





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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case    :       Union[str, Any]		       =    logging.get_logger(__name__)

def     a_       (					lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     int=False      ):
   __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"""vit.encoder.layer.{i}.layernorm_before.weight""")      )
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")      )
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")      )
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")      )
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")      )
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")      )
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")      )
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")      )
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")      )
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")      )

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

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

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      __lowerCAmelCase				      =      [(pair[0], pair[1][4:]) if pair[1].startswith('vit'      ) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ('norm.weight', 'vit.layernorm.weight'),
              ('norm.bias', 'vit.layernorm.bias'),
              ('head.weight', 'classifier.weight'),
              ('head.bias', 'classifier.bias'),
          ]      )

   return rename_keys

def     a_       (					lowerCAmelCase_ :     Any,      lowerCAmelCase_ :     Tuple,      lowerCAmelCase_ :     Optional[int]=False      ):
   for i in range(config.num_hidden_layers      ):
      if base_model:
         __lowerCAmelCase				      =      ''
      else:
         __lowerCAmelCase				      =      'vit.'
      # 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_       (					lowerCAmelCase_ :     List[str]      ):
   __lowerCAmelCase				      =      ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(lowerCAmelCase_,      lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     Union[str, Any]      ):
   __lowerCAmelCase				      =      dct.pop(lowerCAmelCase_      )
   __lowerCAmelCase				      =      val

def     a_       (					):
   __lowerCAmelCase				      =      'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCAmelCase				      =      Image.open(requests.get(lowerCAmelCase_,      stream=lowerCAmelCase_      ).raw      )
   return im

@torch.no_grad()
def     a_       (					lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     Optional[Any]=True      ):
   __lowerCAmelCase				      =      ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      __lowerCAmelCase				      =      8
   # set labels if required
   if not base_model:
      __lowerCAmelCase				      =      1000
      __lowerCAmelCase				      =      'huggingface/label-files'
      __lowerCAmelCase				      =      'imagenet-1k-id2label.json'
      __lowerCAmelCase				      =      json.load(open(hf_hub_download(lowerCAmelCase_,      lowerCAmelCase_,      repo_type='dataset'      ),      'r'      )      )
      __lowerCAmelCase				      =      {int(lowerCAmelCase_      ): v for k, v in idalabel.items()}
      __lowerCAmelCase				      =      idalabel
      __lowerCAmelCase				      =      {v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      __lowerCAmelCase				      =      384
      __lowerCAmelCase				      =      1536
      __lowerCAmelCase				      =      12
      __lowerCAmelCase				      =      6

   # load original model from torch hub
   __lowerCAmelCase				      =      torch.hub.load('facebookresearch/dino:main',      lowerCAmelCase_      )
   original_model.eval()

   # load state_dict of original model, remove and rename some keys
   __lowerCAmelCase				      =      original_model.state_dict()
   if base_model:
      remove_classification_head_(lowerCAmelCase_      )
   __lowerCAmelCase				      =      create_rename_keys(lowerCAmelCase_,      base_model=lowerCAmelCase_      )
   for src, dest in rename_keys:
      rename_key(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )
   read_in_q_k_v(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

   # load HuggingFace model
   if base_model:
      __lowerCAmelCase				      =      ViTModel(lowerCAmelCase_,      add_pooling_layer=lowerCAmelCase_      ).eval()
   else:
      __lowerCAmelCase				      =      ViTForImageClassification(lowerCAmelCase_      ).eval()
   model.load_state_dict(lowerCAmelCase_      )

   # Check outputs on an image, prepared by ViTImageProcessor
   __lowerCAmelCase				      =      ViTImageProcessor()
   __lowerCAmelCase				      =      image_processor(images=prepare_img(),      return_tensors='pt'      )
   __lowerCAmelCase				      =      encoding['pixel_values']
   __lowerCAmelCase				      =      model(lowerCAmelCase_      )

   if base_model:
      __lowerCAmelCase				      =      original_model(lowerCAmelCase_      )
      assert torch.allclose(lowerCAmelCase_,      outputs.last_hidden_state[:, 0, :],      atol=1E-1      )
   else:
      __lowerCAmelCase				      =      original_model(lowerCAmelCase_      )
      assert logits.shape == outputs.logits.shape
      assert torch.allclose(lowerCAmelCase_,      outputs.logits,      atol=1E-3      )

