



'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase: Optional[Any]							=						logging.get_logger()
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A = True    ):
  print(f"""Converting {name}..."""    )

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        a						: Optional[int]					       =					timm.create_model('levit_128s'			,      pretrained=_A    )
      else:
        a						: Optional[Any]					       =					timm.create_model('levit_128'			,      pretrained=_A    )
    if hidden_sizes == 192:
      a						: Optional[Any]					       =					timm.create_model('levit_192'			,      pretrained=_A    )
    if hidden_sizes == 256:
      a						: Dict					       =					timm.create_model('levit_256'			,      pretrained=_A    )
    if hidden_sizes == 384:
      a						: List[Any]					       =					timm.create_model('levit_384'			,      pretrained=_A    )

    from_model.eval()
    a						: Union[str, Any]					       =					LevitForImageClassificationWithTeacher(_A    ).eval()
    a						: Union[str, Any]					       =					OrderedDict()

    a						: Optional[Any]					       =					from_model.state_dict()
    a						: List[Any]					       =					list(from_model.state_dict().keys()    )
    a						: List[Any]					       =					list(our_model.state_dict().keys()    )
    print(len(_A    )			,      len(_A    )    )
    for i in range(len(_A    )    ):
      a						: Any					       =					weights[og_keys[i]]
    our_model.load_state_dict(_A    )

    a						: Union[str, Any]					       =					torch.randn((2, 3, 224, 224)    )
    a						: Optional[Any]					       =					from_model(_A    )
    a						: List[str]					       =					our_model(_A    ).logits

  assert torch.allclose(_A			,      _A    ), "The model logits don't match the original one."

  a						: List[str]					       =					name
  print(_A    )

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name    )
    a						: Optional[Any]					       =					LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name    )

    print(f"""Pushed {checkpoint_name}"""    )
def 				lowerCamelCase__	(						_A			,      _A = None			,      _A = True    ):
  a						: Dict					       =					'imagenet-1k-id2label.json'
  a						: Any					       =					1000
  a						: Union[str, Any]					       =					(1, num_labels)

  a						: Dict					       =					'huggingface/label-files'
  a						: str					       =					num_labels
  a						: str					       =					json.load(open(hf_hub_download(_A			,      _A			,      repo_type='dataset'    )			,      'r'    )    )
  a						: Union[str, Any]					       =					{int(_A    ): v for k, v in idalabel.items()}

  a						: Tuple					       =					idalabel
  a						: Optional[Any]					       =					{v: k for k, v in idalabel.items()}

  a						: Any					       =					partial(_A			,      num_labels=_A			,      idalabel=_A			,      labelaid=_A    )

  a						: Optional[Any]					       =					{
      'levit-128S': 128,
      'levit-128': 128,
      'levit-192': 192,
      'levit-256': 256,
      'levit-384': 384,
  }

  a						: Dict					       =					{
      'levit-128S': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]			,      num_attention_heads=[4, 6, 8]			,      depths=[2, 3, 4]			,      key_dim=[16, 16, 16]			,      drop_path_rate=0			,      ),
      'levit-128': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]			,      num_attention_heads=[4, 8, 12]			,      depths=[4, 4, 4]			,      key_dim=[16, 16, 16]			,      drop_path_rate=0			,      ),
      'levit-192': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384]			,      num_attention_heads=[3, 5, 6]			,      depths=[4, 4, 4]			,      key_dim=[32, 32, 32]			,      drop_path_rate=0			,      ),
      'levit-256': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512]			,      num_attention_heads=[4, 6, 8]			,      depths=[4, 4, 4]			,      key_dim=[32, 32, 32]			,      drop_path_rate=0			,      ),
      'levit-384': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768]			,      num_attention_heads=[6, 9, 12]			,      depths=[4, 4, 4]			,      key_dim=[32, 32, 32]			,      drop_path_rate=0.1			,      ),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name]			,      _A			,      names_to_config[model_name]			,      _A			,      _A    )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]			,      _A			,      _A			,      _A			,      _A    )
  return config, expected_shape


if __name__ == "__main__":
     lowerCAmelCase: int							=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default=None,
         type=str,
         help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='levit-dump-folder/',
         type=Path,
         required=False,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
     parser.add_argument(
         '--no-push_to_hub',
         dest='push_to_hub',
         action='store_false',
         help='Do not push model and image processor to the hub',
     )

     lowerCAmelCase: Optional[Any]							=						parser.parse_args()
     lowerCAmelCase: Path							=						args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()