


import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> Tuple:





	"""simple docstring"""







	A             =					[2, 2, 6, 2] if """tiny""" in model_name else [2, 2, 18, 2]
	A             =					True if """large""" in model_name or """huge""" in model_name else False
	A             =					True if """large""" in model_name or """huge""" in model_name else False
	A             =					True if """large""" in model_name or """huge""" in model_name else False

	if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
		if "fl3" in model_name:
			A             =					[3, 3, 3, 3]
			A             =					[5, 5, 5, 5]
		elif "fl4" in model_name:
			A             =					[4, 4, 4, 4]
			A             =					[3, 3, 3, 3]

	if "tiny" in model_name or "small" in model_name or "base" in model_name:
		A             =					[3, 3, 3, 3]
		if "lrf" in model_name:
			A             =					[3, 3, 3, 3]
		else:
			A             =					[2, 2, 2, 2]

	if "tiny" in model_name:
		A             =					96
	elif "small" in model_name:
		A             =					96
	elif "base" in model_name:
		A             =					1_28
	elif "large" in model_name:
		A             =					1_92
	elif "xlarge" in model_name:
		A             =					2_56
	elif "huge" in model_name:
		A             =					3_52

	# set label information
	A             =					"""huggingface/label-files"""
	if "large" in model_name or "huge" in model_name:
		A             =					"""imagenet-22k-id2label.json"""
	else:
		A             =					"""imagenet-1k-id2label.json"""

	A             =					json.load(open(hf_hub_download(UpperCamelCase__				,       UpperCamelCase__				,       repo_type="""dataset"""    )				,       """r"""    )    )
	A             =					{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
	A             =					{v: k for k, v in idalabel.items()}

	A             =					FocalNetConfig(
	    embed_dim=UpperCamelCase__				,       depths=UpperCamelCase__				,       focal_levels=UpperCamelCase__				,       focal_windows=UpperCamelCase__				,       use_conv_embed=UpperCamelCase__				,       idalabel=UpperCamelCase__				,       labelaid=UpperCamelCase__				,       use_post_layernorm=UpperCamelCase__				,       use_layerscale=UpperCamelCase__				,       )

	return config





def 	_lowerCAmelCase					( UpperCamelCase__:  List[str]    )			-> Any:





	"""simple docstring"""







	if "patch_embed.proj" in name:
		A             =					name.replace("""patch_embed.proj"""				,       """embeddings.patch_embeddings.projection"""    )
	if "patch_embed.norm" in name:
		A             =					name.replace("""patch_embed.norm"""				,       """embeddings.norm"""    )
	if "layers" in name:
		A             =					"""encoder.""" + name
	if "encoder.layers" in name:
		A             =					name.replace("""encoder.layers"""				,       """encoder.stages"""    )
	if "downsample.proj" in name:
		A             =					name.replace("""downsample.proj"""				,       """downsample.projection"""    )
	if "blocks" in name:
		A             =					name.replace("""blocks"""				,       """layers"""    )
	if "modulation.f.weight" in name or "modulation.f.bias" in name:
		A             =					name.replace("""modulation.f"""				,       """modulation.projection_in"""    )
	if "modulation.h.weight" in name or "modulation.h.bias" in name:
		A             =					name.replace("""modulation.h"""				,       """modulation.projection_context"""    )
	if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
		A             =					name.replace("""modulation.proj"""				,       """modulation.projection_out"""    )

	if name == "norm.weight":
		A             =					"""layernorm.weight"""
	if name == "norm.bias":
		A             =					"""layernorm.bias"""

	if "head" in name:
		A             =					name.replace("""head"""				,       """classifier"""    )
	else:
		A             =					"""focalnet.""" + name

	return name





def 	_lowerCAmelCase					( UpperCamelCase__:  Dict				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]=False    )			-> Dict:





	"""simple docstring"""







	A             =					{
	    """focalnet-tiny""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth""",
	    """focalnet-tiny-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth""",
	    """focalnet-small""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth""",
	    """focalnet-small-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth""",
	    """focalnet-base""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth""",
	    """focalnet-base-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth""",
	    """focalnet-large-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth""",
	    """focalnet-large-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth""",
	    """focalnet-xlarge-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth""",
	    """focalnet-xlarge-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth""",
	}
	# fmt: on

	A             =					model_name_to_url[model_name]
	print("""Checkpoint URL: """				,       UpperCamelCase__    )
	A             =					torch.hub.load_state_dict_from_url(UpperCamelCase__				,       map_location="""cpu"""    )["""model"""]

	# rename keys
	for key in state_dict.copy().keys():
		A             =					state_dict.pop(UpperCamelCase__    )
		A             =					val

