





'''simple docstring'''




import argparse
import os
import re


_SCREAMING_SNAKE_CASE							     =			"src/diffusers"

# Pattern that looks at the indentation in a line.
_SCREAMING_SNAKE_CASE							     =			re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_SCREAMING_SNAKE_CASE							     =			re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_SCREAMING_SNAKE_CASE							     =			re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
_SCREAMING_SNAKE_CASE							     =			re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_SCREAMING_SNAKE_CASE							     =			re.compile(r"\[([^\]]+)\]")





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  Dict:
   snake_case 						=   _re_indent.search(__lowerCAmelCase		)
   return "" if search is None else search.groups()[0]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Union[str, Any]=""      ,    __lowerCAmelCase       :							Optional[Any]=None      ,    __lowerCAmelCase       :							List[str]=None		)		->  Union[str, Any]:
   snake_case 						=   0
   snake_case 						=   code.split("""\n"""		)
   if start_prompt is not None:
      while not lines[index].startswith(__lowerCAmelCase		):
         index += 1
      snake_case 						=   ["""\n""".join(lines[:index]		)]
   else:
      snake_case 						=   []

   # We split into blocks until we get to the `end_prompt` (or the end of the block).
   snake_case 						=   [lines[index]]
   index += 1
   while index < len(__lowerCAmelCase		) and (end_prompt is None or not lines[index].startswith(__lowerCAmelCase		)):
      if len(lines[index]		) > 0 and get_indent(lines[index]		) == indent_level:
         if len(__lowerCAmelCase		) > 0 and get_indent(current_block[-1]		).startswith(indent_level + """ """		):
            current_block.append(lines[index]		)
            blocks.append("""\n""".join(__lowerCAmelCase		)		)
            if index < len(__lowerCAmelCase		) - 1:
               snake_case 						=   [lines[index + 1]]
               index += 1
            else:
               snake_case 						=   []
         else:
            blocks.append("""\n""".join(__lowerCAmelCase		)		)
            snake_case 						=   [lines[index]]
      else:
         current_block.append(lines[index]		)
      index += 1

   # Adds current block if it's nonempty.
   if len(__lowerCAmelCase		) > 0:
      blocks.append("""\n""".join(__lowerCAmelCase		)		)

   # Add final block after end_prompt if provided.
   if end_prompt is not None and index < len(__lowerCAmelCase		):
      blocks.append("""\n""".join(lines[index:]		)		)

   return blocks





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  List[Any]:

   def _inner(__lowerCAmelCase       :							str		):
      return key(__lowerCAmelCase		).lower().replace("""_"""      ,    """"""		)

   return _inner





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							List[Any]=None		)		->  Any:

   # If no key is provided, we use a noop.
   def noop(__lowerCAmelCase       :							Any		):
      return x

   if key is None:
      snake_case 						=   noop
   # Constants are all uppercase, they go first.
   snake_case 						=   [obj for obj in objects if key(__lowerCAmelCase		).isupper()]
   # Classes are not all uppercase but start with a capital, they go second.
   snake_case 						=   [obj for obj in objects if key(__lowerCAmelCase		)[0].isupper() and not key(__lowerCAmelCase		).isupper()]
   # Functions begin with a lowercase, they go last.
   snake_case 						=   [obj for obj in objects if not key(__lowerCAmelCase		)[0].isupper()]

   snake_case 						=   ignore_underscore(__lowerCAmelCase		)
   return sorted(__lowerCAmelCase      ,    key=__lowerCAmelCase		) + sorted(__lowerCAmelCase      ,    key=__lowerCAmelCase		) + sorted(__lowerCAmelCase      ,    key=__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  List[str]:

   # This inner function sort imports between [ ].
   def _replace(__lowerCAmelCase       :							Optional[int]		):
      snake_case 						=   match.groups()[0]
      if "," not in imports:
         return F'''[{imports}]'''
      snake_case 						=   [part.strip().replace("""\""""      ,    """"""		) for part in imports.split(""","""		)]
      # We will have a final empty element if the line finished with a comma.
      if len(keys[-1]		) == 0:
         snake_case 						=   keys[:-1]
      return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase		)]		) + "]"

   snake_case 						=   import_statement.split("""\n"""		)
   if len(__lowerCAmelCase		) > 3:
      # Here we have to sort internal imports that are on several lines (one per name):
      # key: [
      #     "object1",
      #     "object2",
      #     ...
      # ]

