
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


SCREAMING_SNAKE_CASE				:int									=							logging.get_logger(__name__)





class 				UpperCAmelCase (    _a    ):




	'''simple docstring'''





	snake_case_          =	["""pixel_values"""]






	def __init__(			self   :					Tuple				,A   :					Tuple = True				,A   :					Union[str, Any] = None				,A   :					Optional[int] = PILImageResampling.BILINEAR				,A   :					Tuple = True				,A   :					Tuple = 1 / 2_55				,A   :					Dict = True				,A   :					Union[str, Any] = None				,A   :					Optional[Any] = True				,**A   :					Dict				,):
					super().__init__(**A					)
					__A       =					size if size is not None else {'shortest_edge': 2_24}
					__A       =					get_size_dict(A				,default_to_square=A					)
					__A       =					crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
					__A       =					get_size_dict(A				,param_name="crop_size"					)

					__A       =					do_resize
					__A       =					size
					__A       =					resample
					__A       =					do_rescale
					__A       =					rescale_factor
					__A       =					do_center_crop
					__A       =					crop_size
					__A       =					do_flip_channel_order






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Tuple				,A   :					Any				,A   :					Tuple = PIL.Image.BILINEAR				,A   :					List[Any] = None				,**A   :					Union[str, Any]				,):
					__A       =					get_size_dict(A				,default_to_square=A					)
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''					)
					__A       =					get_resize_output_image_size(A				,size=size["shortest_edge"]				,default_to_square=A					)
					return resize(A				,size=A				,resample=A				,data_format=A				,**A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					int				,A   :					Dict				,A   :					Dict = None				,**A   :					Any				,):
					__A       =					get_size_dict(A					)
					if "height" not in size or "width" not in size:
									raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
					return center_crop(A				,size=(size["height"], size["width"])				,data_format=A				,**A					)






	def 							UpperCamelCase_    (			self   :					str				,A   :					Any				,A   :					Tuple				,A   :					Any = None				,**A   :					List[Any]				,):
					return rescale(A				,scale=A				,data_format=A				,**A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					str				,A   :					int = None					):
					return flip_channel_order(A				,data_format=A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					int				,A   :					List[Any] = None				,A   :					Any = None				,A   :					Optional[Any] = None				,A   :					List[str] = None				,A   :					List[str] = None				,A   :					List[Any] = None				,A   :					Union[str, Any] = None				,A   :					Optional[Any] = None				,A   :					Dict = None				,A   :					Dict = ChannelDimension.FIRST				,**A   :					Optional[Any]				,):
					__A       =					do_resize if do_resize is not None else self.do_resize
					__A       =					resample if resample is not None else self.resample
					__A       =					do_rescale if do_rescale is not None else self.do_rescale
					__A       =					rescale_factor if rescale_factor is not None else self.rescale_factor
					__A       =					do_center_crop if do_center_crop is not None else self.do_center_crop
					__A       =					(
					    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
					)

					__A       =					size if size is not None else self.size
					__A       =					get_size_dict(A				,default_to_square=A					)
					__A       =					crop_size if crop_size is not None else self.crop_size
					__A       =					get_size_dict(A				,param_name="crop_size"					)

					__A       =					make_list_of_images(A					)

					if not valid_images(A					):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."					)

					if do_resize and size is None:
									raise ValueError("Size must be specified if do_resize is True."					)

					if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."					)

					if do_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True."					)

					# All transformations expect numpy arrays.
					__A       =					[to_numpy_array(A					) for image in images]

					if do_resize:
									__A       =					[self.resize(image=A				,size=A				,resample=A					) for image in images]

					if do_center_crop:
									__A       =					[self.center_crop(image=A				,size=A					) for image in images]

					if do_rescale:
									__A       =					[self.rescale(image=A				,scale=A					) for image in images]

					# the pretrained checkpoints assume images are BGR, not RGB
					if do_flip_channel_order:
									__A       =					[self.flip_channel_order(image=A					) for image in images]

