






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()
UpperCamelCase			    =     logging.get_logger(__name__)

def 	__lowerCamelCase      (		snake_case__		)							->  Dict:






						"""simple docstring"""





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

						_SCREAMING_SNAKE_CASE     				=  """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"
						_SCREAMING_SNAKE_CASE     				=  BitConfig(
						    conv_layer=snake_case__	,num_labels=10_00	,idalabel=snake_case__	,labelaid=snake_case__	,)

						return config

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[int]:






						"""simple docstring"""





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

						return name

def 	__lowerCamelCase      (		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_SCREAMING_SNAKE_CASE     				=  Image.open(requests.get(snake_case__	,stream=snake_case__		).raw		)
						return im

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__=False		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  get_config(snake_case__		)

						# load original model from timm
						_SCREAMING_SNAKE_CASE     				=  create_model(snake_case__	,pretrained=snake_case__		)
						timm_model.eval()

						# load state_dict of original model
						_SCREAMING_SNAKE_CASE     				=  timm_model.state_dict()
						for key in state_dict.copy().keys():
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(snake_case__		)
												_SCREAMING_SNAKE_CASE     				=  val.squeeze() if """head""" in key else val

						# load HuggingFace model
						_SCREAMING_SNAKE_CASE     				=  BitForImageClassification(snake_case__		)
						model.eval()
						model.load_state_dict(snake_case__		)

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

						_SCREAMING_SNAKE_CASE     				=  {
						    """bilinear""": PILImageResampling.BILINEAR,
						    """bicubic""": PILImageResampling.BICUBIC,
						    """nearest""": PILImageResampling.NEAREST,
						}

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

						_SCREAMING_SNAKE_CASE     				=  prepare_img()
						_SCREAMING_SNAKE_CASE     				=  transform(snake_case__		).unsqueeze(0		)
						_SCREAMING_SNAKE_CASE     				=  processor(snake_case__	,return_tensors="""pt"""		).pixel_values

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

						# verify logits
						with torch.no_grad():
												_SCREAMING_SNAKE_CASE     				=  model(snake_case__		)
												_SCREAMING_SNAKE_CASE     				=  outputs.logits

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

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

						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__":
		UpperCamelCase			    =     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.''',
		)

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









import copy
import re



class 				__UpperCAmelCase		:
			__snake_case    :				Any									=							"hp"
			__snake_case    :				str									=							{}
			__snake_case    :				List[Any]									=							None







			@classmethod
			def 							UpperCamelCase				(     cls:  Optional[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  prefix
									_SCREAMING_SNAKE_CASE     				=  defaults
									cls.build_naming_info()







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									if len(UpperCAmelCase_					) == 0:
															return ""
									_SCREAMING_SNAKE_CASE     				=  None
									if any(char.isdigit() for char in word					):
															raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number'					)
									if word in info["short_word"]:
															return info["short_word"][word]
									for prefix_len in range(1				,     len(UpperCAmelCase_					) + 1					):
															_SCREAMING_SNAKE_CASE     				=  word[:prefix_len]
															if prefix in info["reverse_short_word"]:
																					continue
															else:
																					_SCREAMING_SNAKE_CASE     				=  prefix
																					break

									if short_word is None:
															# Paranoid fallback
															def int_to_alphabetic(UpperCAmelCase_:  List[Any]					):
																					_SCREAMING_SNAKE_CASE     				=  """"""
																					while integer != 0:
																											_SCREAMING_SNAKE_CASE     				=  chr(ord("""A"""					) + integer % 10					) + s
																											integer //= 10
																					return s

															_SCREAMING_SNAKE_CASE     				=  0
															while True:
																					_SCREAMING_SNAKE_CASE     				=  word + """#""" + int_to_alphabetic(UpperCAmelCase_					)
																					if sword in info["reverse_short_word"]:
																											continue
																					else:
																											_SCREAMING_SNAKE_CASE     				=  sword
																											break

									_SCREAMING_SNAKE_CASE     				=  short_word
									_SCREAMING_SNAKE_CASE     				=  word
									return short_word







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  param_name.split("""_"""					)

									_SCREAMING_SNAKE_CASE     				=  [TrialShortNamer.shortname_for_word(UpperCAmelCase_				,     UpperCAmelCase_					) for word in words]

									# We try to create a separatorless short name, but if there is a collision we have to fallback
									# to a separated short name
									_SCREAMING_SNAKE_CASE     				=  ["""""", """_"""]

									for separator in separators:
															_SCREAMING_SNAKE_CASE     				=  separator.join(UpperCAmelCase_					)
															if shortname not in info["reverse_short_param"]:
																					_SCREAMING_SNAKE_CASE     				=  shortname
																					_SCREAMING_SNAKE_CASE     				=  param_name
																					return shortname

									return param_name







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TrialShortNamer.shortname_for_key(UpperCAmelCase_				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  short_name
									_SCREAMING_SNAKE_CASE     				=  param_name







			@classmethod
			def 							UpperCamelCase				(     cls:  str					):







									'''simple docstring'''




									if cls.NAMING_INFO is not None:
															return

									_SCREAMING_SNAKE_CASE     				=  {
									    """short_word""": {},
									    """reverse_short_word""": {},
									    """short_param""": {},
									    """reverse_short_param""": {},
									}

									_SCREAMING_SNAKE_CASE     				=  list(cls.DEFAULTS.keys()					)

									for k in field_keys:
															cls.add_new_param_name(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  info







			@classmethod
			def 							UpperCamelCase				(     cls:  Any				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									cls.build_naming_info()
									assert cls.PREFIX is not None
									_SCREAMING_SNAKE_CASE     				=  [copy.copy(cls.PREFIX					)]

									for k, v in params.items():
															if k not in cls.DEFAULTS:
																					raise Exception(F'You should provide a default value for the param name {k} with value {v}'					)
															if v == cls.DEFAULTS[k]:
																					# The default value is not added to the name
																					continue

															_SCREAMING_SNAKE_CASE     				=  cls.NAMING_INFO["""short_param"""][k]

															if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  1 if v else 0

															_SCREAMING_SNAKE_CASE     				=  """""" if isinstance(UpperCAmelCase_				,     (int, float)					) else """-"""
															_SCREAMING_SNAKE_CASE     				=  F'{key}{sep}{v}'
															name.append(UpperCAmelCase_					)

									return "_".join(UpperCAmelCase_					)







			@classmethod
			def 							UpperCamelCase				(     cls:  int				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  repr[len(cls.PREFIX					) + 1 :]
									if repr == "":
															_SCREAMING_SNAKE_CASE     				=  []
									else:
															_SCREAMING_SNAKE_CASE     				=  repr.split("""_"""					)

									_SCREAMING_SNAKE_CASE     				=  {}

									for value in values:
															if "-" in value:
																					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  value.split("""-"""					)
															else:
																					_SCREAMING_SNAKE_CASE     				=  re.sub("""[0-9.]"""				,     """"""				,     UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  float(re.sub("""[^0-9.]"""				,     """"""				,     UpperCAmelCase_					)					)

															_SCREAMING_SNAKE_CASE     				=  cls.NAMING_INFO["""reverse_short_param"""][p_k]

															_SCREAMING_SNAKE_CASE     				=  p_v

									for k in cls.DEFAULTS:
															if k not in parameters:
																					_SCREAMING_SNAKE_CASE     				=  cls.DEFAULTS[k]

									return parameters