      # We may have to ignore one or two lines on each side.
      snake_case 						=   2 if lines[1].strip() == """[""" else 1
      snake_case 						=   [(i, _re_strip_line.search(__lowerCAmelCase		).groups()[0]) for i, line in enumerate(lines[idx:-idx]		)]
      snake_case 						=   sort_objects(__lowerCAmelCase      ,    key=lambda __lowerCAmelCase		: x[1]		)
      snake_case 						=   [lines[x[0] + idx] for x in sorted_indices]
      return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]		)
   elif len(__lowerCAmelCase		) == 3:
      # Here we have to sort internal imports that are on one separate line:
      # key: [
      #     "object1", "object2", ...
      # ]
      if _re_bracket_content.search(lines[1]		) is not None:
         snake_case 						=   _re_bracket_content.sub(_replace      ,    lines[1]		)
      else:
         snake_case 						=   [part.strip().replace("""\""""      ,    """"""		) for part in lines[1].split(""","""		)]
         # We will have a final empty element if the line finished with a comma.
         if len(keys[-1]		) == 0:
            snake_case 						=   keys[:-1]
         snake_case 						=   get_indent(lines[1]		) + """, """.join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase		)]		)
      return "\n".join(__lowerCAmelCase		)
   else:
      # Finally we have to deal with imports fitting on one line
      snake_case 						=   _re_bracket_content.sub(_replace      ,    __lowerCAmelCase		)
      return import_statement





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Union[str, Any]=True		)		->  str:
   with open(__lowerCAmelCase      ,    """r"""		) as f:
      snake_case 						=   f.read()

   if "_import_structure" not in code:
      return

   # Blocks of indent level 0
   snake_case 						=   split_code_in_indented_blocks(
       __lowerCAmelCase      ,    start_prompt="""_import_structure = {"""      ,    end_prompt="""if TYPE_CHECKING:"""		)

   # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
   for block_idx in range(1      ,    len(__lowerCAmelCase		) - 1		):
      # Check if the block contains some `_import_structure`s thingy to sort.
      snake_case 						=   main_blocks[block_idx]
      snake_case 						=   block.split("""\n"""		)

      # Get to the start of the imports.
      snake_case 						=   0
      while line_idx < len(__lowerCAmelCase		) and "_import_structure" not in block_lines[line_idx]:
         # Skip dummy import blocks
         if "import dummy" in block_lines[line_idx]:
            snake_case 						=   len(__lowerCAmelCase		)
         else:
            line_idx += 1
      if line_idx >= len(__lowerCAmelCase		):
         continue

      # Ignore beginning and last line: they don't contain anything.
      snake_case 						=   """\n""".join(block_lines[line_idx:-1]		)
      snake_case 						=   get_indent(block_lines[1]		)
      # Slit the internal block into blocks of indent level 1.
      snake_case 						=   split_code_in_indented_blocks(__lowerCAmelCase      ,    indent_level=__lowerCAmelCase		)
      # We have two categories of import key: list or _import_structure[key].append/extend
      snake_case 						=   _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
      # Grab the keys, but there is a trap: some lines are empty or just comments.
      snake_case 						=   [(pattern.search(__lowerCAmelCase		).groups()[0] if pattern.search(__lowerCAmelCase		) is not None else None) for b in internal_blocks]
      # We only sort the lines with a key.
      snake_case 						=   [(i, key) for i, key in enumerate(__lowerCAmelCase		) if key is not None]
      snake_case 						=   [x[0] for x in sorted(__lowerCAmelCase      ,    key=lambda __lowerCAmelCase		: x[1]		)]

      # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
      snake_case 						=   0
      snake_case 						=   []
      for i in range(len(__lowerCAmelCase		)		):
         if keys[i] is None:
            reordered_blocks.append(internal_blocks[i]		)
         else:
            snake_case 						=   sort_objects_in_import(internal_blocks[sorted_indices[count]]		)
            reordered_blocks.append(__lowerCAmelCase		)
            count += 1

        # And we put our main block back together with its first and last line.
      snake_case 						=   """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]		)

   if code != "\n".join(__lowerCAmelCase		):
      if check_only:
         return True
      else:
         print(F'''Overwriting {file}.'''		)
         with open(__lowerCAmelCase      ,    """w"""		) as f:
            f.write("""\n""".join(__lowerCAmelCase		)		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]=True		)		->  List[str]:
   snake_case 						=   []
   for root, _, files in os.walk(__lowerCAmelCase		):
      if "__init__.py" in files:
         snake_case 						=   sort_imports(os.path.join(__lowerCAmelCase      ,    """__init__.py"""		)      ,    check_only=__lowerCAmelCase		)
         if result:
            snake_case 						=   [os.path.join(__lowerCAmelCase      ,    """__init__.py"""		)]
   if len(__lowerCAmelCase		) > 0:
      raise ValueError(F'''Would overwrite {len(__lowerCAmelCase		)} files, run `make style`.'''		)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()

 sort_imports_in_all_inits(check_only=args.check_only)


"""simple docstring"""



import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)

def  _A(		lowerCAmelCase			,  lowerCAmelCase=False							):
							A__       :     Dict								=			[]
							for i in range(config.num_hidden_layers							):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')							)
														rename_keys.append(
														    (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')							)
														rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')							)

