


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

					if "tiny" in model_name or "small" in model_name or "base" in model_name:
										__a      :	List[str]    =  [3, 3, 3, 3]
										if "lrf" in model_name:
															__a      :	Dict    =  [3, 3, 3, 3]
										else:
															__a      :	Tuple    =  [2, 2, 2, 2]

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

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

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

					__a      :	str    =  FocalNetConfig(
					    embed_dim=_SCREAMING_SNAKE_CASE						,				depths=_SCREAMING_SNAKE_CASE						,				focal_levels=_SCREAMING_SNAKE_CASE						,				focal_windows=_SCREAMING_SNAKE_CASE						,				use_conv_embed=_SCREAMING_SNAKE_CASE						,				idalabel=_SCREAMING_SNAKE_CASE						,				labelaid=_SCREAMING_SNAKE_CASE						,				use_post_layernorm=_SCREAMING_SNAKE_CASE						,				use_layerscale=_SCREAMING_SNAKE_CASE						,				)

					return config






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

					if name == "norm.weight":
										__a      :	Any    =  'layernorm.weight'
					if name == "norm.bias":
										__a      :	List[Any]    =  'layernorm.bias'

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

					return name






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       Tuple=False       ):
					# fmt: off
					__a      :	int    =  {
					    '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      :	int    =  model_name_to_url[model_name]
					print('Checkpoint URL: '						,				_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,				map_location='cpu'       )['model']

					# rename keys
					for key in state_dict.copy().keys():
										__a      :	Optional[Any]    =  state_dict.pop(_SCREAMING_SNAKE_CASE       )
										__a      :	Optional[Any]    =  val

					__a      :	Optional[Any]    =  get_focalnet_config(_SCREAMING_SNAKE_CASE       )
					__a      :	Any    =  FocalNetForImageClassification(_SCREAMING_SNAKE_CASE       )
					model.eval()

					# load state dict
					model.load_state_dict(_SCREAMING_SNAKE_CASE       )

					# verify conversion
					__a      :	Union[str, Any]    =  'http://images.cocodataset.org/val2017/000000039769.jpg'

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

					__a      :	List[Any]    =  transforms.Compose(
					    [
					        transforms.Resize(256       ),
					        transforms.CenterCrop(224       ),
					        transforms.ToTensor(),
					        transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]						,				std=[0.2_2_9, 0.2_2_4, 0.2_2_5]       ),
					    ]       )

					__a      :	int    =  image_transforms(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )

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

					__a      :	Union[str, Any]    =  model(**_SCREAMING_SNAKE_CASE       )

					__a      :	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":
										__a      :	Dict    =  torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]       )
					elif model_name == "focalnet-tiny-lrf":
										__a      :	Optional[int]    =  torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]       )
					elif model_name == "focalnet-small":
										__a      :	Optional[Any]    =  torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]       )
					elif model_name == "focalnet-small-lrf":
										__a      :	List[str]    =  torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]       )
					elif model_name == "focalnet-base":
										__a      :	Tuple    =  torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]       )
					elif model_name == "focalnet-base-lrf":
										__a      :	Optional[Any]    =  torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]       )
					assert torch.allclose(outputs.logits[0, :3]						,				_SCREAMING_SNAKE_CASE						,				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(_SCREAMING_SNAKE_CASE       )
										processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

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







'''simple docstring'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[Any]	=     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



