





"""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_      =				logging.get_logger(__name__)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




       _UpperCAmelCase			     =  ConvNextVaConfig()

       if "atto" in checkpoint_url:
              _UpperCAmelCase			     =  [2, 2, 6, 2]
              _UpperCAmelCase			     =  [40, 80, 160, 320]
       if "femto" in checkpoint_url:
              _UpperCAmelCase			     =  [2, 2, 6, 2]
              _UpperCAmelCase			     =  [48, 96, 192, 384]
       if "pico" in checkpoint_url:
              _UpperCAmelCase			     =  [2, 2, 6, 2]
              _UpperCAmelCase			     =  [64, 128, 256, 512]
       if "nano" in checkpoint_url:
              _UpperCAmelCase			     =  [2, 2, 8, 2]
              _UpperCAmelCase			     =  [80, 160, 320, 640]
       if "tiny" in checkpoint_url:
              _UpperCAmelCase			     =  [3, 3, 9, 3]
              _UpperCAmelCase			     =  [96, 192, 384, 768]
       if "base" in checkpoint_url:
              _UpperCAmelCase			     =  [3, 3, 27, 3]
              _UpperCAmelCase			     =  [128, 256, 512, 1024]
       if "large" in checkpoint_url:
              _UpperCAmelCase			     =  [3, 3, 27, 3]
              _UpperCAmelCase			     =  [192, 384, 768, 1536]
       if "huge" in checkpoint_url:
              _UpperCAmelCase			     =  [3, 3, 27, 3]
              _UpperCAmelCase			     =  [352, 704, 1408, 2816]

       _UpperCAmelCase			     =  1000
       _UpperCAmelCase			     =  'imagenet-1k-id2label.json'
       _UpperCAmelCase			     =  (1, 1000)

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

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

       return config, expected_shape

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




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

       return name

def    __lowerCamelCase     (							)		->							str:






       """simple docstring"""




       _UpperCAmelCase			     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
       _UpperCAmelCase			     =  Image.open(requests.get(SCREAMING_SNAKE_CASE,stream=SCREAMING_SNAKE_CASE				).raw				)
       return im

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




       if "224" in checkpoint_url:
              _UpperCAmelCase			     =  224
              _UpperCAmelCase			     =  224 / 256
       elif "384" in checkpoint_url:
              _UpperCAmelCase			     =  384
              _UpperCAmelCase			     =  None
       else:
              _UpperCAmelCase			     =  512
              _UpperCAmelCase			     =  None

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

@torch.no_grad()
def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Any:






       """simple docstring"""




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

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

       # load HuggingFace model
       _UpperCAmelCase			     =  ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE				)
       model.load_state_dict(SCREAMING_SNAKE_CASE				)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       _UpperCAmelCase			     =  convert_preprocessor(SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  preprocessor(images=prepare_img(),return_tensors='pt'				)
       _UpperCAmelCase			     =  model(**SCREAMING_SNAKE_CASE				).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":
              _UpperCAmelCase			     =  torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]				)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              _UpperCAmelCase			     =  torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]				)
       else:
              raise ValueError(F"""Unknown URL: {checkpoint_url}"""				)

       assert torch.allclose(logits[0, :5],SCREAMING_SNAKE_CASE,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(SCREAMING_SNAKE_CASE				):
                     os.mkdir(SCREAMING_SNAKE_CASE				)

              model.save_pretrained(SCREAMING_SNAKE_CASE				)
              preprocessor.save_pretrained(SCREAMING_SNAKE_CASE				)

       _UpperCAmelCase			     =  '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(SCREAMING_SNAKE_CASE				)
              preprocessor.push_to_hub(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    lowerCAmelCase_      =				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_      =				parser.parse_args()
    convert_convnextva_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
    )













"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






       def 				__A			( self  ):
              for timesteps in [10, 50, 1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=a__  )






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






       def 				__A			( self  ):
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=a__  )






       def 				__A			( self  ):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=a__  )






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






