





'''simple docstring'''


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()
_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    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"
        _lowerCAmelCase						      =   [(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 						__a(SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       int=False     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   dct.pop(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   val


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
    _lowerCAmelCase						      =   Image.open(requests.get(SCREAMING_SNAKE_CASE_		,							stream=SCREAMING_SNAKE_CASE_     ).raw     )
    return im


@torch.no_grad()
def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   DeiTConfig()
    # all deit models have fine-tuned heads
    _lowerCAmelCase						      =   False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    _lowerCAmelCase						      =   1000
    _lowerCAmelCase						      =   "huggingface/label-files"
    _lowerCAmelCase						      =   "imagenet-1k-id2label.json"
    _lowerCAmelCase						      =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							repo_type="dataset"     )		,							"r"     )     )
    _lowerCAmelCase						      =   {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
    _lowerCAmelCase						      =   idalabel
    _lowerCAmelCase						      =   {v: k for k, v in idalabel.items()}
    _lowerCAmelCase						      =   int(deit_name[-6:-4]     )
    _lowerCAmelCase						      =   int(deit_name[-3:]     )
    # size of the architecture
    if deit_name[9:].startswith("tiny"     ):
        _lowerCAmelCase						      =   192
        _lowerCAmelCase						      =   768
        _lowerCAmelCase						      =   12
        _lowerCAmelCase						      =   3
    elif deit_name[9:].startswith("small"     ):
        _lowerCAmelCase						      =   384
        _lowerCAmelCase						      =   1536
        _lowerCAmelCase						      =   12
        _lowerCAmelCase						      =   6
    if deit_name[9:].startswith("base"     ):
        pass
    elif deit_name[4:].startswith("large"     ):
        _lowerCAmelCase						      =   1024
        _lowerCAmelCase						      =   4096
        _lowerCAmelCase						      =   24
        _lowerCAmelCase						      =   16

    # load original model from timm
    _lowerCAmelCase						      =   timm.create_model(SCREAMING_SNAKE_CASE_		,							pretrained=SCREAMING_SNAKE_CASE_     )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    _lowerCAmelCase						      =   timm_model.state_dict()
    _lowerCAmelCase						      =   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
    _lowerCAmelCase						      =   DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_     ).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_     )

    # Check outputs on an image, prepared by DeiTImageProcessor
    _lowerCAmelCase						      =   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
    _lowerCAmelCase						      =   DeiTImageProcessor(size=SCREAMING_SNAKE_CASE_		,							crop_size=config.image_size     )
    _lowerCAmelCase						      =   image_processor(images=prepare_img()		,							return_tensors="pt"     )
    _lowerCAmelCase						      =   encoding["pixel_values"]
    _lowerCAmelCase						      =   model(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   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__":
       _SCREAMING_SNAKE_CASE									=				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."
       )

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









"""simple docstring"""


import random
from typing import Any




def        lowercase__     (       lowercase_      )       ->		list[Any]:




      """simple docstring"""

      for _ in range(len(lowercase_      )      ):
            _UpperCamelCase   :						Dict    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase   :						str    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							data[b], data[a]
      return data


if __name__ == "__main__":
  lowerCamelCase__						= [0, 1, 2, 3, 4, 5, 6, 7]
  lowerCamelCase__						= ["python", "says", "hello", "!"]
  print("Fisher-Yates Shuffle:")
  print("List", integers, strings)
  print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
