






"""simple docstring"""



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()
_UpperCamelCase						:    Tuple		=						logging.get_logger(__name__)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			ConvNextConfig()

  if "tiny" in checkpoint_url:
    lowercase				       =			[3, 3, 9, 3]
    lowercase				       =			[96, 1_92, 3_84, 7_68]
  if "small" in checkpoint_url:
    lowercase				       =			[3, 3, 27, 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 "xlarge" in checkpoint_url:
    lowercase				       =			[3, 3, 27, 3]
    lowercase				       =			[2_56, 5_12, 10_24, 20_48]

  if "1k" in checkpoint_url:
    lowercase				       =			10_00
    lowercase				       =			'''imagenet-1k-id2label.json'''
    lowercase				       =			(1, 10_00)
  else:
    lowercase				       =			2_18_41
    lowercase				       =			'''imagenet-22k-id2label.json'''
    lowercase				       =			(1, 2_18_41)

  lowercase				       =			'''huggingface/label-files'''
  lowercase				       =			num_labels
  lowercase				       =			json.load(open(hf_hub_download(__lowerCAmelCase						,   __lowerCAmelCase						,   repo_type='dataset'       )						,   'r'       )       )
  lowercase				       =			{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]
  lowercase				       =			idalabel
  lowercase				       =			{v: k for k, v in idalabel.items()}
  lowercase				       =			hidden_sizes
  lowercase				       =			depths

  return config, expected_shape
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict       ):





  '''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 "stages" in name:
    lowercase				       =			name.replace('stages'						,   'encoder.stages'       )
  if "norm" in name:
    lowercase				       =			name.replace('norm'						,   'layernorm'       )
  if "gamma" in name:
    lowercase				       =			name.replace('gamma'						,   'layer_scale_parameter'       )
  if "head" in name:
    lowercase				       =			name.replace('head'						,   'classifier'       )

  return name
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowercase				       =			Image.open(requests.get(__lowerCAmelCase						,   stream=__lowerCAmelCase       ).raw       )
  return im
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    int       ):





  '''simple docstring'''





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

  # load HuggingFace model
  lowercase				       =			ConvNextForImageClassification(__lowerCAmelCase       )
  model.load_state_dict(__lowerCAmelCase       )
  model.eval()

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

  lowercase				       =			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":
    lowercase				       =			torch.tensor([-0.1210, -0.6605, 0.1918]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
    lowercase				       =			torch.tensor([-0.4473, -0.1847, -0.6365]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
    lowercase				       =			torch.tensor([0.4525, 0.7539, 0.0308]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
    lowercase				       =			torch.tensor([0.3561, 0.6350, -0.0384]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
    lowercase				       =			torch.tensor([0.4174, -0.0989, 0.1489]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
    lowercase				       =			torch.tensor([0.2513, -0.1349, -0.1613]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
    lowercase				       =			torch.tensor([1.2980, 0.3631, -0.1198]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
    lowercase				       =			torch.tensor([1.2963, 0.1227, 0.1723]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
    lowercase				       =			torch.tensor([1.7956, 0.8390, 0.2820]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
    lowercase				       =			torch.tensor([-0.2822, -0.0502, -0.0878]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
    lowercase				       =			torch.tensor([-0.5672, -0.0730, -0.4348]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
    lowercase				       =			torch.tensor([0.2681, 0.2365, 0.6246]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
    lowercase				       =			torch.tensor([-0.2642, 0.3931, 0.5116]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
    lowercase				       =			torch.tensor([-0.6677, -0.1873, -0.8379]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
    lowercase				       =			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...'       )
  lowercase				       =			'''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__":
    _UpperCamelCase						:    Tuple		=						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.',
    )

    _UpperCamelCase						:    Tuple		=						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
