


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=	logging.get_logger(__name__)


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False       ):
  A_      :				Any		     =					[]
  for i in range(config.num_hidden_layers       ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')       )
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')       )
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')       )
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')       )
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')       )
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')       )
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')       )
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')       )
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')       )
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')       )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]       )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]       )

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    A_      :				List[Any]		     =					[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''       ) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]       )

  return rename_keys


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False       ):

  for i in range(config.num_hidden_layers       ):
    if base_model:
      A_      :				List[str]		     =					''''''
    else:
      A_      :				Optional[Any]		     =					'''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    A_      :				Optional[int]		     =					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''       )
    A_      :				Dict		     =					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''       )
    # next, add query, keys and values (in that order) to the state dict
    A_      :				Optional[Any]		     =					in_proj_weight[
        : config.hidden_size, :
    ]
    A_      :				str		     =					in_proj_bias[: config.hidden_size]
    A_      :				Optional[Any]		     =					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    A_      :				int		     =					in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    A_      :				Optional[int]		     =					in_proj_weight[
        -config.hidden_size :, :
    ]
    A_      :				Optional[Any]		     =					in_proj_bias[-config.hidden_size :]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					dct.pop(SCREAMING_SNAKE_CASE       )
  A_      :				List[str]		     =					val


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				int		     =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  A_      :				Optional[int]		     =					Image.open(requests.get(SCREAMING_SNAKE_CASE ,			stream=SCREAMING_SNAKE_CASE       ).raw       )
  return im


@torch.no_grad()
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Dict		     =					DeiTConfig()
  # all deit models have fine-tuned heads
  A_      :				str		     =					False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  A_      :				int		     =					1_000
  A_      :				Dict		     =					'''huggingface/label-files'''
  A_      :				Tuple		     =					'''imagenet-1k-id2label.json'''
  A_      :				Any		     =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			repo_type='''dataset'''       ) ,			'''r'''       )       )
  A_      :				Optional[Any]		     =					{int(SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
  A_      :				Dict		     =					idalabel
  A_      :				int		     =					{v: k for k, v in idalabel.items()}
  A_      :				List[Any]		     =					int(deit_name[-6:-4]       )
  A_      :				Tuple		     =					int(deit_name[-3:]       )
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''       ):
    A_      :				Optional[Any]		     =					192
    A_      :				int		     =					768
    A_      :				int		     =					12
    A_      :				str		     =					3
  elif deit_name[9:].startswith('''small'''       ):
    A_      :				List[Any]		     =					384
    A_      :				int		     =					1_536
    A_      :				Union[str, Any]		     =					12
    A_      :				Tuple		     =					6
  if deit_name[9:].startswith('''base'''       ):
    pass
  elif deit_name[4:].startswith('''large'''       ):
    A_      :				int		     =					1_024
    A_      :				str		     =					4_096
    A_      :				List[str]		     =					24
    A_      :				List[str]		     =					16

  # load original model from timm
  A_      :				Any		     =					timm.create_model(SCREAMING_SNAKE_CASE ,			pretrained=SCREAMING_SNAKE_CASE       )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  A_      :				List[str]		     =					timm_model.state_dict()
  A_      :				List[Any]		     =					create_rename_keys(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  read_in_q_k_v(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  # load HuggingFace model
  A_      :				List[str]		     =					DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE       ).eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE       )

  # Check outputs on an image, prepared by DeiTImageProcessor
  A_      :				Optional[int]		     =					int(
      (256 / 224) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  A_      :				Any		     =					DeiTImageProcessor(size=SCREAMING_SNAKE_CASE ,			crop_size=config.image_size       )
  A_      :				List[Any]		     =					image_processor(images=prepare_img() ,			return_tensors='''pt'''       )
  A_      :				str		     =					encoding['''pixel_values''']
  A_      :				Dict		     =					model(SCREAMING_SNAKE_CASE       )

  A_      :				int		     =					timm_model(SCREAMING_SNAKE_CASE       )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(SCREAMING_SNAKE_CASE ,			outputs.logits ,			atol=1e-3       )

  Path(SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE       )
  print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''       )
  model.save_pretrained(SCREAMING_SNAKE_CASE       )
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--deit_name""",
         default="""vit_deit_base_distilled_patch16_224""",
         type=str,
         help="""Name of the DeiT timm model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )

     UpperCamelCase								=	parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






def       A       (					lowercase )   ->  list:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		for i in range(1    ,       lowercase ):
				UpperCamelCase		=     collection[i]
				UpperCamelCase		=     0
				UpperCamelCase		=     i - 1

				while low <= high:
						UpperCamelCase		=     (low + high) // 2
						if val < collection[mid]:
								UpperCamelCase		=     mid - 1
						else:
								UpperCamelCase		=     mid + 1
				for j in range(lowercase    ,       lowercase    ,       -1 ):
						UpperCamelCase		=     collection[j - 1]
				UpperCamelCase		=     val
		return collection


if __name__ == "__main__":
					_UpperCAmelCase					: List[Any]      =       input("Enter numbers separated by a comma:\n").strip()
					_UpperCAmelCase					: Optional[int]      =       [int(item) for item in user_input.split(",")]
					print(binary_insertion_sort(unsorted))
