



'''simple docstring'''





import collections
import os
import re
from pathlib import Path


__a          =		"src/transformers"


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





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





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""     ,   newline="""\n"""		) as f:
            snake_case__  :    List[str]    						=      f.readlines()

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

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

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

      snake_case__  :    Optional[int]    						=      {"""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.
            snake_case__  :    List[Any]    						=      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:
                  snake_case__  :    Any    						=      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

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

                  snake_case__  :    Any    						=      objects
            else:
                  line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
      snake_case__  :    List[str]    						=      []
      while (
          line_index < len(_lowerCAmelCase		)
          and find_backend(lines[line_index]		) is None
          and not lines[line_index].startswith("""else"""		)
      ):
            snake_case__  :    Tuple    						=      lines[line_index]
            snake_case__  :    Tuple    						=      _re_import.search(_lowerCAmelCase		)
            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

      snake_case__  :    Dict    						=      {"""none""": objects}
      # Let's continue with backend-specific objects
      while line_index < len(_lowerCAmelCase		):
            # If the line is an if is_backend_available, we grab all objects associated.
            snake_case__  :    List[Any]    						=      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:
                  snake_case__  :    Tuple    						=      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

                  snake_case__  :    List[str]    						=      []
                  # Until we unindent, add backend objects to the list
                  while len(lines[line_index]		) <= 1 or lines[line_index].startswith(""" """ * 8		):
                        snake_case__  :    Any    						=      lines[line_index]
                        snake_case__  :    Optional[Any]    						=      _re_import.search(_lowerCAmelCase		)
                        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

                  snake_case__  :    Any    						=      objects
            else:
                  line_index += 1

      return import_dict_objects, type_hint_objects





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:

      def find_duplicates(_lowerCAmelCase		):
            return [k for k, v in collections.Counter(_lowerCAmelCase		).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!"]

      snake_case__  :    Optional[Any]    						=      []
      for key in import_dict_objects.keys():
            snake_case__  :    int    						=      find_duplicates(import_dict_objects[key]		)
            if duplicate_imports:
                  errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}"		)
            snake_case__  :    List[Any]    						=      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]		)		):
                  snake_case__  :    str    						=      """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:
      snake_case__  :    Any    						=      []
      for root, _, files in os.walk(_lowerCAmelCase		):
            if "__init__.py" in files:
                  snake_case__  :    Optional[int]    						=      os.path.join(_lowerCAmelCase     ,   """__init__.py"""		)
                  snake_case__  :    List[str]    						=      parse_init(_lowerCAmelCase		)
                  if objects is not None:
                        snake_case__  :    Union[str, Any]    						=      analyze_results(*_lowerCAmelCase		)
                        if len(_lowerCAmelCase		) > 0:
                              snake_case__  :    Optional[int]    						=      f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
                              failures.append("""\n""".join(_lowerCAmelCase		)		)
      if len(_lowerCAmelCase		) > 0:
            raise ValueError("""\n\n""".join(_lowerCAmelCase		)		)





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


__a          =		[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
    "models.esm.openfold_utils",
]





def 						__snake_case(				)		-> Any:
      # This is to make sure the transformers module imported is the one in the repo.
      from transformers.utils import direct_transformers_import

      snake_case__  :    Optional[Any]    						=      direct_transformers_import(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      set(transformers._import_structure.keys()		)
      # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
      # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
      # (potentiall re-) add them.
      with open(os.path.join(_lowerCAmelCase     ,   """__init__.py"""		)     ,   """r"""		) as f:
            snake_case__  :    int    						=      f.read()
      import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""     ,   _lowerCAmelCase		)		)		)

      snake_case__  :    List[str]    						=      [
          module
          for module in get_transformers_submodules()
          if module not in IGNORE_SUBMODULES and module not in import_structure_keys
      ]

      if len(_lowerCAmelCase		) > 0:
            snake_case__  :    Optional[int]    						=      """\n""".join(f"- {module}" for module in module_not_registered		)
            raise ValueError(
                """The following submodules are not properly registed 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()





'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> str:
      snake_case__  :    Union[str, Any]    						=      []
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")		)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")		)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")		)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")		)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """deit.embeddings.cls_token"""),
              ("""dist_token""", """deit.embeddings.distillation_token"""),
              ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """deit.embeddings.position_embeddings"""),
          ]		)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("""norm.weight""", """layernorm.weight"""),
                    ("""norm.bias""", """layernorm.bias"""),
                    ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                    ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                ]		)

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            snake_case__  :    List[Any]    						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]		)

      return rename_keys





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Union[str, Any]:
      for i in range(config.num_hidden_layers		):
            if base_model:
                  snake_case__  :    Tuple    						=      """"""
            else:
                  snake_case__  :    Dict    						=      """deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            snake_case__  :    Optional[Any]    						=      state_dict.pop(f"blocks.{i}.attn.qkv.weight"		)
            snake_case__  :    Tuple    						=      state_dict.pop(f"blocks.{i}.attn.qkv.bias"		)
            # next, add query, keys and values (in that order) to the state dict
            snake_case__  :    Any    						=      in_proj_weight[
                : config.hidden_size, :
            ]
            snake_case__  :    Optional[int]    						=      in_proj_bias[: config.hidden_size]
            snake_case__  :    Any    						=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            snake_case__  :    str    						=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            snake_case__  :    List[str]    						=      in_proj_weight[
                -config.hidden_size :, :
            ]
            snake_case__  :    Tuple    						=      in_proj_bias[-config.hidden_size :]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    str    						=      dct.pop(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      val





def 						__snake_case(				)		-> Tuple:
      snake_case__  :    Union[str, Any]    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[int]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case__  :    Union[str, Any]    						=      False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case__  :    int    						=      1_000
      snake_case__  :    Any    						=      """huggingface/label-files"""
      snake_case__  :    Optional[Any]    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    Tuple    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    List[Any]    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    List[Any]    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      int(deit_name[-6:-4]		)
      snake_case__  :    Optional[Any]    						=      int(deit_name[-3:]		)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""		):
            snake_case__  :    Tuple    						=      192
            snake_case__  :    Union[str, Any]    						=      768
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Union[str, Any]    						=      3
      elif deit_name[9:].startswith("""small"""		):
            snake_case__  :    str    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    str    						=      12
            snake_case__  :    int    						=      6
      if deit_name[9:].startswith("""base"""		):
            pass
      elif deit_name[4:].startswith("""large"""		):
            snake_case__  :    Union[str, Any]    						=      1_024
            snake_case__  :    Any    						=      4_096
            snake_case__  :    List[Any]    						=      24
            snake_case__  :    Tuple    						=      16

      # load original model from timm
      snake_case__  :    List[Any]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[Any]    						=      timm_model.state_dict()
      snake_case__  :    Optional[int]    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # load HuggingFace model
      snake_case__  :    Optional[Any]    						=      DeiTForImageClassificationWithTeacher(_lowerCAmelCase		).eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case__  :    List[Any]    						=      int(
          (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case__  :    Optional[Any]    						=      DeiTImageProcessor(size=_lowerCAmelCase     ,   crop_size=config.image_size		)
      snake_case__  :    str    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Optional[Any]    						=      encoding["""pixel_values"""]
      snake_case__  :    Tuple    						=      model(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      timm_model(_lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase     ,   outputs.logits     ,   atol=1e-3		)

      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)
      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __a          =		parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
