




'''simple docstring'''



import random




def    lowercase__(       __UpperCamelCase: Dict  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Optional[int]   =		num - 1
      SCREAMING_SNAKE_CASE					:	str   =		0

      while s % 2 == 0:
            SCREAMING_SNAKE_CASE					:	int   =		s // 2
            t += 1

      for _ in range(5  ):
            SCREAMING_SNAKE_CASE					:	str   =		random.randrange(2	,num - 1  )
            SCREAMING_SNAKE_CASE					:	Any   =		pow(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase  )
            if v != 1:
                  SCREAMING_SNAKE_CASE					:	Optional[Any]   =		0
                  while v != (num - 1):
                        if i == t - 1:
                              return False
                        else:
                              SCREAMING_SNAKE_CASE					:	Any   =		i + 1
                              SCREAMING_SNAKE_CASE					:	Tuple   =		(v**2) % num
      return True




def    lowercase__(       __UpperCamelCase: Any  ):

      """simple docstring"""






      if num < 2:
            return False

      SCREAMING_SNAKE_CASE					:	int   =		[
          2,
          3,
          5,
          7,
          11,
          13,
          17,
          19,
          23,
          29,
          31,
          37,
          41,
          43,
          47,
          53,
          59,
          61,
          67,
          71,
          73,
          79,
          83,
          89,
          97,
          1_01,
          1_03,
          1_07,
          1_09,
          1_13,
          1_27,
          1_31,
          1_37,
          1_39,
          1_49,
          1_51,
          1_57,
          1_63,
          1_67,
          1_73,
          1_79,
          1_81,
          1_91,
          1_93,
          1_97,
          1_99,
          2_11,
          2_23,
          2_27,
          2_29,
          2_33,
          2_39,
          2_41,
          2_51,
          2_57,
          2_63,
          2_69,
          2_71,
          2_77,
          2_81,
          2_83,
          2_93,
          3_07,
          3_11,
          3_13,
          3_17,
          3_31,
          3_37,
          3_47,
          3_49,
          3_53,
          3_59,
          3_67,
          3_73,
          3_79,
          3_83,
          3_89,
          3_97,
          4_01,
          4_09,
          4_19,
          4_21,
          4_31,
          4_33,
          4_39,
          4_43,
          4_49,
          4_57,
          4_61,
          4_63,
          4_67,
          4_79,
          4_87,
          4_91,
          4_99,
          5_03,
          5_09,
          5_21,
          5_23,
          5_41,
          5_47,
          5_57,
          5_63,
          5_69,
          5_71,
          5_77,
          5_87,
          5_93,
          5_99,
          6_01,
          6_07,
          6_13,
          6_17,
          6_19,
          6_31,
          6_41,
          6_43,
          6_47,
          6_53,
          6_59,
          6_61,
          6_73,
          6_77,
          6_83,
          6_91,
          7_01,
          7_09,
          7_19,
          7_27,
          7_33,
          7_39,
          7_43,
          7_51,
          7_57,
          7_61,
          7_69,
          7_73,
          7_87,
          7_97,
          8_09,
          8_11,
          8_21,
          8_23,
          8_27,
          8_29,
          8_39,
          8_53,
          8_57,
          8_59,
          8_63,
          8_77,
          8_81,
          8_83,
          8_87,
          9_07,
          9_11,
          9_19,
          9_29,
          9_37,
          9_41,
          9_47,
          9_53,
          9_67,
          9_71,
          9_77,
          9_83,
          9_91,
          9_97,
      ]

      if num in low_primes:
            return True

      for prime in low_primes:
            if (num % prime) == 0:
                  return False

      return rabin_miller(__UpperCamelCase  )




def    lowercase__(       __UpperCamelCase: int = 10_24  ):

      """simple docstring"""






      while True:
            SCREAMING_SNAKE_CASE					:	int   =		random.randrange(2 ** (keysize - 1)	,2 ** (keysize)  )
            if is_prime_low_num(__UpperCamelCase  ):
                  return num


if __name__ == "__main__":
      UpperCamelCase_       =							generate_large_prime()
      print(("Prime number:", num))
      print(("is_prime_low_num:", is_prime_low_num(num)))



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()
A   :     str       =				logging.get_logger(__name__)


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


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


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


@torch.no_grad()
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE_      =     False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_      =     1_0_0_0
      SCREAMING_SNAKE_CASE_      =     "huggingface/label-files"
      SCREAMING_SNAKE_CASE_      =     "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_      =     json.load(open(hf_hub_download(__UpperCamelCase   ,					__UpperCamelCase   ,					repo_type="dataset" )   ,					"r" ) )
      SCREAMING_SNAKE_CASE_      =     {int(__UpperCamelCase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     idalabel
      SCREAMING_SNAKE_CASE_      =     {v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-6:-4] )
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-3:] )
      # size of the architecture
      if deit_name[9:].startswith("tiny" ):
            SCREAMING_SNAKE_CASE_      =     1_9_2
            SCREAMING_SNAKE_CASE_      =     7_6_8
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     3
      elif deit_name[9:].startswith("small" ):
            SCREAMING_SNAKE_CASE_      =     3_8_4
            SCREAMING_SNAKE_CASE_      =     1_5_3_6
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     6
      if deit_name[9:].startswith("base" ):
            pass
      elif deit_name[4:].startswith("large" ):
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     4_0_9_6
            SCREAMING_SNAKE_CASE_      =     2_4
            SCREAMING_SNAKE_CASE_      =     1_6

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

      # load state_dict of original model, remove and rename some keys
      SCREAMING_SNAKE_CASE_      =     timm_model.state_dict()
      SCREAMING_SNAKE_CASE_      =     create_rename_keys(__UpperCamelCase   ,					__UpperCamelCase )
      for src, dest in rename_keys:
            rename_key(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
      read_in_q_k_v(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_      =     DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
      model.load_state_dict(__UpperCamelCase )

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE_      =     int(
          (2_5_6 / 2_2_4) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE_      =     DeiTImageProcessor(size=__UpperCamelCase   ,					crop_size=config.image_size )
      SCREAMING_SNAKE_CASE_      =     image_processor(images=prepare_img()   ,					return_tensors="pt" )
      SCREAMING_SNAKE_CASE_      =     encoding["pixel_values"]
      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     timm_model(__UpperCamelCase )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__UpperCamelCase   ,					outputs.logits   ,					atol=1E-3 )

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


if __name__ == "__main__":
      A   :     Tuple       =				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."
      )

      A   :     Dict       =				parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


