"""simple docstring"""


import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_:       List[Any]			     =       logging.get_logger(__name__)







def        __a (		A   ):






     '''simple docstring'''



     lowercase__								=				ConvNextVaConfig()

     if "atto" in checkpoint_url:
          lowercase__								=				[2, 2, 6, 2]
          lowercase__								=				[40, 80, 1_60, 3_20]
     if "femto" in checkpoint_url:
          lowercase__								=				[2, 2, 6, 2]
          lowercase__								=				[48, 96, 1_92, 3_84]
     if "pico" in checkpoint_url:
          lowercase__								=				[2, 2, 6, 2]
          lowercase__								=				[64, 1_28, 2_56, 5_12]
     if "nano" in checkpoint_url:
          lowercase__								=				[2, 2, 8, 2]
          lowercase__								=				[80, 1_60, 3_20, 6_40]
     if "tiny" in checkpoint_url:
          lowercase__								=				[3, 3, 9, 3]
          lowercase__								=				[96, 1_92, 3_84, 7_68]
     if "base" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[1_28, 2_56, 5_12, 10_24]
     if "large" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[1_92, 3_84, 7_68, 15_36]
     if "huge" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[3_52, 7_04, 14_08, 28_16]

     lowercase__								=				10_00
     lowercase__								=				"imagenet-1k-id2label.json"
     lowercase__								=				(1, 10_00)

     lowercase__								=				"huggingface/label-files"
     lowercase__								=				num_labels
     lowercase__								=				json.load(open(hf_hub_download(A     ,					A     ,					repo_type="dataset"   )     ,					"r"   )   )
     lowercase__								=				{int(A   ): v for k, v in idalabel.items()}

     lowercase__								=				idalabel
     lowercase__								=				{v: k for k, v in idalabel.items()}
     lowercase__								=				hidden_sizes
     lowercase__								=				depths

     return config, expected_shape







def        __a (		A   ):






     '''simple docstring'''



     if "downsample_layers.0.0" in name:
          lowercase__								=				name.replace("downsample_layers.0.0"     ,					"embeddings.patch_embeddings"   )
     if "downsample_layers.0.1" in name:
          lowercase__								=				name.replace("downsample_layers.0.1"     ,					"embeddings.norm"   )  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          lowercase__								=				name.replace("downsample_layers.1.0"     ,					"stages.1.downsampling_layer.0"   )
     if "downsample_layers.1.1" in name:
          lowercase__								=				name.replace("downsample_layers.1.1"     ,					"stages.1.downsampling_layer.1"   )
     if "downsample_layers.2.0" in name:
          lowercase__								=				name.replace("downsample_layers.2.0"     ,					"stages.2.downsampling_layer.0"   )
     if "downsample_layers.2.1" in name:
          lowercase__								=				name.replace("downsample_layers.2.1"     ,					"stages.2.downsampling_layer.1"   )
     if "downsample_layers.3.0" in name:
          lowercase__								=				name.replace("downsample_layers.3.0"     ,					"stages.3.downsampling_layer.0"   )
     if "downsample_layers.3.1" in name:
          lowercase__								=				name.replace("downsample_layers.3.1"     ,					"stages.3.downsampling_layer.1"   )
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          lowercase__								=				name[: len("stages.0"   )] + ".layers" + name[len("stages.0"   ) :]
     if "gamma" in name:
          lowercase__								=				name.replace("gamma"     ,					"weight"   )
     if "beta" in name:
          lowercase__								=				name.replace("beta"     ,					"bias"   )
     if "stages" in name:
          lowercase__								=				name.replace("stages"     ,					"encoder.stages"   )
     if "norm" in name:
          lowercase__								=				name.replace("norm"     ,					"layernorm"   )
     if "head" in name:
          lowercase__								=				name.replace("head"     ,					"classifier"   )

     return name







def        __a (		):






     '''simple docstring'''



     lowercase__								=				"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowercase__								=				Image.open(requests.get(A     ,					stream=A   ).raw   )
     return im







def        __a (		A   ):






     '''simple docstring'''



     if "224" in checkpoint_url:
          lowercase__								=				2_24
          lowercase__								=				2_24 / 2_56
     elif "384" in checkpoint_url:
          lowercase__								=				3_84
          lowercase__								=				None
     else:
          lowercase__								=				5_12
          lowercase__								=				None

     return ConvNextImageProcessor(
         size=A     ,					crop_pct=A     ,					image_mean=[0.485, 0.456, 0.406]     ,					image_std=[0.229, 0.224, 0.225]     ,					resample=PILImageResampling.BICUBIC     ,					)







@torch.no_grad()
def        __a (		A     ,					A     ,					A     ,					A   ):