   Path(lowerCAmelCase_      ).mkdir(exist_ok=lowerCAmelCase_      )
   print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""      )
   model.save_pretrained(lowerCAmelCase_      )
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
   image_processor.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     _snake_case    :       Dict		       =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     _snake_case    :       List[Any]		       =    parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



import argparse
import struct
import unittest





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase	)							-> None:
							lowerCAmelCase_         =      data

							# Initialize hash values
							lowerCAmelCase_         =      [
							    0x6_A_0_9_E_6_6_7,
							    0xB_B_6_7_A_E_8_5,
							    0x3_C_6_E_F_3_7_2,
							    0xA_5_4_F_F_5_3_A,
							    0x5_1_0_E_5_2_7_F,
							    0x9_B_0_5_6_8_8_C,
							    0x1_F_8_3_D_9_A_B,
							    0x5_B_E_0_C_D_1_9,
							]

							# Initialize round constants
							lowerCAmelCase_         =      [
							    0x4_2_8_A_2_F_9_8,
							    0x7_1_3_7_4_4_9_1,
							    0xB_5_C_0_F_B_C_F,
							    0xE_9_B_5_D_B_A_5,
							    0x3_9_5_6_C_2_5_B,
							    0x5_9_F_1_1_1_F_1,
							    0x9_2_3_F_8_2_A_4,
							    0xA_B_1_C_5_E_D_5,
							    0xD_8_0_7_A_A_9_8,
							    0x1_2_8_3_5_B_0_1,
							    0x2_4_3_1_8_5_B_E,
							    0x5_5_0_C_7_D_C_3,
							    0x7_2_B_E_5_D_7_4,
							    0x8_0_D_E_B_1_F_E,
							    0x9_B_D_C_0_6_A_7,
							    0xC_1_9_B_F_1_7_4,
							    0xE_4_9_B_6_9_C_1,
							    0xE_F_B_E_4_7_8_6,
							    0x0_F_C_1_9_D_C_6,
							    0x2_4_0_C_A_1_C_C,
							    0x2_D_E_9_2_C_6_F,
							    0x4_A_7_4_8_4_A_A,
							    0x5_C_B_0_A_9_D_C,
							    0x7_6_F_9_8_8_D_A,
							    0x9_8_3_E_5_1_5_2,
							    0xA_8_3_1_C_6_6_D,
							    0xB_0_0_3_2_7_C_8,
							    0xB_F_5_9_7_F_C_7,
							    0xC_6_E_0_0_B_F_3,
							    0xD_5_A_7_9_1_4_7,
							    0x0_6_C_A_6_3_5_1,
							    0x1_4_2_9_2_9_6_7,
							    0x2_7_B_7_0_A_8_5,
							    0x2_E_1_B_2_1_3_8,
							    0x4_D_2_C_6_D_F_C,
							    0x5_3_3_8_0_D_1_3,
							    0x6_5_0_A_7_3_5_4,
							    0x7_6_6_A_0_A_B_B,
							    0x8_1_C_2_C_9_2_E,
							    0x9_2_7_2_2_C_8_5,
							    0xA_2_B_F_E_8_A_1,
							    0xA_8_1_A_6_6_4_B,
							    0xC_2_4_B_8_B_7_0,
							    0xC_7_6_C_5_1_A_3,
							    0xD_1_9_2_E_8_1_9,
							    0xD_6_9_9_0_6_2_4,
							    0xF_4_0_E_3_5_8_5,
							    0x1_0_6_A_A_0_7_0,
							    0x1_9_A_4_C_1_1_6,
							    0x1_E_3_7_6_C_0_8,
							    0x2_7_4_8_7_7_4_C,
							    0x3_4_B_0_B_C_B_5,
							    0x3_9_1_C_0_C_B_3,
							    0x4_E_D_8_A_A_4_A,
							    0x5_B_9_C_C_A_4_F,
							    0x6_8_2_E_6_F_F_3,
							    0x7_4_8_F_8_2_E_E,
							    0x7_8_A_5_6_3_6_F,
							    0x8_4_C_8_7_8_1_4,
							    0x8_C_C_7_0_2_0_8,
							    0x9_0_B_E_F_F_F_A,
							    0xA_4_5_0_6_C_E_B,
							    0xB_E_F_9_A_3_F_7,
							    0xC_6_7_1_7_8_F_2,
							]

							lowerCAmelCase_         =      self.preprocessing(self.data	)
							self.final_hash()




