

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





     if not isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
          raise TypeError('plaintext must be a string'    )
     if not isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
          raise TypeError('key must be a string'    )

     if not plaintext:
          raise ValueError('plaintext is empty'    )
     if not key:
          raise ValueError('key is empty'    )

     key += plaintext
     lowercase__					:     int   = plaintext.lower()
     lowercase__					:     Any   = key.lower()
     lowercase__					:     int   = 0
     lowercase__					:     Optional[int]   = 0
     lowercase__					:     List[str]   = ''
     while plaintext_iterator < len(SCREAMING_SNAKE_CASE_    ):
          if (
              ord(plaintext[plaintext_iterator]    ) < 97
              or ord(plaintext[plaintext_iterator]    ) > 122
          ):
               ciphertext += plaintext[plaintext_iterator]
               plaintext_iterator += 1
          elif ord(key[key_iterator]    ) < 97 or ord(key[key_iterator]    ) > 122:
               key_iterator += 1
          else:
               ciphertext += chr(
                   (
                       (ord(plaintext[plaintext_iterator]    ) - 97 + ord(key[key_iterator]    ))
                       - 97
                   )
                   % 26
                   + 97    )
               key_iterator += 1
               plaintext_iterator += 1
     return ciphertext

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





     if not isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
          raise TypeError('ciphertext must be a string'    )
     if not isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
          raise TypeError('key must be a string'    )

     if not ciphertext:
          raise ValueError('ciphertext is empty'    )
     if not key:
          raise ValueError('key is empty'    )

     lowercase__					:     List[Any]   = key.lower()
     lowercase__					:     List[Any]   = 0
     lowercase__					:     Tuple   = 0
     lowercase__					:     Dict   = ''
     while ciphertext_iterator < len(SCREAMING_SNAKE_CASE_    ):
          if (
              ord(ciphertext[ciphertext_iterator]    ) < 97
              or ord(ciphertext[ciphertext_iterator]    ) > 122
          ):
               plaintext += ciphertext[ciphertext_iterator]
          else:
               plaintext += chr(
                   (ord(ciphertext[ciphertext_iterator]    ) - ord(key[key_iterator]    )) % 26
                   + 97    )
               key += chr(
                   (ord(ciphertext[ciphertext_iterator]    ) - ord(key[key_iterator]    )) % 26
                   + 97    )
               key_iterator += 1
          ciphertext_iterator += 1
     return plaintext


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case_    						=				int(input('''Type 1 to encrypt or 2 to decrypt:'''))
       if operation == 1:
              snake_case_    						=				input('''Typeplaintext to be encrypted:\n''')
              snake_case_    						=				input('''Type the key:\n''')
              print(encrypt(plaintext, key))
       elif operation == 2:
              snake_case_    						=				input('''Type the ciphertext to be decrypted:\n''')
              snake_case_    						=				input('''Type the key:\n''')
              print(decrypt(ciphertext, key))
       decrypt('''jsqqs avvwo''', '''coffee''')




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					int=False			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]="data2vec."    ):



     '''simple docstring'''





     lowercase__					:     str   = 'backbone.' if is_semantic else ''

     lowercase__					:     Dict   = []
     for i in range(config.num_hidden_layers    ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")    )
          rename_keys.append(
              (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")    )
          rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")    )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
             (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
             (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
         ]    )

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', f"""{hf_prefix}embeddings.mask_token"""),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]    )
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
              ]    )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', f"""{hf_prefix}pooler.layernorm.weight"""),
                  ('fc_norm.bias', f"""{hf_prefix}pooler.layernorm.bias"""),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]    )

     return rename_keys

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					Dict=False			,  SCREAMING_SNAKE_CASE_	:					List[str]="data2vec_vision."    ):



     '''simple docstring'''





     for i in range(config.num_hidden_layers    ):
          lowercase__					:     Optional[int]   = 'backbone.' if is_semantic else ''
          # queries, keys and values
          lowercase__					:     List[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""    )
          lowercase__					:     Optional[int]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""    )
          lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""    )

          lowercase__					:     Union[str, Any]   = in_proj_weight[
              : config.hidden_size, :
          ]
          lowercase__					:     List[str]   = q_bias
          lowercase__					:     Tuple   = in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase__					:     Union[str, Any]   = in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase__					:     Union[str, Any]   = v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""    )
          lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""    )

          lowercase__					:     List[str]   = gamma_a
          lowercase__					:     Dict   = gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               lowercase__					:     Tuple   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""    )
               lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""    )

               lowercase__					:     int   = table
               lowercase__					:     List[str]   = index

