
"""simple docstring"""

import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase			:       List[Any]									=				logging.get_logger(__name__)






def 							A		(       snake_case   :Optional[int]	)   ->			Any:
 print('Loading config file...'	)

 def flatten_yaml_as_dict(snake_case   :Optional[int]       ,      snake_case   :List[str]=""       ,      snake_case   :str="."	):
  __UpperCamelCase       						= []
  for k, v in d.items():
   __UpperCamelCase       						= parent_key + sep + k if parent_key else k
   if isinstance(a_       ,      collections.abc.MutableMapping	):
    items.extend(flatten_yaml_as_dict(a_       ,      a_       ,      sep=a_	).items()	)
   else:
    items.append((new_key, v)	)
  return dict(a_	)

 __UpperCamelCase       						= argparse.Namespace()
 with open(a_       ,      'r'	) as yaml_file:
  try:
   __UpperCamelCase       						= yaml.load(a_       ,      Loader=yaml.FullLoader	)

   __UpperCamelCase       						= flatten_yaml_as_dict(a_	)
   for k, v in flat_cfg.items():
    setattr(a_       ,      a_       ,      a_	)
  except yaml.YAMLError as exc:
   logger.error('Error while loading config file: {}. Error message: {}'.format(a_       ,      str(a_	)	)	)
 return config






def 							A		(       snake_case   :Any       ,      snake_case   :Any	)   ->			Tuple:
 __UpperCamelCase       						= MobileViTVaConfig()

 __UpperCamelCase       						= False

 # dataset
 if task_name.startswith('imagenet1k_'	):
  __UpperCamelCase       						= 1_0_0_0
  if int(task_name.strip().split('_'	)[-1]	) == 3_8_4:
   __UpperCamelCase       						= 3_8_4
  else:
   __UpperCamelCase       						= 2_5_6
  __UpperCamelCase       						= '''imagenet-1k-id2label.json'''
 elif task_name.startswith('imagenet21k_to_1k_'	):
  __UpperCamelCase       						= 2_1_0_0_0
  if int(task_name.strip().split('_'	)[-1]	) == 3_8_4:
   __UpperCamelCase       						= 3_8_4
  else:
   __UpperCamelCase       						= 2_5_6
  __UpperCamelCase       						= '''imagenet-22k-id2label.json'''
 elif task_name.startswith('ade20k_'	):
  __UpperCamelCase       						= 1_5_1
  __UpperCamelCase       						= 5_1_2
  __UpperCamelCase       						= '''ade20k-id2label.json'''
  __UpperCamelCase       						= True
 elif task_name.startswith('voc_'	):
  __UpperCamelCase       						= 2_1
  __UpperCamelCase       						= 5_1_2
  __UpperCamelCase       						= '''pascal-voc-id2label.json'''
  __UpperCamelCase       						= True

 # orig_config
 __UpperCamelCase       						= load_orig_config_file(a_	)
 assert getattr(a_       ,      'model.classification.name'       ,      -1	) == "mobilevit_v2", "Invalid model"
 __UpperCamelCase       						= getattr(a_       ,      'model.classification.mitv2.width_multiplier'       ,      1.0	)
 assert (
     getattr(a_       ,      'model.classification.mitv2.attn_norm_layer'       ,      -1	) == "layer_norm_2d"
 ), "Norm layers other than layer_norm_2d is not supported"
 __UpperCamelCase       						= getattr(a_       ,      'model.classification.activation.name'       ,      'swish'	)
 # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

 if is_segmentation_model:
  __UpperCamelCase       						= getattr(a_       ,      'model.segmentation.output_stride'       ,      1_6	)
  if "_deeplabv3" in task_name:
   __UpperCamelCase       						= getattr(a_       ,      'model.segmentation.deeplabv3.aspp_rates'       ,      [1_2, 2_4, 3_6]	)
   __UpperCamelCase       						= getattr(a_       ,      'model.segmentation.deeplabv3.aspp_out_channels'       ,      5_1_2	)
   __UpperCamelCase       						= getattr(a_       ,      'model.segmentation.deeplabv3.aspp_dropout'       ,      0.1	)

    # id2label
 __UpperCamelCase       						= '''huggingface/label-files'''
 __UpperCamelCase       						= json.load(open(hf_hub_download(a_       ,      a_       ,      repo_type='dataset'	)       ,      'r'	)	)
 __UpperCamelCase       						= {int(a_	): v for k, v in idalabel.items()}
 __UpperCamelCase       						= idalabel
 __UpperCamelCase       						= {v: k for k, v in idalabel.items()}

 return config






def 							A		(       snake_case   :str       ,      snake_case   :Optional[int]       ,      snake_case   :Any	)   ->			int:
 __UpperCamelCase       						= dct.pop(a_	)
 __UpperCamelCase       						= val






def 							A		(       snake_case   :str       ,      snake_case   :Dict=False	)   ->			int:
 if base_model:
  __UpperCamelCase       						= ''''''
 else:
  __UpperCamelCase       						= '''mobilevitv2.'''

