_:   Dict        =							model(UpperCAmelCase__    )
  UpperCamelCase_:   Dict        =							outputs.predicted_depth

  # verify output
  if model_name is not None:
    if "nyu" in model_name:
      UpperCamelCase_:   str        =							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:
      UpperCamelCase_:   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}'''    )

    UpperCamelCase_:   Optional[int]        =							torch.Size([1, 4_8_0, 6_4_0]    )

    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__":
   A_						:						int						      =					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.',
   )
   A_						:						List[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 os
import sys
import unittest


__UpperCAmelCase            =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase            =       os.path.join(git_repo_path, """src""", """transformers""")

__UpperCAmelCase            =       """
{0} = None
"""

__UpperCAmelCase            =       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__UpperCAmelCase            =       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""						)
							self.assertIsNone(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			find_backend("""    if not is_tokenizers_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend("""    if not is_tensorflow_text_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				List[str]		=			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers_and_vision"""						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("""torch"""      ,						lowerCamelCase_						)
							self.assertIn("""tensorflow_text"""      ,						lowerCamelCase_						)
							self.assertIn("""sentencepiece_and_tokenizers"""      ,						lowerCamelCase_						)

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertModel"""      ,						objects["""tf"""]						)
							self.assertIn("""FlaxBertModel"""      ,						objects["""flax"""]						)
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertTokenizer"""      ,						objects["""tensorflow_text"""]						)
							self.assertIn("""convert_slow_tokenizer"""      ,						objects["""sentencepiece_and_tokenizers"""]						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			create_dummy_object("""CONSTANT"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						"""\nCONSTANT = None\n"""						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_object("""function"""      ,						"""'torch'"""						)
							self.assertEqual(
							    lowerCamelCase_      ,						"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""						)

							SCREAMING_SNAKE_CASE :				str		=			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			create_dummy_object("""FakeClass"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}						)
							self.assertEqual(dummy_files["""torch"""]      ,						lowerCamelCase_						)



