


"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 						__lowerCAmelCase		(		lowercase						:						Dict				)			->		Union[str, Any]:


    """simple docstring"""





    if "img_encoder.pos_embed" in name:
        snake_case					:   Optional[int]         =					name.replace("img_encoder.pos_embed"		,							"vision_model.embeddings.position_embeddings"				)
    if "img_encoder.patch_embed.proj" in name:
        snake_case					:   List[Any]         =					name.replace("img_encoder.patch_embed.proj"		,							"vision_model.embeddings.patch_embeddings.projection"				)
    if "img_encoder.patch_embed.norm" in name:
        snake_case					:   int         =					name.replace("img_encoder.patch_embed.norm"		,							"vision_model.embeddings.layernorm"				)
    if "img_encoder.layers" in name:
        snake_case					:   int         =					name.replace("img_encoder.layers"		,							"vision_model.encoder.stages"				)
    if "blocks" in name and "res" not in name:
        snake_case					:   Any         =					name.replace("blocks"		,							"layers"				)
    if "attn" in name and "pre_assign" not in name:
        snake_case					:   List[str]         =					name.replace("attn"		,							"self_attn"				)
    if "proj" in name and "self_attn" in name and "text" not in name:
        snake_case					:   int         =					name.replace("proj"		,							"out_proj"				)
    if "pre_assign_attn.attn.proj" in name:
        snake_case					:   List[Any]         =					name.replace("pre_assign_attn.attn.proj"		,							"pre_assign_attn.attn.out_proj"				)
    if "norm1" in name:
        snake_case					:   Tuple         =					name.replace("norm1"		,							"layer_norm1"				)
    if "norm2" in name and "pre_assign" not in name:
        snake_case					:   int         =					name.replace("norm2"		,							"layer_norm2"				)
    if "img_encoder.norm" in name:
        snake_case					:   List[Any]         =					name.replace("img_encoder.norm"		,							"vision_model.layernorm"				)
    # text encoder
    if "text_encoder.token_embedding" in name:
        snake_case					:   Optional[int]         =					name.replace("text_encoder.token_embedding"		,							"text_model.embeddings.token_embedding"				)
    if "text_encoder.positional_embedding" in name:
        snake_case					:   int         =					name.replace("text_encoder.positional_embedding"		,							"text_model.embeddings.position_embedding.weight"				)
    if "text_encoder.transformer.resblocks." in name:
        snake_case					:   Optional[int]         =					name.replace("text_encoder.transformer.resblocks."		,							"text_model.encoder.layers."				)
    if "ln_1" in name:
        snake_case					:   List[Any]         =					name.replace("ln_1"		,							"layer_norm1"				)
    if "ln_2" in name:
        snake_case					:   Any         =					name.replace("ln_2"		,							"layer_norm2"				)
    if "c_fc" in name:
        snake_case					:   Dict         =					name.replace("c_fc"		,							"fc1"				)
    if "c_proj" in name:
        snake_case					:   Union[str, Any]         =					name.replace("c_proj"		,							"fc2"				)
    if "text_encoder" in name:
        snake_case					:   List[Any]         =					name.replace("text_encoder"		,							"text_model"				)
    if "ln_final" in name:
        snake_case					:   List[str]         =					name.replace("ln_final"		,							"final_layer_norm"				)
    # projection layers
    if "img_projector.linear_hidden." in name:
        snake_case					:   Any         =					name.replace("img_projector.linear_hidden."		,							"visual_projection."				)
    if "img_projector.linear_out." in name:
        snake_case					:   str         =					name.replace("img_projector.linear_out."		,							"visual_projection.3."				)
    if "text_projector.linear_hidden" in name:
        snake_case					:   Dict         =					name.replace("text_projector.linear_hidden"		,							"text_projection"				)
    if "text_projector.linear_out" in name:
        snake_case					:   Any         =					name.replace("text_projector.linear_out"		,							"text_projection.3"				)

    return name







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						Dict				)			->		Optional[int]:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case					:   List[Any]         =					orig_state_dict.pop(lowercase				)