				@staticmethod
				def 	__a		(   _UpperCamelCase	)							-> bytes:
							lowerCAmelCase_         =      B"\x80" + (B"\x00" * (63 - (len(_UpperCamelCase	) + 8) % 64))
							lowerCAmelCase_         =      struct.pack(">Q"							,							(len(_UpperCamelCase	) * 8)	)
							return data + padding + big_endian_integer




				def 	__a		(   self	)							-> None:
							# Convert into blocks of 64 bytes
							lowerCAmelCase_         =      [
							    self.preprocessed_data[x : x + 64]
							    for x in range(0							,							len(self.preprocessed_data	)							,							64	)
							]

							for block in self.blocks:
										# Convert the given block into a list of 4 byte integers
										lowerCAmelCase_         =      list(struct.unpack(">16L"							,							_UpperCamelCase	)	)
										# add 48 0-ed integers
										words += [0] * 48

										lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      self.hashes

										for index in range(0							,							64	):
													if index > 15:
																# modify the zero-ed indexes at the end of the array
																lowerCAmelCase_         =      (
																    self.ror(words[index - 15]							,							7	)
																    ^ self.ror(words[index - 15]							,							18	)
																    ^ (words[index - 15] >> 3)
																)
																lowerCAmelCase_         =      (
																    self.ror(words[index - 2]							,							17	)
																    ^ self.ror(words[index - 2]							,							19	)
																    ^ (words[index - 2] >> 10)
																)

																lowerCAmelCase_         =      (
																    words[index - 16] + sa + words[index - 7] + sa
																) % 0x1_0_0_0_0_0_0_0_0

													# Compression
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							6	) ^ self.ror(_UpperCamelCase							,							11	) ^ self.ror(_UpperCamelCase							,							25	)
													lowerCAmelCase_         =      (e & f) ^ ((~e & 0xF_F_F_F_F_F_F_F) & g)
													lowerCAmelCase_         =      (
													    h + sa + ch + self.round_constants[index] + words[index]
													) % 0x1_0_0_0_0_0_0_0_0
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							2	) ^ self.ror(_UpperCamelCase							,							13	) ^ self.ror(_UpperCamelCase							,							22	)
													lowerCAmelCase_         =      (a & b) ^ (a & c) ^ (b & c)
													lowerCAmelCase_         =      (sa + maj) % 0x1_0_0_0_0_0_0_0_0

													lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      (
													    g,
													    f,
													    e,
													    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
													    c,
													    b,
													    a,
													    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
													)

										lowerCAmelCase_         =      [a, b, c, d, e, f, g, h]

										# Modify final values
										lowerCAmelCase_         =      [
										    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
										    for index, element in enumerate(self.hashes	)
										]

							lowerCAmelCase_         =      "".join([hex(_UpperCamelCase	)[2:].zfill(8	) for value in self.hashes]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							return 0xF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> None:
							import hashlib

							lowerCAmelCase_         =      bytes("Test String"							,							"utf-8"	)
							self.assertEqual(SHAaaa(_UpperCamelCase	).hash							,							hashlib.shaaaa(_UpperCamelCase	).hexdigest()	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			import doctest

			doctest.testmod()

			lowerCAmelCase_         =      argparse.ArgumentParser()
			parser.add_argument(
			    "-s"       ,				"--string"       ,				dest="input_string"       ,				default="Hello World!! Welcome to Cryptography"       ,				help="Hash the string"       ,				)
			parser.add_argument(
			    "-f"       ,				"--file"       ,				dest="input_file"       ,				help="Hash contents of a file"							)

			lowerCAmelCase_         =      parser.parse_args()

			lowerCAmelCase_         =      args.input_string

			# hash input should be a bytestring
			if args.input_file:
						with open(args.input_file       ,				"rb"							) as f:
									lowerCAmelCase_         =      f.read()
			else:
						lowerCAmelCase_         =      bytes(__lowerCAmelCase       ,				"utf-8"							)

			print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
			main()



