

'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = '''huggingface/label-files'''
 __lowercase							  = '''imagenet-1k-id2label.json'''
 __lowercase							  = json.load(open(hf_hub_download(A__     ,				A__     ,				repo_type='''dataset'''      )     ,				'''r'''      )      )
 __lowercase							  = {int(A__      ): v for k, v in idalabel.items()}
 __lowercase							  = {v: k for k, v in idalabel.items()}

 __lowercase							  = '''std_conv''' if '''bit''' in model_name else False

 # note that when using BiT as backbone for ViT-hybrid checkpoints,
 # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
 # config.conv_layer = "std_conv_same"
 __lowercase							  = BitConfig(
     conv_layer=A__     ,				num_labels=1000     ,				idalabel=A__     ,				labelaid=A__     ,				)

 return config


def 		_A     (    A__      ):






 """simple docstring"""



 if "stem.conv" in name:
  __lowercase							  = name.replace('''stem.conv'''     ,				'''bit.embedder.convolution'''      )
 if "blocks" in name:
  __lowercase							  = name.replace('''blocks'''     ,				'''layers'''      )
 if "head.fc" in name:
  __lowercase							  = name.replace('''head.fc'''     ,				'''classifier.1'''      )
 if name.startswith('''norm'''      ):
  __lowercase							  = '''bit.''' + name
 if "bit" not in name and "classifier" not in name:
  __lowercase							  = '''bit.encoder.''' + name

 return name


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __lowercase							  = Image.open(requests.get(A__     ,				stream=A__      ).raw      )
 return im


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__=False      ):






 """simple docstring"""



 __lowercase							  = get_config(A__      )

 # load original model from timm
 __lowercase							  = create_model(A__     ,				pretrained=A__      )
 timm_model.eval()

 # load state_dict of original model
 __lowercase							  = timm_model.state_dict()
 for key in state_dict.copy().keys():
  __lowercase							  = state_dict.pop(A__      )
  __lowercase							  = val.squeeze() if '''head''' in key else val

 # load HuggingFace model
 __lowercase							  = BitForImageClassification(A__      )
 model.eval()
 model.load_state_dict(A__      )

 # create image processor
 __lowercase							  = create_transform(**resolve_data_config({}     ,				model=A__      )      )
 __lowercase							  = transform.transforms

 __lowercase							  = {
     '''bilinear''': PILImageResampling.BILINEAR,
     '''bicubic''': PILImageResampling.BICUBIC,
     '''nearest''': PILImageResampling.NEAREST,
 }

 __lowercase							  = BitImageProcessor(
     do_resize=A__     ,				size={'''shortest_edge''': timm_transforms[0].size}     ,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]     ,				do_center_crop=A__     ,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}     ,				do_normalize=A__     ,				image_mean=timm_transforms[-1].mean.tolist()     ,				image_std=timm_transforms[-1].std.tolist()     ,				)

 __lowercase							  = prepare_img()
 __lowercase							  = transform(A__      ).unsqueeze(0      )
 __lowercase							  = processor(A__     ,				return_tensors='''pt'''      ).pixel_values

 # verify pixel values
 assert torch.allclose(A__     ,				A__      )

 # verify logits
 with torch.no_grad():
  __lowercase							  = model(A__      )
  __lowercase							  = outputs.logits

 print('''Logits:'''     ,				logits[0, :3]      )
 print('''Predicted class:'''     ,				model.config.idalabel[logits.argmax(-1      ).item()]      )
 __lowercase							  = timm_model(A__      )
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(A__     ,				outputs.logits     ,				atol=1e-3      )
 print('''Looks ok!'''      )

 if pytorch_dump_folder_path is not None:
  Path(A__      ).mkdir(exist_ok=A__      )
  print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"      )
  model.save_pretrained(A__      )
  processor.save_pretrained(A__      )

 if push_to_hub:
  print(F"Pushing model {model_name} and processor to the hub"      )
  model.push_to_hub(F"ybelkada/{model_name}"      )
  processor.push_to_hub(F"ybelkada/{model_name}"      )


