
"""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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase_					=  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_					=  [3, 3, 3, 3]
									lowerCamelCase_					=  [5, 5, 5, 5]
						elif "fl4" in model_name:
									lowerCamelCase_					=  [4, 4, 4, 4]
									lowerCamelCase_					=  [3, 3, 3, 3]

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

			if "tiny" in model_name:
						lowerCamelCase_					=  96
			elif "small" in model_name:
						lowerCamelCase_					=  96
			elif "base" in model_name:
						lowerCamelCase_					=  128
			elif "large" in model_name:
						lowerCamelCase_					=  192
			elif "xlarge" in model_name:
						lowerCamelCase_					=  256
			elif "huge" in model_name:
						lowerCamelCase_					=  352

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

			lowerCamelCase_					=  json.load(open(hf_hub_download(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

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

			if name == "norm.weight":
						lowerCamelCase_					=  '''layernorm.weight'''
			if name == "norm.bias":
						lowerCamelCase_					=  '''layernorm.bias'''

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

			return name






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False						):
			# fmt: off
			lowerCamelCase_					=  {
			    '''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_					=  model_name_to_url[model_name]
			print('''Checkpoint URL: '''	,lowerCAmelCase__						)
			lowerCamelCase_					=  torch.hub.load_state_dict_from_url(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']

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

			lowerCamelCase_					=  get_focalnet_config(lowerCAmelCase__						)
			lowerCamelCase_					=  FocalNetForImageClassification(lowerCAmelCase__						)
			model.eval()

			# load state dict
			model.load_state_dict(lowerCAmelCase__						)

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

			lowerCamelCase_					=  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__	,)
			lowerCamelCase_					=  Image.open(requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__						).raw						)
			lowerCamelCase_					=  processor(images=lowerCAmelCase__	,return_tensors='''pt'''						)

			lowerCamelCase_					=  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_					=  image_transforms(lowerCAmelCase__						).unsqueeze(0						)

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

			lowerCamelCase_					=  model(**lowerCAmelCase__						)

			lowerCamelCase_					=  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_					=  torch.tensor([0.2_166, -0.4_368, 0.2_191]						)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase_					=  torch.tensor([1.1_669, 0.0_125, -0.1_695]						)
			elif model_name == "focalnet-small":
						lowerCamelCase_					=  torch.tensor([0.4_917, -0.0_430, 0.1_341]						)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase_					=  torch.tensor([-0.2_588, -0.5_342, -0.2_331]						)
			elif model_name == "focalnet-base":
						lowerCamelCase_					=  torch.tensor([-0.1_655, -0.4_090, -0.1_730]						)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase_					=  torch.tensor([0.5_306, -0.0_483, -0.3_928]						)
			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__":
						A_    							=					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.""",
						)

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









import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