	A             =					get_focalnet_config(UpperCamelCase__    )
	A             =					FocalNetForImageClassification(UpperCamelCase__    )
	model.eval()

	# load state dict
	model.load_state_dict(UpperCamelCase__    )

	# verify conversion
	A             =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""

	A             =					BitImageProcessor(
	    do_resize=UpperCamelCase__				,       size={"""shortest_edge""": 2_56}				,       resample=PILImageResampling.BILINEAR				,       do_center_crop=UpperCamelCase__				,       crop_size=2_24				,       do_normalize=UpperCamelCase__				,       image_mean=UpperCamelCase__				,       image_std=UpperCamelCase__				,       )
	A             =					Image.open(requests.get(UpperCamelCase__				,       stream=UpperCamelCase__    ).raw    )
	A             =					processor(images=UpperCamelCase__				,       return_tensors="""pt"""    )

	A             =					transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]    ),
	    ]    )

	A             =					image_transforms(UpperCamelCase__    ).unsqueeze(0    )

	# verify pixel_values
	assert torch.allclose(inputs.pixel_values				,       UpperCamelCase__				,       atol=1e-4    )

	A             =					model(**UpperCamelCase__    )

	A             =					outputs.logits.argmax(-1    ).item()
	print("""Predicted class:"""				,       model.config.idalabel[predicted_class_idx]    )

	print("""First values of logits:"""				,       outputs.logits[0, :3]    )

	if model_name == "focalnet-tiny":
		A             =					torch.tensor([0.21_66, -0.43_68, 0.21_91]    )
	elif model_name == "focalnet-tiny-lrf":
		A             =					torch.tensor([1.16_69, 0.01_25, -0.16_95]    )
	elif model_name == "focalnet-small":
		A             =					torch.tensor([0.49_17, -0.04_30, 0.13_41]    )
	elif model_name == "focalnet-small-lrf":
		A             =					torch.tensor([-0.25_88, -0.53_42, -0.23_31]    )
	elif model_name == "focalnet-base":
		A             =					torch.tensor([-0.16_55, -0.40_90, -0.17_30]    )
	elif model_name == "focalnet-base-lrf":
		A             =					torch.tensor([0.53_06, -0.04_83, -0.39_28]    )
	assert torch.allclose(outputs.logits[0, :3]				,       UpperCamelCase__				,       atol=1e-4    )
	print("""Looks ok!"""    )

	if pytorch_dump_folder_path is not None:
		print(f'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'    )
		model.save_pretrained(UpperCamelCase__    )
		processor.save_pretrained(UpperCamelCase__    )

	if push_to_hub:
		print(f'Pushing model and processor of {model_name} to the hub...'    )
		model.push_to_hub(f'{model_name}'    )
		processor.push_to_hub(f'{model_name}'    )


if __name__ == "__main__":
				_lowercase							:   Union[str, Any]						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="focalnet-tiny",
				    type=str,
				    help="Name of the FocalNet 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 and processor to the hub.",
				)

				_lowercase							:   str						=						parser.parse_args()
				convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				_lowercase							:   List[str]						=						None

_lowercase							:   List[str]						=						logging.get_logger(__name__)
_lowercase							:   List[Any]						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase							:   str						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase							:   List[str]						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase							:   Dict						=						"▁"






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			AlbertTokenizer

				def __init__(    self					, a__=None					, a__=None					, a__=True					, a__=True					, a__=False					, a__="[CLS]"					, a__="[SEP]"					, a__="<unk>"					, a__="[SEP]"					, a__="<pad>"					, a__="[CLS]"					, a__="[MASK]"					, **a__					, )						->					Dict:
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					A             =					(
					    AddedToken(a__					, lstrip=a__					, rstrip=a__					, normalized=a__ )
					    if isinstance(a__					, a__ )
					    else mask_token
					)

					super().__init__(
					    a__					, tokenizer_file=a__					, do_lower_case=a__					, remove_space=a__					, keep_accents=a__					, bos_token=a__					, eos_token=a__					, unk_token=a__					, sep_token=a__					, pad_token=a__					, cls_token=a__					, mask_token=a__					, **a__					, )

					A             =					do_lower_case
					A             =					remove_space
					A             =					keep_accents
					A             =					vocab_file
					A             =					False if not self.vocab_file else True

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[self.cls_token_id]
					if token_ids_a is None:
						return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[self.cls_token_id]

					if token_ids_a is None:
						return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					Tuple[str]:
					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
						    """tokenizer.""" )

					if not os.path.isdir(a__ ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
						return
					A             =					os.path.join(
					    a__					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ):
						copyfile(self.vocab_file					, a__ )

					return (out_vocab_file,)