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''resnetv2_50x1_bitm''',
     type=str,
     help='''Name of the BiT 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.'''
 )
 parser.add_argument(
     '''--push_to_hub''',
     action='''store_true''',
     help='''Whether to push the model to the hub.''',
 )

 lowerCAmelCase__											=	parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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


lowercase__				:     str 		=						NewType("DataClass", Any)
lowercase__				:     Union[str, Any] 		=						NewType("DataClassType", Any)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if isinstance(_A     ,	_A							):
		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__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {str(_A							): choice for choice in choices}
	return lambda _A							: str_to_choice.get(_A     ,	_A							)


def 						lowerCamelCase__    (		*,
    _A = None     ,	_A = None     ,	_A = dataclasses.MISSING     ,	_A = dataclasses.MISSING     ,	_A = None     ,	**_A     ,	):







	'''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_		    =     {}
	if aliases is not None:
		snake_case_		    =     aliases
	if help is not None:
		snake_case_		    =     help

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






class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						lowerCAmelCase_     		=   42






						def __init__(						self							:     Optional[Any]       ,	__lowercase							:     Union[DataClassType, Iterable[DataClassType]]       ,	**__lowercase							:     Optional[int]						):






							"""simple docstring"""






							if "formatter_class" not in kwargs:
								snake_case_		    =     ArgumentDefaultsHelpFormatter
							super().__init__(**__lowercase						)
							if dataclasses.is_dataclass(__lowercase						):
								snake_case_		    =     [dataclass_types]
							snake_case_		    =     list(__lowercase						)
							for dtype in self.dataclass_types:
								self._add_dataclass_arguments(__lowercase						)






						@staticmethod
						def        snake_case__						(						__lowercase							:     ArgumentParser       ,	__lowercase							:     dataclasses.Field						):






							"""simple docstring"""






							snake_case_		    =     f"--{field.name}"
							snake_case_		    =     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       ,	__lowercase						):
								raise RuntimeError(
								    "Unresolved type detected, which should have been done with the help of "
								    "`typing.get_type_hints` method by default"						)

							snake_case_		    =     kwargs.pop("aliases"       ,	[]						)
							if isinstance(__lowercase       ,	__lowercase						):
								snake_case_		    =     [aliases]

							snake_case_		    =     getattr(field.type       ,	"__origin__"       ,	field.type						)
							if origin_type is Union or (hasattr(__lowercase       ,	"UnionType"						) and isinstance(__lowercase       ,	types.UnionType						)):
								if str not in field.type.__args__ and (
								    len(field.type.__args__						) != 2 or type(__lowercase						) 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(__lowercase						) not in field.type.__args__:
									# filter `str` in Union
									snake_case_		    =     field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
									snake_case_		    =     getattr(field.type       ,	"__origin__"       ,	field.type						)
								elif bool not in field.type.__args__:
									# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
									snake_case_		    =     (
									    field.type.__args__[0] if isinstance(__lowercase       ,	field.type.__args__[1]						) else field.type.__args__[1]
									)
									snake_case_		    =     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_		    =     {}
							if origin_type is Literal or (isinstance(field.type       ,	__lowercase						) and issubclass(field.type       ,	__lowercase						)):
								if origin_type is Literal:
									snake_case_		    =     field.type.__args__
								else:
									snake_case_		    =     [x.value for x in field.type]

								snake_case_		    =     make_choice_type_function(kwargs["choices"]						)

								if field.default is not dataclasses.MISSING:
									snake_case_		    =     field.default
								else:
									snake_case_		    =     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_		    =     copy(__lowercase						)

								# Hack because type=bool in argparse does not behave as we want.
								snake_case_		    =     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_		    =     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_		    =     default
									# This tells argparse we accept 0 or 1 value after --field_name
									snake_case_		    =     "?"
									# This is the value that will get picked if we do --field_name (without value)
									snake_case_		    =     True
							elif isclass(__lowercase						) and issubclass(__lowercase       ,	__lowercase						):
								snake_case_		    =     field.type.__args__[0]
								snake_case_		    =     "+"
								if field.default_factory is not dataclasses.MISSING:
									snake_case_		    =     field.default_factory()
								elif field.default is dataclasses.MISSING:
									snake_case_		    =     True
							else:
								snake_case_		    =     field.type
								if field.default is not dataclasses.MISSING:
									snake_case_		    =     field.default
								elif field.default_factory is not dataclasses.MISSING:
									snake_case_		    =     field.default_factory()
								else:
									snake_case_		    =     True
							parser.add_argument(__lowercase       ,	*__lowercase       ,	**__lowercase						)

