





"""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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      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(SCREAMING_SNAKE_CASE			)-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(SCREAMING_SNAKE_CASE			)-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(SCREAMING_SNAKE_CASE			)-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(SCREAMING_SNAKE_CASE			)-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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      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 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       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)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

      logger.info("""Converting model..."""			)

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       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
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

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

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]			)
            elif "kitti" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]			)
            else:
                  raise ValueError(F'Unknown model name: {model_name}'			)

            lowerCAmelCase  						=       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)







import collections
import importlib.util
import os
import re
from pathlib import Path


_SCREAMING_SNAKE_CASE										=						'src/transformers'


# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE										=						re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_SCREAMING_SNAKE_CASE										=						re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_SCREAMING_SNAKE_CASE										=						re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
_SCREAMING_SNAKE_CASE										=						re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_SCREAMING_SNAKE_CASE										=						re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
_SCREAMING_SNAKE_CASE										=						re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*try:')
# Catches a line with else:
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*else:')






def  snake_case  (       snake_case__			:Optional[Any])						->   List[str]:
       if _re_test_backend.search(snake_case__) is None:
              return None
       _A        =  [b[0] for b in _re_backend.findall(snake_case__)]
       backends.sort()
       return "_and_".join(snake_case__)






def  snake_case  (       snake_case__			:Any)						->   Any:
       with open(snake_case__   ,	"""r"""   ,	encoding="""utf-8"""   ,	newline="""\n""") as f:
              _A        =  f.readlines()

       _A        =  0
       while line_index < len(snake_case__) and not lines[line_index].startswith("""_import_structure = {"""):
              line_index += 1

       # If this is a traditional init, just return.
       if line_index >= len(snake_case__):
              return None

       # First grab the objects without a specific backend in _import_structure
       _A        =  []
       while not lines[line_index].startswith("""if TYPE_CHECKING""") and find_backend(lines[line_index]) is None:
              _A        =  lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(snake_case__):
                     _A        =  _re_one_line_import_struct.search(snake_case__).groups()[0]
                     _A        =  re.findall("""\[([^\]]+)\]"""   ,	snake_case__)
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(""", """)])
                     line_index += 1
                     continue
              _A        =  _re_import_struct_key_value.search(snake_case__)
              if single_line_import_search is not None:
                     _A        =  [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """) if len(snake_case__) > 0]
                     objects.extend(snake_case__)
              elif line.startswith(""" """ * 8 + """\""""):
                     objects.append(line[9:-3])
              line_index += 1

       _A        =  {"""none""": objects}
       # Let's continue with backend-specific objects in _import_structure
       while not lines[line_index].startswith("""if TYPE_CHECKING"""):
              # If the line is an if not is_backend_available, we grab all objects associated.
              _A        =  find_backend(lines[line_index])
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]) is None:
                     _A        =  None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]) is None:
                            line_index += 1

                     line_index += 1

                     _A        =  []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]) <= 1 or lines[line_index].startswith(""" """ * 4):
                            _A        =  lines[line_index]
                            if _re_import_struct_add_one.search(snake_case__) is not None:
                                   objects.append(_re_import_struct_add_one.search(snake_case__).groups()[0])
                            elif _re_import_struct_add_many.search(snake_case__) is not None:
                                   _A        =  _re_import_struct_add_many.search(snake_case__).groups()[0].split(""", """)
                                   _A        =  [obj[1:-1] for obj in imports if len(snake_case__) > 0]
                                   objects.extend(snake_case__)
                            elif _re_between_brackets.search(snake_case__) is not None:
                                   _A        =  _re_between_brackets.search(snake_case__).groups()[0].split(""", """)
                                   _A        =  [obj[1:-1] for obj in imports if len(snake_case__) > 0]
                                   objects.extend(snake_case__)
                            elif _re_quote_object.search(snake_case__) is not None:
                                   objects.append(_re_quote_object.search(snake_case__).groups()[0])
                            elif line.startswith(""" """ * 8 + """\""""):
                                   objects.append(line[9:-3])
                            elif line.startswith(""" """ * 12 + """\""""):
                                   objects.append(line[13:-3])
                            line_index += 1

