'''simple docstring'''


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  lowerCamelCase_		:Any						    =		(DDIMScheduler,)
  lowerCamelCase_		:Optional[Any]						    =		(('''eta''', 0.0), ('''num_inference_steps''', 50))


  def _UpperCamelCase     ( self      ,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      {
           'num_train_timesteps': 1_0_0_0,
           'beta_start': 0.00_01,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**snake_case_						)
       return config


  def _UpperCamelCase     ( self      ,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.scheduler_classes[0]
       UpperCAmelCase_							:   Any   =      self.get_scheduler_config(**snake_case_						)
       UpperCAmelCase_							:   List[str]   =      scheduler_class(**snake_case_						)

       UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      1_0, 0.0

       UpperCAmelCase_							:   List[Any]   =      self.dummy_model()
       UpperCAmelCase_							:   Tuple   =      self.dummy_sample_deter

       scheduler.set_timesteps(snake_case_						)

       for t in scheduler.timesteps:
            UpperCAmelCase_							:   Any   =      model(snake_case_      ,		snake_case_						)
            UpperCAmelCase_							:   Tuple   =      scheduler.step(snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						).prev_sample

       return sample


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=snake_case_						)

       UpperCAmelCase_							:   Optional[Any]   =      self.scheduler_classes[0]
       UpperCAmelCase_							:   Union[str, Any]   =      self.get_scheduler_config(steps_offset=1						)
       UpperCAmelCase_							:   Optional[int]   =      scheduler_class(**snake_case_						)
       scheduler.set_timesteps(5						)
       assert torch.equal(scheduler.timesteps      ,		torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]						)						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]      ,		[0.0_02, 0.02, 0.2, 2]						):
            self.check_over_configs(beta_start=snake_case_      ,		beta_end=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.check_over_configs(thresholding=snake_case_						)
       for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=snake_case_      ,		prediction_type=snake_case_      ,		sample_max_value=snake_case_      ,		)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for t in [1, 1_0, 4_9]:
            self.check_over_forward(time_step=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for t, num_inference_steps in zip([1, 1_0, 5_0]      ,		[1_0, 5_0, 5_0_0]						):
            self.check_over_forward(time_step=snake_case_      ,		num_inference_steps=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for t, eta in zip([1, 1_0, 4_9]      ,		[0.0, 0.5, 1.0]						):
            self.check_over_forward(time_step=snake_case_      ,		eta=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.scheduler_classes[0]
       UpperCAmelCase_							:   List[Any]   =      self.get_scheduler_config()
       UpperCAmelCase_							:   Dict   =      scheduler_class(**snake_case_						)

       assert torch.sum(torch.abs(scheduler._get_variance(0      ,		0						) - 0.0						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,		4_0_0						) - 0.1_47_71						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,		9_6_0						) - 0.3_24_60						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0      ,		0						) - 0.0						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ,		4_8_6						) - 0.0_09_79						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,		9_9_8						) - 0.02						)						) < 1E-5


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.full_loop()

       UpperCAmelCase_							:   int   =      torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:   Dict   =      torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 1_72.00_67						) < 1E-2
       assert abs(result_mean.item() - 0.22_39_67						) < 1E-3


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      self.full_loop(prediction_type='v_prediction'						)

       UpperCAmelCase_							:   Union[str, Any]   =      torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:   Optional[Any]   =      torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 52.53_02						) < 1E-2
       assert abs(result_mean.item() - 0.06_84						) < 1E-3


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      self.full_loop(set_alpha_to_one=snake_case_      ,		beta_start=0.01						)
       UpperCAmelCase_							:   Dict   =      torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:   Dict   =      torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 1_49.82_95						) < 1E-2
       assert abs(result_mean.item() - 0.19_51						) < 1E-3






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      self.full_loop(set_alpha_to_one=snake_case_      ,		beta_start=0.01						)
       UpperCAmelCase_							:   int   =      torch.sum(torch.abs(snake_case_						)						)
       UpperCAmelCase_							:   str   =      torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_sum.item() - 1_49.07_84						) < 1E-2
       assert abs(result_mean.item() - 0.19_41						) < 1E-3







