"""simple docstring"""
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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Tuple:
    _lowerCamelCase	: Dict       =       [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
    _lowerCamelCase	: str       =       True if '''large''' in model_name or '''huge''' in model_name else False
    _lowerCamelCase	: Tuple       =       True if '''large''' in model_name or '''huge''' in model_name else False
    _lowerCamelCase	: Tuple       =       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:
            _lowerCamelCase	: Any       =       [3, 3, 3, 3]
            _lowerCamelCase	: Tuple       =       [5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowerCamelCase	: Tuple       =       [4, 4, 4, 4]
            _lowerCamelCase	: Optional[int]       =       [3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowerCamelCase	: List[Any]       =       [3, 3, 3, 3]
        if "lrf" in model_name:
            _lowerCamelCase	: Union[str, Any]       =       [3, 3, 3, 3]
        else:
            _lowerCamelCase	: int       =       [2, 2, 2, 2]

    if "tiny" in model_name:
        _lowerCamelCase	: List[Any]       =       96
    elif "small" in model_name:
        _lowerCamelCase	: Dict       =       96
    elif "base" in model_name:
        _lowerCamelCase	: int       =       128
    elif "large" in model_name:
        _lowerCamelCase	: Union[str, Any]       =       192
    elif "xlarge" in model_name:
        _lowerCamelCase	: Tuple       =       256
    elif "huge" in model_name:
        _lowerCamelCase	: List[Any]       =       352

    # set label information
    _lowerCamelCase	: Optional[int]       =       '''huggingface/label-files'''
    if "large" in model_name or "huge" in model_name:
        _lowerCamelCase	: Any       =       '''imagenet-22k-id2label.json'''
    else:
        _lowerCamelCase	: Optional[int]       =       '''imagenet-1k-id2label.json'''

    _lowerCamelCase	: List[str]       =       json.load(open(hf_hub_download(UpperCamelCase__						,	UpperCamelCase__						,	repo_type='''dataset''' )						,	'''r''' ) )
    _lowerCamelCase	: List[str]       =       {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
    _lowerCamelCase	: int       =       {v: k for k, v in idalabel.items()}

    _lowerCamelCase	: Union[str, Any]       =       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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Any:







    if "patch_embed.proj" in name:
        _lowerCamelCase	: Optional[int]       =       name.replace('''patch_embed.proj'''						,	'''embeddings.patch_embeddings.projection''' )
    if "patch_embed.norm" in name:
        _lowerCamelCase	: List[str]       =       name.replace('''patch_embed.norm'''						,	'''embeddings.norm''' )
    if "layers" in name:
        _lowerCamelCase	: Union[str, Any]       =       '''encoder.''' + name
    if "encoder.layers" in name:
        _lowerCamelCase	: Dict       =       name.replace('''encoder.layers'''						,	'''encoder.stages''' )
    if "downsample.proj" in name:
        _lowerCamelCase	: Optional[Any]       =       name.replace('''downsample.proj'''						,	'''downsample.projection''' )
    if "blocks" in name:
        _lowerCamelCase	: Optional[int]       =       name.replace('''blocks'''						,	'''layers''' )
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        _lowerCamelCase	: Optional[Any]       =       name.replace('''modulation.f'''						,	'''modulation.projection_in''' )
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        _lowerCamelCase	: Any       =       name.replace('''modulation.h'''						,	'''modulation.projection_context''' )
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        _lowerCamelCase	: int       =       name.replace('''modulation.proj'''						,	'''modulation.projection_out''' )

    if name == "norm.weight":
        _lowerCamelCase	: List[str]       =       '''layernorm.weight'''
    if name == "norm.bias":
        _lowerCamelCase	: List[str]       =       '''layernorm.bias'''

    if "head" in name:
        _lowerCamelCase	: Dict       =       name.replace('''head'''						,	'''classifier''' )
    else:
        _lowerCamelCase	: List[Any]       =       '''focalnet.''' + name

    return name



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_=False )      ->Dict:
    _lowerCamelCase	: Tuple       =       {
        '''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

    _lowerCamelCase	: List[str]       =       model_name_to_url[model_name]
    print('''Checkpoint URL: '''						,	UpperCamelCase__ )
    _lowerCamelCase	: List[Any]       =       torch.hub.load_state_dict_from_url(UpperCamelCase__						,	map_location='''cpu''' )['''model''']