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

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

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														A__       :     Dict								=			[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""							) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]							)

							return rename_keys

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

def  _A(		lowerCAmelCase							):
							A__       :     str								=			["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)

def  _A(		lowerCAmelCase							):
							# projection head is used in the self-supervised pre-training in MSN,
							# for downstream task it's not needed.
							A__       :     Dict								=			[
							    """module.fc.fc1.weight""",
							    """module.fc.fc1.bias""",
							    """module.fc.bn1.weight""",
							    """module.fc.bn1.bias""",
							    """module.fc.bn1.running_mean""",
							    """module.fc.bn1.running_var""",
							    """module.fc.bn1.num_batches_tracked""",
							    """module.fc.fc2.weight""",
							    """module.fc.fc2.bias""",
							    """module.fc.bn2.weight""",
							    """module.fc.bn2.bias""",
							    """module.fc.bn2.running_mean""",
							    """module.fc.bn2.running_var""",
							    """module.fc.bn2.num_batches_tracked""",
							    """module.fc.fc3.weight""",
							    """module.fc.fc3.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			dct.pop(lowerCAmelCase							)
							A__       :     List[Any]								=			val

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			ViTMSNConfig()
							A__       :     List[str]								=			1000

							A__       :     Optional[int]								=			"""datasets/huggingface/label-files"""
							A__       :     Optional[int]								=			"""imagenet-1k-id2label.json"""
							A__       :     str								=			json.load(open(hf_hub_download(lowerCAmelCase			,  lowerCAmelCase							)			,  """r"""							)							)
							A__       :     Tuple								=			{int(lowerCAmelCase							): v for k, v in idalabel.items()}
							A__       :     Optional[Any]								=			idalabel
							A__       :     List[Any]								=			{v: k for k, v in idalabel.items()}

							if "s16" in checkpoint_url:
														A__       :     Tuple								=			384
														A__       :     int								=			1536
														A__       :     Union[str, Any]								=			6
							elif "l16" in checkpoint_url:
														A__       :     int								=			1024
														A__       :     int								=			4096
														A__       :     Any								=			24
														A__       :     int								=			16
														A__       :     Union[str, Any]								=			0.1
							elif "b4" in checkpoint_url:
														A__       :     Dict								=			4
							elif "l7" in checkpoint_url:
														A__       :     List[str]								=			7
														A__       :     Optional[int]								=			1024
														A__       :     Optional[int]								=			4096
														A__       :     Any								=			24
														A__       :     Union[str, Any]								=			16
														A__       :     Tuple								=			0.1

							A__       :     List[str]								=			ViTMSNModel(lowerCAmelCase							)

							A__       :     Any								=			torch.hub.load_state_dict_from_url(lowerCAmelCase			,  map_location="""cpu"""							)["""target_encoder"""]

							A__       :     List[str]								=			ViTImageProcessor(size=config.image_size							)

							remove_projection_head(lowerCAmelCase							)
							A__       :     List[str]								=			create_rename_keys(lowerCAmelCase			,  base_model=lowerCAmelCase							)

							for src, dest in rename_keys:
														rename_key(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							read_in_q_k_v(lowerCAmelCase			,  lowerCAmelCase			,  base_model=lowerCAmelCase							)

							model.load_state_dict(lowerCAmelCase							)
							model.eval()

							A__       :     str								=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""

							A__       :     int								=			Image.open(requests.get(lowerCAmelCase			,  stream=lowerCAmelCase							).raw							)
							A__       :     List[Any]								=			ViTImageProcessor(
							    size=config.image_size			,  image_mean=lowerCAmelCase			,  image_std=lowerCAmelCase							)
							A__       :     Any								=			image_processor(images=lowerCAmelCase			,  return_tensors="""pt"""							)

							# forward pass
							torch.manual_seed(2							)
							A__       :     Tuple								=			model(**lowerCAmelCase							)
							A__       :     Union[str, Any]								=			outputs.last_hidden_state

							# The following Colab Notebook was used to generate these outputs:
							# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
							if "s16" in checkpoint_url:
														A__       :     Union[str, Any]								=			torch.tensor([[-1.0915, -1.4876, -1.1809]]							)
							elif "b16" in checkpoint_url:
														A__       :     int								=			torch.tensor([[14.2889, -18.9045, 11.7281]]							)
							elif "l16" in checkpoint_url:
														A__       :     List[Any]								=			torch.tensor([[41.5028, -22.8681, 45.6475]]							)
							elif "b4" in checkpoint_url:
														A__       :     Optional[Any]								=			torch.tensor([[-4.3868, 5.2932, -0.4137]]							)
							else:
														A__       :     str								=			torch.tensor([[-0.1792, -0.6465, 2.4263]]							)

							# verify logits
							assert torch.allclose(last_hidden_state[:, 0, :3]			,  lowerCAmelCase			,  atol=1E-4							)

							print(F'''Saving model 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__":
				_UpperCamelCase								=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
				    type=str,
				    help="URL of the checkpoint you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)

				_UpperCamelCase								=		parser.parse_args()
				convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