        if "qkv" in key:
            # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            snake_case					:   List[Any]         =					key.split("."				)
            snake_case      ,snake_case					:   Dict         =					int(key_split[2]				), int(key_split[4]				)
            snake_case					:   Tuple         =					config.vision_config.hidden_size
            if "weight" in key:
                snake_case					:   List[Any]         =					val[:dim, :]
                snake_case					:   str         =					val[dim : dim * 2, :]
                snake_case					:   List[str]         =					val[-dim:, :]
            else:
                snake_case					:   Any         =					val[:dim]
                snake_case					:   Union[str, Any]         =					val[dim : dim * 2]
                snake_case					:   Optional[Any]         =					val[-dim:]
        elif "in_proj" in key:
            # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            snake_case					:   Dict         =					key.split("."				)
            snake_case					:   str         =					int(key_split[3]				)
            snake_case					:   int         =					config.text_config.hidden_size
            if "weight" in key:
                snake_case					:   Optional[Any]         =					val[:dim, :]
                snake_case					:   Union[str, Any]         =					val[
                    dim : dim * 2, :
                ]
                snake_case					:   str         =					val[-dim:, :]
            else:
                snake_case					:   Optional[Any]         =					val[:dim]
                snake_case					:   Dict         =					val[dim : dim * 2]
                snake_case					:   Any         =					val[-dim:]
        else:
            snake_case					:   Any         =					rename_key(lowercase				)
            # squeeze if necessary
            if (
                "text_projection.0" in new_name
                or "text_projection.3" in new_name
                or "visual_projection.0" in new_name
                or "visual_projection.3" in new_name
            ):
                snake_case					:   Dict         =					val.squeeze_()
            else:
                snake_case					:   Optional[Any]         =					val

    return orig_state_dict







def 						__lowerCAmelCase		(		)			->		List[Any]:


    """simple docstring"""





    snake_case					:   List[Any]         =					"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case					:   str         =					Image.open(requests.get(lowercase		,							stream=lowercase				).raw				)
    return im







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						Tuple		,							lowercase						:						Optional[Any]		,							lowercase						:						Optional[Any]="groupvit-gcc-yfcc"		,							lowercase						:						int=False				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					GroupViTConfig()
    snake_case					:   Tuple         =					GroupViTModel(lowercase				).eval()

    snake_case					:   Optional[Any]         =					torch.load(lowercase		,							map_location="cpu"				)["model"]
    snake_case					:   Optional[int]         =					convert_state_dict(lowercase		,							lowercase				)
    snake_case      ,snake_case					:   Optional[int]         =					model.load_state_dict(lowercase		,							strict=lowercase				)
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase				) == 0)

    # verify result
    snake_case					:   List[Any]         =					CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"				)
    snake_case					:   Any         =					prepare_img()
    snake_case					:   Dict         =					processor(text=["a photo of a cat", "a photo of a dog"]		,							images=lowercase		,							padding=lowercase		,							return_tensors="pt"				)

    with torch.no_grad():
        snake_case					:   Any         =					model(**lowercase				)

    if model_name == "groupvit-gcc-yfcc":
        snake_case					:   Optional[int]         =					torch.tensor([[13.3523, 6.3629]]				)
    elif model_name == "groupvit-gcc-redcaps":
        snake_case					:   Optional[Any]         =					torch.tensor([[16.1873, 8.6230]]				)
    else:
        raise ValueError(F'Model name {model_name} not supported.'				)
    assert torch.allclose(outputs.logits_per_image		,							lowercase		,							atol=1e-3				)

    processor.save_pretrained(lowercase				)
    model.save_pretrained(lowercase				)
    print("Successfully saved processor and model to"		,							lowercase				)

    if push_to_hub:
        print("Pushing to the hub..."				)
        processor.push_to_hub(lowercase		,							organization="nielsr"				)
        model.push_to_hub(lowercase		,							organization="nielsr"				)


if __name__ == "__main__":
  __snake_case         =					argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
  )
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
  parser.add_argument(
      """--model_name""",
      default="""groupvit-gccy-fcc""",
      type=str,
      help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
  )
  __snake_case         =					parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)









"""simple docstring"""



import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__snake_case         =					NewType("""DataClass""", Any)
__snake_case         =					NewType("""DataClassType""", Any)







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]				)			->		Tuple:


    """simple docstring"""





    if isinstance(lowercase		,							lowercase				):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise ArgumentTypeError(
            F'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'				)







def 						__lowerCAmelCase		(		lowercase						:						list				)			->		Callable[[str], Any]:


    """simple docstring"""





    snake_case					:   Any         =					{str(lowercase				): choice for choice in choices}
    return lambda lowercase				: str_to_choice.get(lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		*,
    lowercase						:						Union[str, List[str]] = None		,							lowercase						:						str = None		,							lowercase						:						Any = dataclasses.MISSING		,							lowercase						:						Callable[[], Any] = dataclasses.MISSING		,							lowercase						:						dict = None		,							**lowercase						:						Tuple		,							)			->		dataclasses.Field:


    """simple docstring"""





    if metadata is None:
        # Important, don't use as default param in function signature because dict is mutable and shared across function calls
        snake_case					:   Any         =					{}
    if aliases is not None:
        snake_case					:   List[str]         =					aliases
    if help is not None:
        snake_case					:   Union[str, Any]         =					help

    return dataclasses.field(metadata=lowercase		,							default=lowercase		,							default_factory=lowercase		,							**lowercase				)