 __UpperCamelCase       						= []
 for k in state_dict.keys():
  if k[:8] == "encoder.":
   __UpperCamelCase       						= k[8:]
  else:
   __UpperCamelCase       						= k

  if ".block." in k:
   __UpperCamelCase       						= k_new.replace('.block.'       ,      '.'	)
  if ".conv." in k:
   __UpperCamelCase       						= k_new.replace('.conv.'       ,      '.convolution.'	)
  if ".norm." in k:
   __UpperCamelCase       						= k_new.replace('.norm.'       ,      '.normalization.'	)

  if "conv_1." in k:
   __UpperCamelCase       						= k_new.replace('conv_1.'       ,      f'{model_prefix}conv_stem.'	)
  for i in [1, 2]:
   if f'layer_{i}.' in k:
    __UpperCamelCase       						= k_new.replace(f'layer_{i}.'       ,      f'{model_prefix}encoder.layer.{i-1}.layer.'	)
  if ".exp_1x1." in k:
   __UpperCamelCase       						= k_new.replace('.exp_1x1.'       ,      '.expand_1x1.'	)
  if ".red_1x1." in k:
   __UpperCamelCase       						= k_new.replace('.red_1x1.'       ,      '.reduce_1x1.'	)

  for i in [3, 4, 5]:
   if f'layer_{i}.0.' in k:
    __UpperCamelCase       						= k_new.replace(f'layer_{i}.0.'       ,      f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.'	)
   if f'layer_{i}.1.local_rep.0.' in k:
    __UpperCamelCase       						= k_new.replace(f'layer_{i}.1.local_rep.0.'       ,      f'{model_prefix}encoder.layer.{i-1}.conv_kxk.'	)
   if f'layer_{i}.1.local_rep.1.' in k:
    __UpperCamelCase       						= k_new.replace(f'layer_{i}.1.local_rep.1.'       ,      f'{model_prefix}encoder.layer.{i-1}.conv_1x1.'	)

  for i in [3, 4, 5]:
   if i == 3:
    __UpperCamelCase       						= [0, 1]
   elif i == 4:
    __UpperCamelCase       						= [0, 1, 2, 3]
   elif i == 5:
    __UpperCamelCase       						= [0, 1, 2]

   for j in j_in:
    if f'layer_{i}.1.global_rep.{j}.' in k:
     __UpperCamelCase       						= k_new.replace(
         f'layer_{i}.1.global_rep.{j}.'       ,      f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'	)
   if f'layer_{i}.1.global_rep.{j+1}.' in k:
    __UpperCamelCase       						= k_new.replace(
        f'layer_{i}.1.global_rep.{j+1}.'       ,      f'{model_prefix}encoder.layer.{i-1}.layernorm.'	)

   if f'layer_{i}.1.conv_proj.' in k:
    __UpperCamelCase       						= k_new.replace(f'layer_{i}.1.conv_proj.'       ,      f'{model_prefix}encoder.layer.{i-1}.conv_projection.'	)

  if "pre_norm_attn.0." in k:
   __UpperCamelCase       						= k_new.replace('pre_norm_attn.0.'       ,      'layernorm_before.'	)
  if "pre_norm_attn.1." in k:
   __UpperCamelCase       						= k_new.replace('pre_norm_attn.1.'       ,      'attention.'	)
  if "pre_norm_ffn.0." in k:
   __UpperCamelCase       						= k_new.replace('pre_norm_ffn.0.'       ,      'layernorm_after.'	)
  if "pre_norm_ffn.1." in k:
   __UpperCamelCase       						= k_new.replace('pre_norm_ffn.1.'       ,      'ffn.conv1.'	)
  if "pre_norm_ffn.3." in k:
   __UpperCamelCase       						= k_new.replace('pre_norm_ffn.3.'       ,      'ffn.conv2.'	)

  if "classifier.1." in k:
   __UpperCamelCase       						= k_new.replace('classifier.1.'       ,      'classifier.'	)