     '''simple docstring'''



     print("Downloading original model from checkpoint..."   )
     # define ConvNeXTV2 configuration based on URL
     lowercase__			,    lowercase__								=				get_convnextva_config(A   )
     # load original state_dict from URL
     lowercase__								=				torch.hub.load_state_dict_from_url(A   )["model"]

     print("Converting model parameters..."   )
     # rename keys
     for key in state_dict.copy().keys():
          lowercase__								=				state_dict.pop(A   )
          lowercase__								=				val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          lowercase__								=				state_dict.pop(A   )
          if not key.startswith("classifier"   ):
               lowercase__								=				"convnextv2." + key
          lowercase__								=				val

     # load HuggingFace model
     lowercase__								=				ConvNextVaForImageClassification(A   )
     model.load_state_dict(A   )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     lowercase__								=				convert_preprocessor(A   )
     lowercase__								=				preprocessor(images=prepare_img()     ,					return_tensors="pt"   )
     lowercase__								=				model(**A   ).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
          lowercase__								=				torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
          lowercase__								=				torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
          lowercase__								=				torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
          lowercase__								=				torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
          lowercase__								=				torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
          lowercase__								=				torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
          lowercase__								=				torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
          lowercase__								=				torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
          lowercase__								=				torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
          lowercase__								=				torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]   )
     else:
          raise ValueError(f'''Unknown URL: {checkpoint_url}'''   )

     assert torch.allclose(logits[0, :5]     ,					A     ,					atol=1e-3   )
     assert logits.shape == expected_shape
     print("Model outputs match the original results!"   )

     if save_model:
          print("Saving model to local..."   )
          # Create folder to save model
          if not os.path.isdir(A   ):
               os.mkdir(A   )

          model.save_pretrained(A   )
          preprocessor.save_pretrained(A   )

     lowercase__								=				"convnextv2"
     if "atto" in checkpoint_url:
          model_name += "-atto"
     if "femto" in checkpoint_url:
          model_name += "-femto"
     if "pico" in checkpoint_url:
          model_name += "-pico"
     if "nano" in checkpoint_url:
          model_name += "-nano"
     elif "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "large" in checkpoint_url:
          model_name += "-large"
     elif "huge" in checkpoint_url:
          model_name += "-huge"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     elif "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"
     elif "1k" in checkpoint_url:
          model_name += "-1k"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     elif "512" in checkpoint_url:
          model_name += "-512"

     if push_to_hub:
          print(f'''Pushing {model_name} to the hub...'''   )
          model.push_to_hub(A   )
          preprocessor.push_to_hub(A   )


if __name__ == "__main__":
 lowerCAmelCase_:       Any			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
     type=str,
     help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="model",
     type=str,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--save_model", action="store_true", help="Save model to local")
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

 lowerCAmelCase_:       Optional[Any]			     =       parser.parse_args()
 convert_convnextva_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
 )





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor






class  a__     ( unittest.TestCase						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=7,					_UpperCAmelCase=3,					_UpperCAmelCase=18,					_UpperCAmelCase=30,					_UpperCAmelCase=400,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073],					_UpperCAmelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711],					_UpperCAmelCase=True,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				num_channels
        lowercase__								=				image_size
        lowercase__								=				min_resolution
        lowercase__								=				max_resolution
        lowercase__								=				do_resize
        lowercase__								=				size if size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_center_crop
        lowercase__								=				crop_size if crop_size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_normalize
        lowercase__								=				image_mean
        lowercase__								=				image_std
        lowercase__								=				do_convert_rgb


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   OwlViTImageProcessor if is_vision_available() else None


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				OwlViTImageProcessingTester(self	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_resize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"size"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_normalize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_mean"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_std"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_convert_rgb"	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size,					{"height": 18, "width": 18}	)
        self.assertEqual(image_processor.crop_size,					{"height": 18, "width": 18}	)

        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict,					size=42,					crop_size=84	)
        self.assertEqual(image_processor.size,					{"height": 42, "width": 42}	)
        self.assertEqual(image_processor.crop_size,					{"height": 84, "width": 84}	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase	)

        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					Image.Image	)

        # Test not batched input
        lowercase__								=				image_processing(image_inputs[0],					return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)

        # Test batched
        lowercase__								=				image_processing(_UpperCAmelCase,					return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase,					numpify=_UpperCAmelCase	)
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					np.ndarray	)

        # Test not batched input
        lowercase__								=				image_processing(image_inputs[0],					return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)

        # Test batched
        lowercase__								=				image_processing(_UpperCAmelCase,					return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase,					torchify=_UpperCAmelCase	)
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					torch.Tensor	)

        # Test not batched input
        lowercase__								=				image_processing(image_inputs[0],					return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)

        # Test batched
        lowercase__								=				image_processing(_UpperCAmelCase,					return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)