							# 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_		    =     False
								parser.add_argument(f"--no_{field.name}"       ,	action="store_false"       ,	dest=field.name       ,	**__lowercase						)






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     DataClassType						):






							"""simple docstring"""






							if hasattr(__lowercase       ,	"_argument_group_name"						):
								snake_case_		    =     self.add_argument_group(dtype._argument_group_name						)
							else:
								snake_case_		    =     self

							try:
								snake_case_		    =     get_type_hints(__lowercase						)
							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(__lowercase						):
									snake_case_		    =     ".".join(map(__lowercase       ,	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(__lowercase						):
								if not field.init:
									continue
								snake_case_		    =     type_hints[field.name]
								self._parse_dataclass_field(__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     Union[str, Any]=None       ,	__lowercase							:     Union[str, Any]=False       ,	__lowercase							:     List[str]=True       ,	__lowercase							:     int=None       ,	__lowercase							:     Optional[int]=None       ,	):






							"""simple docstring"""






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

								if args_filename:
									args_files.append(Path(__lowercase						)						)
								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_		    =     ArgumentParser()
									args_file_parser.add_argument(__lowercase       ,	type=__lowercase       ,	action="append"						)

									# Use only remaining args for further parsing (remove the args_file_flag)
									snake_case_    ,      snake_case_		    =     args_file_parser.parse_known_args(args=__lowercase						)
									snake_case_		    =     vars(__lowercase						).get(args_file_flag.lstrip("-"						)       ,	__lowercase						)

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

								snake_case_		    =     []
								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_		    =     file_args + args if args is not None else file_args + sys.argv[1:]
							snake_case_    ,      snake_case_		    =     self.parse_known_args(args=__lowercase						)
							snake_case_		    =     []
							for dtype in self.dataclass_types:
								snake_case_		    =     {f.name for f in dataclasses.fields(__lowercase						) if f.init}
								snake_case_		    =     {k: v for k, v in vars(__lowercase						).items() if k in keys}
								for k in keys:
									delattr(__lowercase       ,	__lowercase						)
								snake_case_		    =     dtype(**__lowercase						)
								outputs.append(__lowercase						)
							if len(namespace.__dict__						) > 0:
								# additional namespace.
								outputs.append(__lowercase						)
							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        snake_case__						(						self							:     int       ,	__lowercase							:     Dict[str, Any]       ,	__lowercase							:     bool = False						):






							"""simple docstring"""






							snake_case_		    =     set(args.keys()						)
							snake_case_		    =     []
							for dtype in self.dataclass_types:
								snake_case_		    =     {f.name for f in dataclasses.fields(__lowercase						) if f.init}
								snake_case_		    =     {k: v for k, v in args.items() if k in keys}
								unused_keys.difference_update(inputs.keys()						)
								snake_case_		    =     dtype(**__lowercase						)
								outputs.append(__lowercase						)
							if not allow_extra_keys and unused_keys:
								raise ValueError(f"Some keys are not used by the HfArgumentParser: {sorted(__lowercase						)}"						)
							return tuple(__lowercase						)






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     str       ,	__lowercase							:     bool = False						):






							"""simple docstring"""






							with open(Path(__lowercase						)       ,	encoding="utf-8"						) as open_json_file:
								snake_case_		    =     json.loads(open_json_file.read()						)
							snake_case_		    =     self.parse_dict(__lowercase       ,	allow_extra_keys=__lowercase						)
							return tuple(__lowercase						)






						def        snake_case__						(						self							:     int       ,	__lowercase							:     str       ,	__lowercase							:     bool = False						):






							"""simple docstring"""






							snake_case_		    =     self.parse_dict(yaml.safe_load(Path(__lowercase						).read_text()						)       ,	allow_extra_keys=__lowercase						)
							return tuple(__lowercase						)