  if "seg_head." in k:
   __UpperCamelCase       						= k_new.replace('seg_head.'       ,      'segmentation_head.'	)
  if ".aspp_layer." in k:
   __UpperCamelCase       						= k_new.replace('.aspp_layer.'       ,      '.'	)
  if ".aspp_pool." in k:
   __UpperCamelCase       						= k_new.replace('.aspp_pool.'       ,      '.'	)

  rename_keys.append((k, k_new)	)
 return rename_keys






def 							A		(       snake_case   :Tuple	)   ->			str:
 __UpperCamelCase       						= []
 for k in state_dict.keys():
  if k.startswith('seg_head.aux_head.'	):
   keys_to_ignore.append(a_	)
 for k in keys_to_ignore:
  state_dict.pop(a_       ,      a_	)






def 							A		(       )   ->			Tuple:
 __UpperCamelCase       						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
 __UpperCamelCase       						= Image.open(requests.get(a_       ,      stream=a_	).raw	)
 return im






@torch.no_grad()
def 							A		(       snake_case   :Tuple       ,      snake_case   :str       ,      snake_case   :List[str]       ,      snake_case   :Optional[int]	)   ->			str:
 __UpperCamelCase       						= get_mobilevitva_config(a_       ,      a_	)

 # load original state_dict
 __UpperCamelCase       						= torch.load(a_       ,      map_location='cpu'	)

 # load huggingface model
 if task_name.startswith('ade20k_'	) or task_name.startswith('voc_'	):
  __UpperCamelCase       						= MobileViTVaForSemanticSegmentation(a_	).eval()
  __UpperCamelCase       						= False
 else:
  __UpperCamelCase       						= MobileViTVaForImageClassification(a_	).eval()
  __UpperCamelCase       						= False

 # remove and rename some keys of load the original model
 __UpperCamelCase       						= checkpoint
 remove_unused_keys(a_	)
 __UpperCamelCase       						= create_rename_keys(a_       ,      base_model=a_	)
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(a_       ,      a_       ,      a_	)

 # load modified state_dict
 model.load_state_dict(a_	)

 # Check outputs on an image, prepared by MobileViTImageProcessor
 __UpperCamelCase       						= MobileViTImageProcessor(crop_size=config.image_size       ,      size=config.image_size + 3_2	)
 __UpperCamelCase       						= image_processor(images=prepare_img()       ,      return_tensors='pt'	)
 __UpperCamelCase       						= model(**a_	)

 # verify classification model
 if task_name.startswith('imagenet'	):
  __UpperCamelCase       						= outputs.logits
  __UpperCamelCase       						= logits.argmax(-1	).item()
  print('Predicted class:'       ,      model.config.idalabel[predicted_class_idx]	)
  if task_name.startswith('imagenet1k_256'	) and config.width_multiplier == 1.0:
   # expected_logits for base variant
   __UpperCamelCase       						= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]	)
   assert torch.allclose(logits[0, :3]       ,      a_       ,      atol=1e-4	)

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


if __name__ == "__main__":
      UpperCamelCase			:       Union[str, Any]									=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--task",
          default="imagenet1k_256",
          type=str,
          help=(
              "Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . "
              "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
          ),
          choices=[
              "imagenet1k_256",
              "imagenet1k_384",
              "imagenet21k_to_1k_256",
              "imagenet21k_to_1k_384",
              "ade20k_deeplabv3",
              "voc_deeplabv3",
          ],
      )

      parser.add_argument(
          "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
      )
      parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
      parser.add_argument(
          "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
      )

      UpperCamelCase			:       Tuple									=				parser.parse_args()
      convert_mobilevitva_checkpoint(
          args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
      )





"""simple docstring"""

import argparse
import struct
import unittest

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= data

      # Initialize hash values
      __UpperCamelCase       						= [
          0x6a_09_e6_67,
          0xbb_67_ae_85,
          0x3c_6e_f3_72,
          0xa5_4f_f5_3a,
          0x51_0e_52_7f,
          0x9b_05_68_8c,
          0x1f_83_d9_ab,
          0x5b_e0_cd_19,
      ]

