import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case         =     logging.get_logger(__name__)





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase     :			List[str]     	=ConvNextConfig()

      if "tiny" in checkpoint_url:
            UpperCAmelCase     :			Dict     	=[3, 3, 9, 3]
            UpperCAmelCase     :			List[Any]     	=[96, 1_92, 3_84, 7_68]
      if "small" in checkpoint_url:
            UpperCAmelCase     :			Tuple     	=[3, 3, 27, 3]
            UpperCAmelCase     :			Dict     	=[96, 1_92, 3_84, 7_68]
      if "base" in checkpoint_url:
            UpperCAmelCase     :			Tuple     	=[3, 3, 27, 3]
            UpperCAmelCase     :			List[Any]     	=[1_28, 2_56, 5_12, 10_24]
      if "large" in checkpoint_url:
            UpperCAmelCase     :			Optional[Any]     	=[3, 3, 27, 3]
            UpperCAmelCase     :			List[str]     	=[1_92, 3_84, 7_68, 15_36]
      if "xlarge" in checkpoint_url:
            UpperCAmelCase     :			Dict     	=[3, 3, 27, 3]
            UpperCAmelCase     :			Optional[int]     	=[2_56, 5_12, 10_24, 20_48]

      if "1k" in checkpoint_url:
            UpperCAmelCase     :			Any     	=10_00
            UpperCAmelCase     :			List[Any]     	='''imagenet-1k-id2label.json'''
            UpperCAmelCase     :			Dict     	=(1, 10_00)
      else:
            UpperCAmelCase     :			Dict     	=2_18_41
            UpperCAmelCase     :			Optional[Any]     	='''imagenet-22k-id2label.json'''
            UpperCAmelCase     :			str     	=(1, 2_18_41)

      UpperCAmelCase     :			Dict     	='''huggingface/label-files'''
      UpperCAmelCase     :			Any     	=num_labels
      UpperCAmelCase     :			Optional[Any]     	=json.load(open(hf_hub_download(__lowerCAmelCase		,       __lowerCAmelCase		,       repo_type='''dataset'''    )		,       '''r'''    )    )
      UpperCAmelCase     :			Tuple     	={int(__lowerCAmelCase    ): v for k, v in idalabel.items()}
      if "1k" not in checkpoint_url:
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[92_05]
            del idalabel[1_50_27]
      UpperCAmelCase     :			List[str]     	=idalabel
      UpperCAmelCase     :			Optional[int]     	={v: k for k, v in idalabel.items()}
      UpperCAmelCase     :			Union[str, Any]     	=hidden_sizes
      UpperCAmelCase     :			Dict     	=depths

      return config, expected_shape





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	str:






      '''simple docstring'''





      if "downsample_layers.0.0" in name:
            UpperCAmelCase     :			Tuple     	=name.replace('''downsample_layers.0.0'''		,       '''embeddings.patch_embeddings'''    )
      if "downsample_layers.0.1" in name:
            UpperCAmelCase     :			Union[str, Any]     	=name.replace('''downsample_layers.0.1'''		,       '''embeddings.norm'''    )  # we rename to layernorm later on
      if "downsample_layers.1.0" in name:
            UpperCAmelCase     :			Union[str, Any]     	=name.replace('''downsample_layers.1.0'''		,       '''stages.1.downsampling_layer.0'''    )
      if "downsample_layers.1.1" in name:
            UpperCAmelCase     :			Any     	=name.replace('''downsample_layers.1.1'''		,       '''stages.1.downsampling_layer.1'''    )
      if "downsample_layers.2.0" in name:
            UpperCAmelCase     :			Dict     	=name.replace('''downsample_layers.2.0'''		,       '''stages.2.downsampling_layer.0'''    )
      if "downsample_layers.2.1" in name:
            UpperCAmelCase     :			Dict     	=name.replace('''downsample_layers.2.1'''		,       '''stages.2.downsampling_layer.1'''    )
      if "downsample_layers.3.0" in name:
            UpperCAmelCase     :			Tuple     	=name.replace('''downsample_layers.3.0'''		,       '''stages.3.downsampling_layer.0'''    )
      if "downsample_layers.3.1" in name:
            UpperCAmelCase     :			Dict     	=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.
            UpperCAmelCase     :			Dict     	=name[: len('''stages.0'''    )] + '''.layers''' + name[len('''stages.0'''    ) :]
      if "stages" in name:
            UpperCAmelCase     :			Dict     	=name.replace('''stages'''		,       '''encoder.stages'''    )
      if "norm" in name:
            UpperCAmelCase     :			Tuple     	=name.replace('''norm'''		,       '''layernorm'''    )
      if "gamma" in name:
            UpperCAmelCase     :			Any     	=name.replace('''gamma'''		,       '''layer_scale_parameter'''    )
      if "head" in name:
            UpperCAmelCase     :			Tuple     	=name.replace('''head'''		,       '''classifier'''    )

      return name





def 	lowerCAmelCase_			(					)->	List[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase     :			Tuple     	=Image.open(requests.get(__lowerCAmelCase		,       stream=__lowerCAmelCase    ).raw    )
      return im





@torch.no_grad()
def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Tuple:






      '''simple docstring'''