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     List[str]   = argparse.ArgumentParser(
         'Convert Data2VecVision to HF for image classification and pretraining'			,  add_help=SCREAMING_SNAKE_CASE_    )
     parser.add_argument('--hf_checkpoint_name'			,  type=SCREAMING_SNAKE_CASE_    )
     parser.add_argument('--input_size'			,  default=224			,  type=SCREAMING_SNAKE_CASE_			,  help='images input size'    )
     parser.add_argument('--beit_checkpoint'			,  default=''			,  help='beit checkpoint'    )

     return parser.parse_args()

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





     def load_state_dict(SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""			,  SCREAMING_SNAKE_CASE_	:					List[Any]="relative_position_index"    ):
          lowercase__					:     str   = []
          lowercase__					:     str   = []
          lowercase__					:     Dict   = []
          # copy state_dict so _load_from_state_dict can modify it
          lowercase__					:     List[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  '_metadata'			,  SCREAMING_SNAKE_CASE_    )
          lowercase__					:     Optional[int]   = state_dict.copy()
          if metadata is not None:
               lowercase__					:     Dict   = metadata

          def load(SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""    ):
               lowercase__					:     Optional[Any]   = {} if metadata is None else metadata.get(prefix[:-1]			,  {}    )
               module._load_from_state_dict(
                   SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
               for name, child in module._modules.items():
                    if child is not None:
                         load(SCREAMING_SNAKE_CASE_			,  prefix + name + '.'    )

          load(SCREAMING_SNAKE_CASE_			,  prefix=SCREAMING_SNAKE_CASE_    )

          lowercase__					:     List[Any]   = []
          lowercase__					:     Dict   = []
          for key in missing_keys:
               lowercase__					:     Any   = True
               for ignore_key in ignore_missing.split('|'    ):
                    if ignore_key in key:
                         lowercase__					:     Union[str, Any]   = False
                         break
               if keep_flag:
                    warn_missing_keys.append(SCREAMING_SNAKE_CASE_    )
               else:
                    ignore_missing_keys.append(SCREAMING_SNAKE_CASE_    )

          lowercase__					:     Tuple   = warn_missing_keys

          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print(
                   'Weights of {} not initialized from pretrained model: {}'.format(
                       model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print(
                   'Ignored weights of {} not initialized from pretrained model: {}'.format(
                       model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     lowercase__					:     int   = {
         'pretrained': False,
         'use_shared_rel_pos_bias': True,
         'use_abs_pos_emb': False,
         'init_values': 0.1,
     }

     if is_finetuned:
          model_kwargs.update(
              {
                  'num_classes': 1_000,
                  'use_mean_pooling': True,
                  'init_scale': 0.001,
                  'use_rel_pos_bias': True,
              }    )

     lowercase__					:     Dict   = create_model(
         'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'			,  **SCREAMING_SNAKE_CASE_			,  )
     lowercase__					:     int   = model.patch_embed.patch_size
     lowercase__					:     int   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
     lowercase__					:     str   = torch.load(args.beit_checkpoint			,  map_location='cpu'    )

     print(f"""Load ckpt from {args.beit_checkpoint}"""    )
     lowercase__					:     int   = None
     for model_key in ("model", "module"):
          if model_key in checkpoint:
               lowercase__					:     Tuple   = checkpoint[model_key]
               print(f"""Load state_dict by model_key = {model_key}"""    )
               break

     lowercase__					:     Optional[Any]   = list(checkpoint_model.keys()    )
     for key in all_keys:
          if "relative_position_index" in key:
               checkpoint_model.pop(SCREAMING_SNAKE_CASE_    )

          if "relative_position_bias_table" in key:
               lowercase__					:     Any   = checkpoint_model[key]
               lowercase__   ,				lowercase__					:     List[Any]   = rel_pos_bias.size()
               lowercase__   ,				lowercase__					:     Optional[int]   = model.state_dict()[key].size()
               lowercase__					:     Dict   = model.patch_embed.patch_shape
               if dst_patch_shape[0] != dst_patch_shape[1]:
                    raise NotImplementedError()

     load_state_dict(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  prefix=''    )

     return model

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     List[str]   = get_args()

     lowercase__					:     Union[str, Any]   = 'ft1k' in args.hf_checkpoint_name
     lowercase__					:     Dict   = 'large' in args.hf_checkpoint_name

     if is_finetuned:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
          # into this folder.
          import modeling_finetune  # noqa: F401
     else:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
          # into this folder
          # IMPORTANT: Note that for now we've only converted the down-stream
          # model and not the full pretrained model. This means for the integration
          # test you need to add a `return x` after the following line:
          # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
          # to make the integration test pass.
          import modeling_cyclical  # noqa: F401

