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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
						SCREAMING_SNAKE_CASE							:					Dict           =							True if 'large' in model_name or 'huge' in model_name else False
						SCREAMING_SNAKE_CASE							:					Any           =							True if 'large' in model_name or 'huge' in model_name else False
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[3, 3, 3, 3]
																		SCREAMING_SNAKE_CASE							:					int           =							[5, 5, 5, 5]
												elif "fl4" in model_name:
																		SCREAMING_SNAKE_CASE							:					Any           =							[4, 4, 4, 4]
																		SCREAMING_SNAKE_CASE							:					Any           =							[3, 3, 3, 3]

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

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

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

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

						SCREAMING_SNAKE_CASE							:					Tuple           =							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    A			(					_lowercase		):







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

						if name == "norm.weight":
												SCREAMING_SNAKE_CASE							:					List[str]           =							'layernorm.weight'
						if name == "norm.bias":
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'layernorm.bias'

						if "head" in name:
												SCREAMING_SNAKE_CASE							:					str           =							name.replace('''head'''	,						'''classifier'''		)
						else:
												SCREAMING_SNAKE_CASE							:					Tuple           =							'focalnet.' + name

						return name
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							{
						    '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

						SCREAMING_SNAKE_CASE							:					Tuple           =							model_name_to_url[model_name]
						print('''Checkpoint URL: '''	,						lowerCAmelCase__		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.hub.load_state_dict_from_url(lowerCAmelCase__	,						map_location='''cpu'''		)['model']

						# rename keys
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(lowerCAmelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							val

						SCREAMING_SNAKE_CASE							:					List[Any]           =							get_focalnet_config(lowerCAmelCase__		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							FocalNetForImageClassification(lowerCAmelCase__		)
						model.eval()

						# load state dict
						model.load_state_dict(lowerCAmelCase__		)

						# verify conversion
						SCREAMING_SNAKE_CASE							:					Dict           =							'http://images.cocodataset.org/val2017/000000039769.jpg'

						SCREAMING_SNAKE_CASE							:					Union[str, 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__	,						)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							Image.open(requests.get(lowerCAmelCase__	,						stream=lowerCAmelCase__		).raw		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							processor(images=lowerCAmelCase__	,						return_tensors='''pt'''		)

						SCREAMING_SNAKE_CASE							:					Dict           =							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]		),
						    ]		)

						SCREAMING_SNAKE_CASE							:					str           =							image_transforms(lowerCAmelCase__		).unsqueeze(0		)

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

						SCREAMING_SNAKE_CASE							:					Any           =							model(**lowerCAmelCase__		)

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

					__UpperCamelCase	: int    		=  parser.parse_args()
					convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int = 1000					,	UpperCamelCase__			:       Optional[Union[np.ndarray, List[float]]] = None		):





												'''simple docstring'''





												self.set_timesteps(UpperCamelCase__		)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					str           =							1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												SCREAMING_SNAKE_CASE							:					Tuple           =							4

												# running values
												SCREAMING_SNAKE_CASE							:					int           =							[]


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, torch.device] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.linspace(1					,	0					,	num_inference_steps + 1		)[:-1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cat([steps, torch.tensor([0.0]		)]		)

												if self.config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(self.config.trained_betas					,	dtype=torch.floataa		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sin(steps * math.pi / 2		) ** 2

												SCREAMING_SNAKE_CASE							:					Dict           =							(1.0 - self.betas**2) ** 0.5

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(torch.atana(self.betas					,	self.alphas		) / math.pi * 2)[:-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							timesteps.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       bool = True					,	):





												'''simple docstring'''





												if self.num_inference_steps is None:
																		raise ValueError(
																		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.timesteps == timestep).nonzero().item()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timestep_index + 1

												SCREAMING_SNAKE_CASE							:					int           =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(UpperCamelCase__		)

												if len(self.ets		) == 1:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.ets[-1]
												elif len(self.ets		) == 2:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets		) == 3:
																		SCREAMING_SNAKE_CASE							:					str           =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._get_prev_sample(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       torch.FloatTensor					,	*UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return sample


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.alphas[timestep_index]
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.betas[timestep_index]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.alphas[prev_timestep_index]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.betas[prev_timestep_index]

												SCREAMING_SNAKE_CASE							:					Dict           =							(sample - sigma * ets) / max(UpperCamelCase__					,	1E-8		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							next_alpha * pred + ets * next_sigma

												return prev_sample



						def __len__( self			:       int		):





												'''simple docstring'''





												return self.config.num_train_timesteps





