
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()
lowercase_											=	logging.get_logger(__name__)


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Tuple			=						ConvNextConfig()

  if "tiny" in checkpoint_url:
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[3, 3, 9, 3]
    __SCREAMING_SNAKE_CASE     :					Dict			=						[96, 192, 384, 768]
  if "small" in checkpoint_url:
    __SCREAMING_SNAKE_CASE     :					List[str]			=						[3, 3, 27, 3]
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						[96, 192, 384, 768]
  if "base" in checkpoint_url:
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[3, 3, 27, 3]
    __SCREAMING_SNAKE_CASE     :					Any			=						[128, 256, 512, 1_024]
  if "large" in checkpoint_url:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						[3, 3, 27, 3]
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[192, 384, 768, 1_536]
  if "xlarge" in checkpoint_url:
    __SCREAMING_SNAKE_CASE     :					Any			=						[3, 3, 27, 3]
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[256, 512, 1_024, 2_048]

  if "1k" in checkpoint_url:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						1_000
    __SCREAMING_SNAKE_CASE     :					str			=						'''imagenet-1k-id2label.json'''
    __SCREAMING_SNAKE_CASE     :					Dict			=						(1, 1_000)
  else:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						21_841
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						'''imagenet-22k-id2label.json'''
    __SCREAMING_SNAKE_CASE     :					List[str]			=						(1, 21_841)

  __SCREAMING_SNAKE_CASE     :					Any			=						'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE     :					Any			=						num_labels
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						json.load(open(hf_hub_download(snake_case   ,	snake_case   ,	repo_type='''dataset'''    )   ,	'''r'''    )    )
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{int(snake_case    ): 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[9_205]
    del idalabel[15_027]
  __SCREAMING_SNAKE_CASE     :					Tuple			=						idalabel
  __SCREAMING_SNAKE_CASE     :					Dict			=						{v: k for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE     :					List[str]			=						hidden_sizes
  __SCREAMING_SNAKE_CASE     :					Dict			=						depths

  return config, expected_shape


def     a__			(    snake_case    ):





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

  return name


def     a__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						Image.open(requests.get(snake_case   ,	stream=snake_case    ).raw    )
  return im


@torch.no_grad()
def     a__			(    snake_case   ,	snake_case    ):





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

  # load HuggingFace model
  __SCREAMING_SNAKE_CASE     :					Tuple			=						ConvNextForImageClassification(snake_case    )
  model.load_state_dict(snake_case    )
  model.eval()

  # Check outputs on an image, prepared by ConvNextImageProcessor
  __SCREAMING_SNAKE_CASE     :					Any			=						224 if '''224''' in checkpoint_url else 384
  __SCREAMING_SNAKE_CASE     :					Dict			=						ConvNextImageProcessor(size=snake_case    )
  __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(images=prepare_img()   ,	return_tensors='''pt'''    ).pixel_values

  __SCREAMING_SNAKE_CASE     :					str			=						model(snake_case    ).logits

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

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

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

  print('''Pushing model to the hub...'''    )
  __SCREAMING_SNAKE_CASE     :					int			=						'''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(snake_case   ,	snake_case    )   ,	organization='''nielsr'''   ,	commit_message='''Add model'''   ,	)


if __name__ == "__main__":
   lowercase_											=	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.""",
   )

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


from collections import defaultdict


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					defaultdict[str, int]			=						defaultdict(snake_case    )
  # Creating a dictionary containing count of each word
  for word in sentence.split():
    occurrence[word] += 1
  return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("""INPUT STRING""").items():
      print(f'''{word}: {count}''')