'''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()
snake_case__     :			Optional[Any]      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Dict   =      ConvNextConfig()

     if "tiny" in checkpoint_url:
          UpperCAmelCase_							:   Any   =      [3, 3, 9, 3]
          UpperCAmelCase_							:   Any   =      [96, 192, 384, 768]
     if "small" in checkpoint_url:
          UpperCAmelCase_							:   int   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Optional[int]   =      [96, 192, 384, 768]
     if "base" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Dict   =      [128, 256, 512, 1024]
     if "large" in checkpoint_url:
          UpperCAmelCase_							:   Optional[Any]   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   List[Any]   =      [192, 384, 768, 1536]
     if "xlarge" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Any   =      [256, 512, 1024, 2048]

     if "1k" in checkpoint_url:
          UpperCAmelCase_							:   int   =      1000
          UpperCAmelCase_							:   Any   =      'imagenet-1k-id2label.json'
          UpperCAmelCase_							:   str   =      (1, 1000)
     else:
          UpperCAmelCase_							:   Dict   =      21841
          UpperCAmelCase_							:   Optional[Any]   =      'imagenet-22k-id2label.json'
          UpperCAmelCase_							:   Optional[Any]   =      (1, 21841)

     UpperCAmelCase_							:   int   =      'huggingface/label-files'
     UpperCAmelCase_							:   Any   =      num_labels
     UpperCAmelCase_							:   Dict   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
     UpperCAmelCase_							:   List[Any]   =      {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[9205]
          del idalabel[15027]
     UpperCAmelCase_							:   Optional[Any]   =      idalabel
     UpperCAmelCase_							:   Dict   =      {v: k for k, v in idalabel.items()}
     UpperCAmelCase_							:   Tuple   =      hidden_sizes
     UpperCAmelCase_							:   List[str]   =      depths

     return config, expected_shape


def 					_lowerCamelCase    (      lowerCamelCase_					:							int   ):



     """simple docstring"""





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

     return name


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[str]   =      'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_							:   Optional[int]   =      Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_   ).raw   )
     return im


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							int	,     lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





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

     # load HuggingFace model
     UpperCAmelCase_							:   Tuple   =      ConvNextForImageClassification(lowerCamelCase_   )
     model.load_state_dict(lowerCamelCase_   )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     UpperCAmelCase_							:   Tuple   =      224 if '224' in checkpoint_url else 384
     UpperCAmelCase_							:   Optional[Any]   =      ConvNextImageProcessor(size=lowerCamelCase_   )
     UpperCAmelCase_							:   Tuple   =      image_processor(images=prepare_img()	,     return_tensors='pt'   ).pixel_values

     UpperCAmelCase_							:   Dict   =      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_							:   Tuple   =      torch.tensor([-0.1210, -0.6605, 0.1918]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          UpperCAmelCase_							:   str   =      torch.tensor([-0.4473, -0.1847, -0.6365]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          UpperCAmelCase_							:   Tuple   =      torch.tensor([0.4525, 0.7539, 0.0308]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          UpperCAmelCase_							:   List[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[str]   =      torch.tensor([0.4174, -0.0989, 0.1489]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          UpperCAmelCase_							:   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_							:   Union[str, Any]   =      torch.tensor([1.2963, 0.1227, 0.1723]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          UpperCAmelCase_							:   Optional[Any]   =      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_							:   Union[str, Any]   =      torch.tensor([-0.5672, -0.0730, -0.4348]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          UpperCAmelCase_							:   Optional[Any]   =      torch.tensor([0.2681, 0.2365, 0.6246]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          UpperCAmelCase_							:   Optional[int]   =      torch.tensor([-0.2642, 0.3931, 0.5116]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          UpperCAmelCase_							:   Union[str, 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_							:   str   =      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_							:   Union[str, Any]   =      '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__     :			List[Any]      			=  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__     :			int      			=  parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






