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


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






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[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"""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"),
						        ("pre_logits.fc.weight", "pooler.dense.weight"),
						        ("pre_logits.fc.bias", "pooler.dense.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   lowerCamelCase__							(							__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    Tuple=False							):




			"""simple docstring"""


			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   lowerCamelCase__							(							__lowerCAmelCase    :    str							):




			"""simple docstring"""


			lowerCAmelCase_         =      ["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(__lowerCAmelCase       ,				__lowerCAmelCase							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      dct.pop(__lowerCAmelCase							)
			lowerCAmelCase_         =      val






def   lowerCamelCase__							(							):




			"""simple docstring"""


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






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      ViTConfig()
			lowerCAmelCase_         =      False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						lowerCAmelCase_         =      True
						lowerCAmelCase_         =      int(vit_name[-12:-10]							)
						lowerCAmelCase_         =      int(vit_name[-9:-6]							)
			else:
						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()}
						lowerCAmelCase_         =      int(vit_name[-6:-4]							)
						lowerCAmelCase_         =      int(vit_name[-3:]							)
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith("tiny"							):
									lowerCAmelCase_         =      192
									lowerCAmelCase_         =      768
									lowerCAmelCase_         =      12
									lowerCAmelCase_         =      3
						elif vit_name[9:].startswith("small"							):
									lowerCAmelCase_         =      384
									lowerCAmelCase_         =      1536
									lowerCAmelCase_         =      12
									lowerCAmelCase_         =      6
						else:
									pass
			else:
						if vit_name[4:].startswith("small"							):
									lowerCAmelCase_         =      768
									lowerCAmelCase_         =      2304
									lowerCAmelCase_         =      8
									lowerCAmelCase_         =      8
						elif vit_name[4:].startswith("base"							):
									pass
						elif vit_name[4:].startswith("large"							):
									lowerCAmelCase_         =      1024
									lowerCAmelCase_         =      4096
									lowerCAmelCase_         =      24
									lowerCAmelCase_         =      16
						elif vit_name[4:].startswith("huge"							):
									lowerCAmelCase_         =      1280
									lowerCAmelCase_         =      5120
									lowerCAmelCase_         =      32
									lowerCAmelCase_         =      16

    # load original model from timm
			lowerCAmelCase_         =      timm.create_model(__lowerCAmelCase       ,				pretrained=__lowerCAmelCase							)
			timm_model.eval()

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						lowerCAmelCase_         =      ViTModel(__lowerCAmelCase							).eval()
			else:
						lowerCAmelCase_         =      ViTForImageClassification(__lowerCAmelCase							).eval()
			model.load_state_dict(__lowerCAmelCase							)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						lowerCAmelCase_         =      DeiTImageProcessor(size=config.image_size							)
			else:
						lowerCAmelCase_         =      ViTImageProcessor(size=config.image_size							)
			lowerCAmelCase_         =      image_processor(images=prepare_img()       ,				return_tensors="pt"							)
			lowerCAmelCase_         =      encoding["pixel_values"]
			lowerCAmelCase_         =      model(__lowerCAmelCase							)

			if base_model:
						lowerCAmelCase_         =      timm_model.forward_features(__lowerCAmelCase							)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(__lowerCAmelCase       ,				outputs.pooler_output       ,				atol=1e-3							)
			else:
						lowerCAmelCase_         =      timm_model(__lowerCAmelCase							)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(__lowerCAmelCase       ,				outputs.logits       ,				atol=1e-3							)

			Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
			print(F"""Saving model {vit_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__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--vit_name",
			    default="vit_base_patch16_224",
			    type=str,
			    help="Name of the ViT 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  =	parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





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()