     # 1. Create model config
     lowercase__					:     Optional[Any]   = DataaVecVisionConfig()
     if is_finetuned:
          lowercase__					:     int   = True
          lowercase__					:     Any   = False
          lowercase__					:     Union[str, Any]   = True
          lowercase__					:     Union[str, Any]   = 1_000

          lowercase__					:     Any   = 'huggingface/label-files'
          lowercase__					:     Any   = 'imagenet-1k-id2label.json'
          lowercase__					:     Union[str, Any]   = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  repo_type='dataset'    )			,  'r'    )    )
          lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
          lowercase__					:     Optional[int]   = idalabel
          lowercase__					:     Optional[int]   = {v: k for k, v in idalabel.items()}
     else:
          lowercase__					:     Dict   = False
          lowercase__					:     int   = True
          lowercase__					:     List[str]   = False

     if is_large:
          lowercase__					:     List[str]   = 1_024
          lowercase__					:     Union[str, Any]   = 4_096
          lowercase__					:     Union[str, Any]   = 24
          lowercase__					:     Optional[int]   = 16

     # 2. Load Beit model
     lowercase__					:     List[Any]   = load_beit_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
     orig_model.eval()

     # 3. Forward Beit model
     lowercase__					:     Optional[Any]   = BeitImageProcessor(size=config.image_size			,  do_center_crop=SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Tuple   = Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'    )
     lowercase__					:     Tuple   = image_processor(images=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    )
     lowercase__					:     List[Any]   = encoding['pixel_values']

     lowercase__					:     List[Any]   = (pixel_values,) if is_finetuned else (pixel_values, None)
     with torch.no_grad():
          lowercase__					:     List[str]   = orig_model(*SCREAMING_SNAKE_CASE_    )

     # 4. Load HF Data2VecVision model
     if is_finetuned:
          lowercase__					:     Optional[int]   = DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_    )
          hf_model.eval()
          lowercase__					:     Union[str, Any]   = False
          lowercase__					:     Dict   = 'data2vec_vision.'
     else:
          lowercase__					:     Tuple   = DataaVecVisionModel(SCREAMING_SNAKE_CASE_    )
          hf_model.eval()
          lowercase__					:     int   = True
          lowercase__					:     Tuple   = ''

     lowercase__					:     Tuple   = create_rename_keys(SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
     lowercase__					:     str   = orig_model.state_dict()
     for src, dest in rename_keys:
          lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
          lowercase__					:     Dict   = val

     read_in_q_k_v(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
     lowercase__   ,				lowercase__					:     List[Any]   = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_			,  strict=SCREAMING_SNAKE_CASE_    )
     print('HF missing'			,  SCREAMING_SNAKE_CASE_    )
     print('HF unexpected_keys'			,  SCREAMING_SNAKE_CASE_    )

     # 5. Forward HF Data2VecVision model
     with torch.no_grad():
          lowercase__					:     Optional[int]   = hf_model(SCREAMING_SNAKE_CASE_    )

     lowercase__					:     Optional[int]   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

     # 6. Compare
     lowercase__					:     Dict   = torch.max(torch.abs(hf_output - orig_model_output    )    ).item()

     print(f"""max_absolute_diff = {max_absolute_diff}"""    )
     lowercase__					:     Dict   = torch.allclose(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  atol=1E-3    )
     print('Do both models output the same tensors?'			,  '🔥' if success else '💩'    )
     if not success:
          raise Exception('Something went wRoNg'    )

     # 7. Save
     print(f"""Saving to {args.hf_checkpoint_name}"""    )
     hf_model.save_pretrained(args.hf_checkpoint_name    )
     image_processor.save_pretrained(args.hf_checkpoint_name    )


if __name__ == "__main__":
       main()
       # Run the following to convert checkpoints
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

