
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 (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
		lowerCamelCase					:      Any          =   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					:      List[str]          =   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					:      List[Any]          =   [3, 3, 3, 3]
						lowerCamelCase					:      Optional[Any]          =   [5, 5, 5, 5]
				elif "fl4" in model_name:
						lowerCamelCase					:      Tuple          =   [4, 4, 4, 4]
						lowerCamelCase					:      List[Any]          =   [3, 3, 3, 3]

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

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

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

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

		lowerCamelCase					:      Dict          =   FocalNetConfig(
		    embed_dim=a_,						depths=a_,						focal_levels=a_,						focal_windows=a_,						use_conv_embed=a_,						idalabel=a_,						labelaid=a_,						use_post_layernorm=a_,						use_layerscale=a_,						)

		return config
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


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

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

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

		return name
def 		UpperCAmelCase (		a_,						a_,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   {
		    '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					:      Optional[Any]          =   model_name_to_url[model_name]
		print('Checkpoint URL: ',						a_      )
		lowerCamelCase					:      List[str]          =   torch.hub.load_state_dict_from_url(a_,						map_location='cpu'      )['model']

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

		lowerCamelCase					:      int          =   get_focalnet_config(a_      )
		lowerCamelCase					:      Optional[int]          =   FocalNetForImageClassification(a_      )
		model.eval()

		# load state dict
		model.load_state_dict(a_      )

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

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

		lowerCamelCase					:      int          =   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]      ),
		    ]      )

		lowerCamelCase					:      Tuple          =   image_transforms(a_      ).unsqueeze(0      )

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

		lowerCamelCase					:      Union[str, Any]          =   model(**a_      )

		lowerCamelCase					:      Optional[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":
				lowerCamelCase					:      Any          =   torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]      )
		elif model_name == "focalnet-tiny-lrf":
				lowerCamelCase					:      Optional[int]          =   torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]      )
		elif model_name == "focalnet-small":
				lowerCamelCase					:      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":
				lowerCamelCase					:      List[str]          =   torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]      )
		elif model_name == "focalnet-base":
				lowerCamelCase					:      List[str]          =   torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]      )
		elif model_name == "focalnet-base-lrf":
				lowerCamelCase					:      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],						a_,						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(a_      )
				processor.save_pretrained(a_      )

		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)








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'		,			UpperCAmelCase_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      Any          =   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