    # rename keys
    for key in state_dict.copy().keys():
        _lowerCamelCase	: Union[str, Any]       =       state_dict.pop(UpperCamelCase__ )
        _lowerCamelCase	: str       =       val

    _lowerCamelCase	: Union[str, Any]       =       get_focalnet_config(UpperCamelCase__ )
    _lowerCamelCase	: Union[str, Any]       =       FocalNetForImageClassification(UpperCamelCase__ )
    model.eval()

    # load state dict
    model.load_state_dict(UpperCamelCase__ )

    # verify conversion
    _lowerCamelCase	: str       =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''

    _lowerCamelCase	: Dict       =       BitImageProcessor(
        do_resize=UpperCamelCase__						,	size={'''shortest_edge''': 256}						,	resample=PILImageResampling.BILINEAR						,	do_center_crop=UpperCamelCase__						,	crop_size=224						,	do_normalize=UpperCamelCase__						,	image_mean=UpperCamelCase__						,	image_std=UpperCamelCase__						,	)
    _lowerCamelCase	: int       =       Image.open(requests.get(UpperCamelCase__						,	stream=UpperCamelCase__ ).raw )
    _lowerCamelCase	: List[Any]       =       processor(images=UpperCamelCase__						,	return_tensors='''pt''' )

    _lowerCamelCase	: List[str]       =       transforms.Compose(
        [
            transforms.Resize(256 ),
            transforms.CenterCrop(224 ),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406]						,	std=[0.229, 0.224, 0.225] ),
        ] )

    _lowerCamelCase	: int       =       image_transforms(UpperCamelCase__ ).unsqueeze(0 )

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

    _lowerCamelCase	: Union[str, Any]       =       model(**UpperCamelCase__ )

    _lowerCamelCase	: str       =       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":
        _lowerCamelCase	: Dict       =       torch.tensor([0.2166, -0.4368, 0.2191] )
    elif model_name == "focalnet-tiny-lrf":
        _lowerCamelCase	: int       =       torch.tensor([1.1669, 0.0125, -0.1695] )
    elif model_name == "focalnet-small":
        _lowerCamelCase	: Tuple       =       torch.tensor([0.4917, -0.0430, 0.1341] )
    elif model_name == "focalnet-small-lrf":
        _lowerCamelCase	: List[Any]       =       torch.tensor([-0.2588, -0.5342, -0.2331] )
    elif model_name == "focalnet-base":
        _lowerCamelCase	: int       =       torch.tensor([-0.1655, -0.4090, -0.1730] )
    elif model_name == "focalnet-base-lrf":
        _lowerCamelCase	: Optional[int]       =       torch.tensor([0.5306, -0.0483, -0.3928] )
    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__":
  SCREAMING_SNAKE_CASE__   :      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.',
  )

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






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:   Any						=						logging.get_logger(__name__)

_lowercase							:   Union[str, Any]						=						{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'efficientformer'

				def __init__(    self					, a__ = [3, 2, 6, 4]					, a__ = [48, 96, 224, 448]					, a__ = [True, True, True, True]					, a__ = 448					, a__ = 32					, a__ = 4					, a__ = 7					, a__ = 5					, a__ = 8					, a__ = 4					, a__ = 0.0					, a__ = 16					, a__ = 3					, a__ = 3					, a__ = 3					, a__ = 2					, a__ = 1					, a__ = 0.0					, a__ = 1					, a__ = True					, a__ = True					, a__ = 1e-5					, a__ = "gelu"					, a__ = 0.02					, a__ = 1e-12					, a__ = 224					, a__ = 1e-05					, **a__					, )						->					None:
					super().__init__(**a__ )

					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					hidden_sizes
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					initializer_range
					A             =					layer_norm_eps
					A             =					patch_size
					A             =					num_channels
					A             =					depths
					A             =					mlp_expansion_ratio
					A             =					downsamples
					A             =					dim
					A             =					key_dim
					A             =					attention_ratio
					A             =					resolution
					A             =					pool_size
					A             =					downsample_patch_size
					A             =					downsample_stride
					A             =					downsample_pad
					A             =					drop_path_rate
					A             =					num_metaad_blocks
					A             =					distillation
					A             =					use_layer_scale
					A             =					layer_scale_init_value
					A             =					image_size
					A             =					batch_norm_eps



