from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase	: Dict    		=  logging.get_logger(__name__)


__UpperCamelCase	: List[str]    		=  {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
__UpperCamelCase	: Union[str, Any]    		=  111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__UpperCamelCase	: str    		=  0

__UpperCamelCase	: Tuple    		=  0xe000
__UpperCamelCase	: Any    		=  0xe001
__UpperCamelCase	: List[str]    		=  0xe002
__UpperCamelCase	: Union[str, Any]    		=  0xe003
__UpperCamelCase	: Tuple    		=  0xe004

# Maps special codepoints to human-readable names.
__UpperCamelCase	: Dict[int, str]    		=  {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__UpperCamelCase	: Dict[str, int]    		=  {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       List[Any]=chr(UpperCamelCase__		)					,	UpperCamelCase__			:       Dict=chr(UpperCamelCase__		)					,	UpperCamelCase__			:       str=chr(UpperCamelCase__		)					,	UpperCamelCase__			:       List[Any]=chr(UpperCamelCase__		)					,	UpperCamelCase__			:       List[Any]=chr(UpperCamelCase__		)					,	UpperCamelCase__			:       List[Any]=chr(UpperCamelCase__		)					,	UpperCamelCase__			:       Optional[Any]=False					,	UpperCamelCase__			:       Dict=2048					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else bos_token
												SCREAMING_SNAKE_CASE							:					List[Any]           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else eos_token
												SCREAMING_SNAKE_CASE							:					int           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else sep_token
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else cls_token
												SCREAMING_SNAKE_CASE							:					Any           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else pad_token

												# Mask token behave like a normal word, i.e. include the space before it
												SCREAMING_SNAKE_CASE							:					Any           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else mask_token

												super().__init__(
												    bos_token=UpperCamelCase__					,	eos_token=UpperCamelCase__					,	sep_token=UpperCamelCase__					,	cls_token=UpperCamelCase__					,	pad_token=UpperCamelCase__					,	mask_token=UpperCamelCase__					,	add_prefix_space=UpperCamelCase__					,	model_max_length=UpperCamelCase__					,	**UpperCamelCase__					,	)

												# Creates a mapping for looking up the IDs of special symbols.
												SCREAMING_SNAKE_CASE							:					Dict[str, int]           =							{}
												for codepoint, name in SPECIAL_CODEPOINTS.items():
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							codepoint

												# Creates a mapping for looking up the string forms of special symbol IDs.
												SCREAMING_SNAKE_CASE							:					Dict[int, str]           =							{
												    codepoint: name for name, codepoint in self._special_codepoints.items()
												}

												SCREAMING_SNAKE_CASE							:					Any           =							UNICODE_VOCAB_SIZE
												SCREAMING_SNAKE_CASE							:					List[Any]           =							len(self._special_codepoints		)


						@property
						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return self._unicode_vocab_size


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												return list(UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												try:
																		return ord(UpperCamelCase__		)
												except TypeError:
																		raise ValueError(f"""invalid token: '{token}'"""		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												try:
																		if index in SPECIAL_CODEPOINTS:
																								return SPECIAL_CODEPOINTS[index]
																		return chr(UpperCamelCase__		)
												except TypeError:
																		raise ValueError(f"""invalid id: {index}"""		)


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												return "".join(UpperCamelCase__		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[self.cls_token_id]

												SCREAMING_SNAKE_CASE							:					Tuple           =							cls + token_ids_a + sep
												if token_ids_a is not None:
																		result += token_ids_a + sep
												return result


						def  __A ( self			:       str					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None					,	UpperCamelCase__			:       bool = False		):





												'''simple docstring'''





												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=UpperCamelCase__					,	token_ids_a=UpperCamelCase__					,	already_has_special_tokens=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							[1] + ([0] * len(UpperCamelCase__		)) + [1]
												if token_ids_a is not None:
																		result += ([0] * len(UpperCamelCase__		)) + [1]
												return result


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[self.cls_token_id]

												SCREAMING_SNAKE_CASE							:					Any           =							len(cls + token_ids_a + sep		) * [0]
												if token_ids_a is not None:
																		result += len(token_ids_a + sep		) * [1]
												return result



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





												return ()






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	: str    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					Any           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					Any           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{int(_lowercase		): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Dict           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					str           =							'''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							:					Optional[int]           =							BitConfig(
						    conv_layer=_lowercase	,						num_labels=1_000	,						idalabel=_lowercase	,						labelaid=_lowercase	,						)

						return config
def    A			(					_lowercase		):
						if "stem.conv" in name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							name.replace('''stem.conv'''	,						'''bit.embedder.convolution'''		)
						if "blocks" in name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							name.replace('''blocks'''	,						'''layers'''		)
						if "head.fc" in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							name.replace('''head.fc'''	,						'''classifier.1'''		)
						if name.startswith('''norm'''		):
												SCREAMING_SNAKE_CASE							:					str           =							'''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''bit.encoder.''' + name

						return name
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							get_config(_lowercase		)

						# load original model from timm
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_model(_lowercase	,						pretrained=_lowercase		)
						timm_model.eval()

						# load state_dict of original model
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							timm_model.state_dict()
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							val.squeeze() if '''head''' in key else val

						# load HuggingFace model
						SCREAMING_SNAKE_CASE							:					str           =							BitForImageClassification(_lowercase		)
						model.eval()
						model.load_state_dict(_lowercase		)

						# create image processor
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_transform(**resolve_data_config({}	,						model=_lowercase		)		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							transform.transforms

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

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

						SCREAMING_SNAKE_CASE							:					Any           =							prepare_img()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							transform(_lowercase		).unsqueeze(0		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							processor(_lowercase	,						return_tensors='''pt'''		).pixel_values

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

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Dict           =							model(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							outputs.logits

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

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

						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	: Any    		=  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	: Optional[int]    		=  parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