      # Initialize round constants
      __UpperCamelCase       						= [
          0x42_8a_2f_98,
          0x71_37_44_91,
          0xb5_c0_fb_cf,
          0xe9_b5_db_a5,
          0x39_56_c2_5b,
          0x59_f1_11_f1,
          0x92_3f_82_a4,
          0xab_1c_5e_d5,
          0xd8_07_aa_98,
          0x12_83_5b_01,
          0x24_31_85_be,
          0x55_0c_7d_c3,
          0x72_be_5d_74,
          0x80_de_b1_fe,
          0x9b_dc_06_a7,
          0xc1_9b_f1_74,
          0xe4_9b_69_c1,
          0xef_be_47_86,
          0x0f_c1_9d_c6,
          0x24_0c_a1_cc,
          0x2d_e9_2c_6f,
          0x4a_74_84_aa,
          0x5c_b0_a9_dc,
          0x76_f9_88_da,
          0x98_3e_51_52,
          0xa8_31_c6_6d,
          0xb0_03_27_c8,
          0xbf_59_7f_c7,
          0xc6_e0_0b_f3,
          0xd5_a7_91_47,
          0x06_ca_63_51,
          0x14_29_29_67,
          0x27_b7_0a_85,
          0x2e_1b_21_38,
          0x4d_2c_6d_fc,
          0x53_38_0d_13,
          0x65_0a_73_54,
          0x76_6a_0a_bb,
          0x81_c2_c9_2e,
          0x92_72_2c_85,
          0xa2_bf_e8_a1,
          0xa8_1a_66_4b,
          0xc2_4b_8b_70,
          0xc7_6c_51_a3,
          0xd1_92_e8_19,
          0xd6_99_06_24,
          0xf4_0e_35_85,
          0x10_6a_a0_70,
          0x19_a4_c1_16,
          0x1e_37_6c_08,
          0x27_48_77_4c,
          0x34_b0_bc_b5,
          0x39_1c_0c_b3,
          0x4e_d8_aa_4a,
          0x5b_9c_ca_4f,
          0x68_2e_6f_f3,
          0x74_8f_82_ee,
          0x78_a5_63_6f,
          0x84_c8_78_14,
          0x8c_c7_02_08,
          0x90_be_ff_fa,
          0xa4_50_6c_eb,
          0xbe_f9_a3_f7,
          0xc6_71_78_f2,
      ]

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





     @staticmethod
     def 		UpperCAmelCase      (			__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= b'\x80' + (b'\x00' * (63 - (len(__UpperCAmelCase   ) + 8) % 64))
      __UpperCamelCase       						= struct.pack('>Q'			,  (len(__UpperCAmelCase   ) * 8)   )
      return data + padding + big_endian_integer





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= [
          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
       __UpperCamelCase       						= list(struct.unpack('>16L'			,  __UpperCAmelCase   )   )
       # add 48 0-ed integers
       words += [0] * 48

       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= self.hashes

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

         __UpperCamelCase       						= (
             words[index - 16] + sa + words[index - 7] + sa
         ) % 0x1_00_00_00_00

        # Compression
        __UpperCamelCase       						= self.ror(__UpperCAmelCase			,  6   ) ^ self.ror(__UpperCAmelCase			,  11   ) ^ self.ror(__UpperCAmelCase			,  25   )
        __UpperCamelCase       						= (e & f) ^ ((~e & 0xff_ff_ff_ff) & g)
        __UpperCamelCase       						= (
            h + sa + ch + self.round_constants[index] + words[index]
        ) % 0x1_00_00_00_00
        __UpperCamelCase       						= self.ror(__UpperCAmelCase			,  2   ) ^ self.ror(__UpperCAmelCase			,  13   ) ^ self.ror(__UpperCAmelCase			,  22   )
        __UpperCamelCase       						= (a & b) ^ (a & c) ^ (b & c)
        __UpperCamelCase       						= (sa + maj) % 0x1_00_00_00_00

        __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= (
            g,
            f,
            e,
            ((d + tempa) % 0x1_00_00_00_00),
            c,
            b,
            a,
            ((tempa + tempa) % 0x1_00_00_00_00),
        )

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

       # Modify final values
       __UpperCamelCase       						= [
           ((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
           for index, element in enumerate(self.hashes   )
       ]

      __UpperCamelCase       						= ''.join([hex(__UpperCAmelCase   )[2:].zfill(8   ) for value in self.hashes]   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return 0xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)


class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      import hashlib

      __UpperCamelCase       						= bytes('Test String'			,  'utf-8'   )
      self.assertEqual(SHAaaa(__UpperCAmelCase   ).hash			,  hashlib.shaaaa(__UpperCAmelCase   ).hexdigest()   )






def 							A		(       )   ->			None:
 import doctest

 doctest.testmod()

 __UpperCamelCase       						= 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'	)

 __UpperCamelCase       						= parser.parse_args()

 __UpperCamelCase       						= args.input_string

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

 print(SHAaaa(snake_case	).hash	)


if __name__ == "__main__":
      main()