					__A       =					[to_channel_dimension_format(A				,A					) for image in images]

					__A       =					{'pixel_values': images}
					return BatchFeature(data=A				,tensor_type=A					)







	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					Optional[int]				,A   :					List[Any] = None					):
					__A       =					outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
									if len(A					) != len(A					):
													raise ValueError(
													    "Make sure that you pass in as many target sizes as the batch dimension of the logits"					)

									if is_torch_tensor(A					):
													__A       =					target_sizes.numpy()

									__A       =					[]

									for idx in range(len(A					)					):
													__A       =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0					)				,size=target_sizes[idx]				,mode="bilinear"				,align_corners=A					)
													__A       =					resized_logits[0].argmax(dim=0					)
													semantic_segmentation.append(A					)
					else:
									__A       =					logits.argmax(dim=1					)
									__A       =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

					return semantic_segmentation




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_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    _lowercase			:				Tuple 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Any 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Dict 						=						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:
            _lowercase			:				Any 						=						[3, 3, 3, 3]
            _lowercase			:				Any 						=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowercase			:				Dict 						=						[4, 4, 4, 4]
            _lowercase			:				Tuple 						=						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowercase			:				str 						=						[3, 3, 3, 3]
        if "lrf" in model_name:
            _lowercase			:				Optional[int] 						=						[3, 3, 3, 3]
        else:
            _lowercase			:				Dict 						=						[2, 2, 2, 2]

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

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

    _lowercase			:				Union[str, Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    _lowercase			:				Optional[Any] 						=						FocalNetConfig(
        embed_dim=lowerCamelCase_				,					depths=lowerCamelCase_				,					focal_levels=lowerCamelCase_				,					focal_windows=lowerCamelCase_				,					use_conv_embed=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid=lowerCamelCase_				,					use_post_layernorm=lowerCamelCase_				,					use_layerscale=lowerCamelCase_				,					)

    return config



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

    if name == "norm.weight":
        _lowercase			:				Any 						=						'layernorm.weight'
    if name == "norm.bias":
        _lowercase			:				Tuple 						=						'layernorm.bias'

    if "head" in name:
        _lowercase			:				Optional[int] 						=						name.replace('head'				,					'classifier'					)
    else:
        _lowercase			:				Optional[int] 						=						'focalnet.' + name

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    # fmt: off
    _lowercase			:				Dict 						=						{
        '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

    _lowercase			:				Dict 						=						model_name_to_url[model_name]
    print('Checkpoint URL: '				,					lowerCamelCase_					)
    _lowercase			:				List[str] 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    # rename keys
    for key in state_dict.copy().keys():
        _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						val

    _lowercase			:				Union[str, Any] 						=						get_focalnet_config(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						FocalNetForImageClassification(lowerCamelCase_					)
    model.eval()

    # load state dict
    model.load_state_dict(lowerCamelCase_					)

    # verify conversion
    _lowercase			:				Optional[Any] 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

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

    _lowercase			:				str 						=						transforms.Compose(
        [
            transforms.Resize(256					),
            transforms.CenterCrop(224					),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,					std=[0.2_29, 0.2_24, 0.2_25]					),
        ]					)

    _lowercase			:				List[str] 						=						image_transforms(lowerCamelCase_					).unsqueeze(0					)

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

    _lowercase			:				Dict 						=						model(**lowerCamelCase_					)

    _lowercase			:				int 						=						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":
        _lowercase			:				Optional[Any] 						=						torch.tensor([0.21_66, -0.43_68, 0.21_91]					)
    elif model_name == "focalnet-tiny-lrf":
        _lowercase			:				int 						=						torch.tensor([1.16_69, 0.01_25, -0.16_95]					)
    elif model_name == "focalnet-small":
        _lowercase			:				str 						=						torch.tensor([0.49_17, -0.04_30, 0.13_41]					)
    elif model_name == "focalnet-small-lrf":
        _lowercase			:				Any 						=						torch.tensor([-0.25_88, -0.53_42, -0.23_31]					)
    elif model_name == "focalnet-base":
        _lowercase			:				List[Any] 						=						torch.tensor([-0.16_55, -0.40_90, -0.17_30]					)
    elif model_name == "focalnet-base-lrf":
        _lowercase			:				int 						=						torch.tensor([0.53_06, -0.04_83, -0.39_28]					)
    assert torch.allclose(outputs.logits[0, :3]				,					lowerCamelCase_				,					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(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

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