      UpperCAmelCase	,    UpperCAmelCase     :			List[Any]     	=get_convnext_config(__lowerCAmelCase    )
      # load original state_dict from URL
      UpperCAmelCase     :			Optional[Any]     	=torch.hub.load_state_dict_from_url(__lowerCAmelCase    )['''model''']
      # rename keys
      for key in state_dict.copy().keys():
            UpperCAmelCase     :			List[Any]     	=state_dict.pop(__lowerCAmelCase    )
            UpperCAmelCase     :			Union[str, Any]     	=val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
            UpperCAmelCase     :			Dict     	=state_dict.pop(__lowerCAmelCase    )
            if not key.startswith('''classifier'''    ):
                  UpperCAmelCase     :			Optional[Any]     	='''convnext.''' + key
            UpperCAmelCase     :			Union[str, Any]     	=val

      # load HuggingFace model
      UpperCAmelCase     :			Any     	=ConvNextForImageClassification(__lowerCAmelCase    )
      model.load_state_dict(__lowerCAmelCase    )
      model.eval()

      # Check outputs on an image, prepared by ConvNextImageProcessor
      UpperCAmelCase     :			str     	=2_24 if '''224''' in checkpoint_url else 3_84
      UpperCAmelCase     :			List[str]     	=ConvNextImageProcessor(size=__lowerCAmelCase    )
      UpperCAmelCase     :			int     	=image_processor(images=prepare_img()		,       return_tensors='''pt'''    ).pixel_values

      UpperCAmelCase     :			List[Any]     	=model(__lowerCAmelCase    ).logits

      # note: the logits below were obtained without center cropping
      if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
            UpperCAmelCase     :			Dict     	=torch.tensor([-0.1210, -0.6605, 0.1918]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
            UpperCAmelCase     :			List[str]     	=torch.tensor([-0.4473, -0.1847, -0.6365]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
            UpperCAmelCase     :			int     	=torch.tensor([0.4525, 0.7539, 0.0308]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
            UpperCAmelCase     :			Union[str, Any]     	=torch.tensor([0.3561, 0.6350, -0.0384]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
            UpperCAmelCase     :			List[Any]     	=torch.tensor([0.4174, -0.0989, 0.1489]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
            UpperCAmelCase     :			Union[str, Any]     	=torch.tensor([0.2513, -0.1349, -0.1613]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
            UpperCAmelCase     :			Dict     	=torch.tensor([1.2980, 0.3631, -0.1198]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
            UpperCAmelCase     :			str     	=torch.tensor([1.2963, 0.1227, 0.1723]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
            UpperCAmelCase     :			int     	=torch.tensor([1.7956, 0.8390, 0.2820]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
            UpperCAmelCase     :			Tuple     	=torch.tensor([-0.2822, -0.0502, -0.0878]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
            UpperCAmelCase     :			Optional[int]     	=torch.tensor([-0.5672, -0.0730, -0.4348]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
            UpperCAmelCase     :			Union[str, Any]     	=torch.tensor([0.2681, 0.2365, 0.6246]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
            UpperCAmelCase     :			Dict     	=torch.tensor([-0.2642, 0.3931, 0.5116]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
            UpperCAmelCase     :			List[Any]     	=torch.tensor([-0.6677, -0.1873, -0.8379]    )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
            UpperCAmelCase     :			Tuple     	=torch.tensor([-0.7749, -0.2967, -0.6444]    )
      else:
            raise ValueError(f'''Unknown URL: {checkpoint_url}'''    )

      assert torch.allclose(logits[0, :3]		,       __lowerCAmelCase		,       atol=1e-3    )
      assert logits.shape == expected_shape

      Path(__lowerCAmelCase    ).mkdir(exist_ok=__lowerCAmelCase    )
      print(f'''Saving model to {pytorch_dump_folder_path}'''    )
      model.save_pretrained(__lowerCAmelCase    )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
      image_processor.save_pretrained(__lowerCAmelCase    )

      print('''Pushing model to the hub...'''    )
      UpperCAmelCase     :			Dict     	='''convnext'''
      if "tiny" in checkpoint_url:
            model_name += "-tiny"
      elif "small" in checkpoint_url:
            model_name += "-small"
      elif "base" in checkpoint_url:
            model_name += "-base"
      elif "xlarge" in checkpoint_url:
            model_name += "-xlarge"
      elif "large" in checkpoint_url:
            model_name += "-large"
      if "224" in checkpoint_url:
            model_name += "-224"
      elif "384" in checkpoint_url:
            model_name += "-384"
      if "22k" in checkpoint_url and "1k" not in checkpoint_url:
            model_name += "-22k"
      if "22k" in checkpoint_url and "1k" in checkpoint_url:
            model_name += "-22k-1k"

      model.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase		,       __lowerCAmelCase    )		,       organization='''nielsr'''		,       commit_message='''Add model'''		,       )


if __name__ == "__main__":
     __snake_case         =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
         type=str,
         help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the output PyTorch model directory.''',
     )

     __snake_case         =     parser.parse_args()
     convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

       UpperCAmelCase     :			str     	=torch.randint(0		, snake_case__		, (batch_size, height * width)    )

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       def model(snake_case__		, snake_case__		, *snake_case__    ):
             UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=sample.shape
             UpperCAmelCase     :			Tuple     	=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
             UpperCAmelCase     :			List[str]     	=F.log_softmax(logits.double()		, dim=1    ).float()
             return return_value

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       for t in [0, 50, 99]:
             self.check_over_forward(time_step=snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