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Iterable[DataClassType]







   def __init__(			self		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       if "formatter_class" not in kwargs:
           snake_case					:   List[Any]         =					ArgumentDefaultsHelpFormatter
       super().__init__(**UpperCamelCase__		)
       if dataclasses.is_dataclass(UpperCamelCase__		):
           snake_case					:   str         =					[dataclass_types]
       snake_case					:   Any         =					list(UpperCamelCase__		)
       for dtype in self.dataclass_types:
           self._add_dataclass_arguments(UpperCamelCase__		)







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       snake_case					:   Tuple         =					F'--{field.name}'
       snake_case					:   Optional[Any]         =					field.metadata.copy()
       # field.metadata is not used at all by Data Classes,
       # it is provided as a third-party extension mechanism.
       if isinstance(field.type		,			UpperCamelCase__		):
           raise RuntimeError(
               "Unresolved type detected, which should have been done with the help of "
               "`typing.get_type_hints` method by default"		)

       snake_case					:   int         =					kwargs.pop("aliases"		,			[]		)
       if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Tuple         =					[aliases]

       snake_case					:   Tuple         =					getattr(field.type		,			"__origin__"		,			field.type		)
       if origin_type is Union or (hasattr(UpperCamelCase__		,			"UnionType"		) and isinstance(UpperCamelCase__		,			types.UnionType		)):
           if str not in field.type.__args__ and (
               len(field.type.__args__		) != 2 or type(UpperCamelCase__		) not in field.type.__args__
           ):
               raise ValueError(
                   "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
                   " the argument parser only supports one type per argument."
                   F' Problem encountered in field \'{field.name}\'.'		)
           if type(UpperCamelCase__		) not in field.type.__args__:
               # filter `str` in Union
               snake_case					:   Union[str, Any]         =					field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
               snake_case					:   str         =					getattr(field.type		,			"__origin__"		,			field.type		)
           elif bool not in field.type.__args__:
               # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
               snake_case					:   List[Any]         =					(
                   field.type.__args__[0] if isinstance(UpperCamelCase__		,			field.type.__args__[1]		) else field.type.__args__[1]
               )
               snake_case					:   Tuple         =					getattr(field.type		,			"__origin__"		,			field.type		)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
       snake_case					:   Optional[int]         =					{}
       if origin_type is Literal or (isinstance(field.type		,			UpperCamelCase__		) and issubclass(field.type		,			UpperCamelCase__		)):
           if origin_type is Literal:
               snake_case					:   Any         =					field.type.__args__
           else:
               snake_case					:   Dict         =					[x.value for x in field.type]

           snake_case					:   List[str]         =					make_choice_type_function(kwargs["choices"]		)

           if field.default is not dataclasses.MISSING:
               snake_case					:   Any         =					field.default
           else:
               snake_case					:   List[Any]         =					True
       elif field.type is bool or field.type == Optional[bool]:
           # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
           # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
           snake_case					:   Optional[Any]         =					copy(UpperCamelCase__		)

           # Hack because type=bool in argparse does not behave as we want.
           snake_case					:   Optional[Any]         =					string_to_bool
           if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
               # Default value is False if we have no default when of type bool.
               snake_case					:   Union[str, Any]         =					False if field.default is dataclasses.MISSING else field.default
               # This is the value that will get picked if we don't include --field_name in any way
               snake_case					:   Any         =					default
               # This tells argparse we accept 0 or 1 value after --field_name
               snake_case					:   Optional[int]         =					"?"
               # This is the value that will get picked if we do --field_name (without value)
               snake_case					:   Any         =					True
       elif isclass(UpperCamelCase__		) and issubclass(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   List[Any]         =					field.type.__args__[0]
           snake_case					:   Tuple         =					"+"
           if field.default_factory is not dataclasses.MISSING:
               snake_case					:   Union[str, Any]         =					field.default_factory()
           elif field.default is dataclasses.MISSING:
               snake_case					:   Dict         =					True
       else:
           snake_case					:   List[str]         =					field.type
           if field.default is not dataclasses.MISSING:
               snake_case					:   List[Any]         =					field.default
           elif field.default_factory is not dataclasses.MISSING:
               snake_case					:   Optional[Any]         =					field.default_factory()
           else:
               snake_case					:   Any         =					True
       parser.add_argument(UpperCamelCase__		,			*UpperCamelCase__		,			**UpperCamelCase__		)

       # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
       # Order is important for arguments with the same destination!
       # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
       # here and we do not need those changes/additional keys.
       if field.default is True and (field.type is bool or field.type == Optional[bool]):
           snake_case					:   Optional[Any]         =					False
           parser.add_argument(F'--no_{field.name}'		,			action="store_false"		,			dest=field.name		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       if hasattr(UpperCamelCase__		,			"_argument_group_name"		):
           snake_case					:   Optional[Any]         =					self.add_argument_group(dtype._argument_group_name		)
       else:
           snake_case					:   int         =					self

       try:
           snake_case					:   Dict[str, type]         =					get_type_hints(UpperCamelCase__		)
       except NameError:
           raise RuntimeError(
               F'Type resolution failed for {dtype}. Try declaring the class in global scope or '
               "removing line of `from __future__ import annotations` which opts in Postponed "
               "Evaluation of Annotations (PEP 563)"		)
       except TypeError as ex:
           # Remove this block when we drop Python 3.9 support
           if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(UpperCamelCase__		):
               snake_case					:   Dict         =					".".join(map(UpperCamelCase__		,			sys.version_info[:3]		)		)
               raise RuntimeError(
                   F'Type resolution failed for {dtype} on Python {python_version}. Try removing '
                   "line of `from __future__ import annotations` which opts in union types as "
                   "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
                   "support Python versions that lower than 3.10, you need to use "
                   "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
                   "`X | None`."		) from ex
           raise

       for field in dataclasses.fields(UpperCamelCase__		):
           if not field.init:
               continue
           snake_case					:   Optional[int]         =					type_hints[field.name]
           self._parse_dataclass_field(UpperCamelCase__		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
           snake_case					:   Optional[int]         =					[]

           if args_filename:
               args_files.append(Path(UpperCamelCase__		)		)
           elif look_for_args_file and len(sys.argv		):
               args_files.append(Path(sys.argv[0]		).with_suffix(".args"		)		)

           # args files specified via command line flag should overwrite default args files so we add them last
           if args_file_flag:
               # Create special parser just to extract the args_file_flag values
               snake_case					:   str         =					ArgumentParser()
               args_file_parser.add_argument(UpperCamelCase__		,			type=UpperCamelCase__		,			action="append"		)

               # Use only remaining args for further parsing (remove the args_file_flag)
               snake_case      ,snake_case					:   Dict         =					args_file_parser.parse_known_args(args=UpperCamelCase__		)
               snake_case					:   int         =					vars(UpperCamelCase__		).get(args_file_flag.lstrip("-"		)		,			UpperCamelCase__		)

               if cmd_args_file_paths:
                   args_files.extend([Path(UpperCamelCase__		) for p in cmd_args_file_paths]		)

           snake_case					:   Optional[Any]         =					[]
           for args_file in args_files:
               if args_file.exists():
                   file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
           snake_case					:   Dict         =					file_args + args if args is not None else file_args + sys.argv[1:]
       snake_case      ,snake_case					:   Any         =					self.parse_known_args(args=UpperCamelCase__		)
       snake_case					:   List[str]         =					[]
       for dtype in self.dataclass_types:
           snake_case					:   Optional[int]         =					{f.name for f in dataclasses.fields(UpperCamelCase__		) if f.init}
           snake_case					:   Optional[int]         =					{k: v for k, v in vars(UpperCamelCase__		).items() if k in keys}
           for k in keys:
               delattr(UpperCamelCase__		,			UpperCamelCase__		)
           snake_case					:   List[str]         =					dtype(**UpperCamelCase__		)
           outputs.append(UpperCamelCase__		)
       if len(namespace.__dict__		) > 0:
           # additional namespace.
           outputs.append(UpperCamelCase__		)
       if return_remaining_strings:
           return (*outputs, remaining_args)
       else:
           if remaining_args:
               raise ValueError(F'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'		)

           return (*outputs,)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       snake_case					:   int         =					set(args.keys()		)
       snake_case					:   int         =					[]
       for dtype in self.dataclass_types:
           snake_case					:   Tuple         =					{f.name for f in dataclasses.fields(UpperCamelCase__		) if f.init}
           snake_case					:   Dict         =					{k: v for k, v in args.items() if k in keys}
           unused_keys.difference_update(inputs.keys()		)
           snake_case					:   Optional[Any]         =					dtype(**UpperCamelCase__		)
           outputs.append(UpperCamelCase__		)
       if not allow_extra_keys and unused_keys:
           raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(UpperCamelCase__		)}'		)
       return tuple(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       with open(Path(UpperCamelCase__		)		,			encoding="utf-8"		) as open_json_file:
           snake_case					:   Optional[Any]         =					json.loads(open_json_file.read()		)
       snake_case					:   int         =					self.parse_dict(UpperCamelCase__		,			allow_extra_keys=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.parse_dict(yaml.safe_load(Path(UpperCamelCase__		).read_text()		)		,			allow_extra_keys=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)