                     _A        =  objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       _A        =  []
       while (
           line_index < len(snake_case__)
           and find_backend(lines[line_index]) is None
           and not lines[line_index].startswith("""else""")
       ):
              _A        =  lines[line_index]
              _A        =  _re_import.search(snake_case__)
              if single_line_import_search is not None:
                     objects.extend(single_line_import_search.groups()[0].split(""", """))
              elif line.startswith(""" """ * 8):
                     objects.append(line[8:-2])
              line_index += 1

       _A        =  {"""none""": objects}
       # Let's continue with backend-specific objects
       while line_index < len(snake_case__):
              # If the line is an if is_backend_available, we grab all objects associated.
              _A        =  find_backend(lines[line_index])
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]) is None:
                     _A        =  None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]) is None:
                            line_index += 1

                     line_index += 1

                     _A        =  []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]) <= 1 or lines[line_index].startswith(""" """ * 8):
                            _A        =  lines[line_index]
                            _A        =  _re_import.search(snake_case__)
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(""", """))
                            elif line.startswith(""" """ * 12):
                                   objects.append(line[12:-2])
                            line_index += 1

                     _A        =  objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects






def  snake_case  (       snake_case__			:Dict   ,	snake_case__			:int)						->   List[Any]:

       def find_duplicates(snake_case__			:Union[str, Any]):
              return [k for k, v in collections.Counter(snake_case__).items() if v > 1]

       if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):
              return ["Both sides of the init do not have the same backends!"]

       _A        =  []
       for key in import_dict_objects.keys():
              _A        =  find_duplicates(import_dict_objects[key])
              if duplicate_imports:
                     errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''')
              _A        =  find_duplicates(type_hint_objects[key])
              if duplicate_type_hints:
                     errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''')

              if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):
                     _A        =  """base imports""" if key == """none""" else F'''{key} backend'''
                     errors.append(F'''Differences for {name}:''')
                     for a in type_hint_objects[key]:
                            if a not in import_dict_objects[key]:
                                   errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.''')
                     for a in import_dict_objects[key]:
                            if a not in type_hint_objects[key]:
                                   errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.''')
       return errors






def  snake_case  (       )						->   int:
       _A        =  []
       for root, _, files in os.walk(snake_case__):
              if "__init__.py" in files:
                     _A        =  os.path.join(snake_case__   ,	"""__init__.py""")
                     _A        =  parse_init(snake_case__)
                     if objects is not None:
                            _A        =  analyze_results(*snake_case__)
                            if len(snake_case__) > 0:
                                   _A        =  F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
                                   failures.append("""\n""".join(snake_case__))
       if len(snake_case__) > 0:
              raise ValueError("""\n\n""".join(snake_case__))






def  snake_case  (       )						->   Optional[Any]:
       _A        =  []
       for path, directories, files in os.walk(snake_case__):
              for folder in directories:
                     # Ignore private modules
                     if folder.startswith("""_"""):
                            directories.remove(snake_case__)
                            continue
                     # Ignore leftovers from branches (empty folders apart from pycache)
                     if len(list((Path(snake_case__) / folder).glob("""*.py"""))) == 0:
                            continue
                     _A        =  str((Path(snake_case__) / folder).relative_to(snake_case__))
                     _A        =  short_path.replace(os.path.sep   ,	""".""")
                     submodules.append(snake_case__)
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     _A        =  str((Path(snake_case__) / fname).relative_to(snake_case__))
                     _A        =  short_path.replace(""".py"""   ,	"""""").replace(os.path.sep   ,	""".""")
                     if len(submodule.split(""".""")) == 1:
                            submodules.append(snake_case__)
       return submodules


_SCREAMING_SNAKE_CASE										=						[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]






def  snake_case  (       )						->   Union[str, Any]:
       # This is to make sure the transformers module imported is the one in the repo.
       _A        =  importlib.util.spec_from_file_location(
           """transformers"""   ,	os.path.join(snake_case__   ,	"""__init__.py""")   ,	submodule_search_locations=[PATH_TO_TRANSFORMERS]   ,	)
       _A        =  spec.loader.load_module()

       _A        =  [
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
       ]
       if len(snake_case__) > 0:
              _A        =  """\n""".join(F'''- {module}''' for module in module_not_registered)
              raise ValueError(
                  """The following submodules are not properly registered in the main init of Transformers:\n"""
                  F'''{list_of_modules}\n'''
                  """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""")


if __name__ == "__main__":
    check_all_inits()
    check_submodules()




