






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


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


def 					snake_case__				(				UpperCAmelCase				:	Tuple							):
    lowerCAmelCase__ :Optional[Any]   				=					["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(UpperCAmelCase     ,      UpperCAmelCase							)


def 					snake_case__				(				UpperCAmelCase				:	int     ,      UpperCAmelCase				:	Union[str, Any]     ,      UpperCAmelCase				:	Optional[int]							):
    lowerCAmelCase__ :List[Any]   				=					dct.pop(UpperCAmelCase							)
    lowerCAmelCase__ :Any   				=					val


def 					snake_case__				(				):
    lowerCAmelCase__ :str   				=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase__ :str   				=					Image.open(requests.get(UpperCAmelCase     ,      stream=UpperCAmelCase							).raw							)
    return im


@torch.no_grad()
def 					snake_case__				(				UpperCAmelCase				:	Optional[Any]     ,      UpperCAmelCase				:	Optional[Any]							):
    lowerCAmelCase__ :int   				=					ViTConfig()
    lowerCAmelCase__ :List[str]   				=					False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        lowerCAmelCase__ :Tuple   				=					True
        lowerCAmelCase__ :List[str]   				=					int(vit_name[-1_2:-1_0]							)
        lowerCAmelCase__ :int   				=					int(vit_name[-9:-6]							)
    else:
        lowerCAmelCase__ :Optional[Any]   				=					1_0_0_0
        lowerCAmelCase__ :Any   				=					"""huggingface/label-files"""
        lowerCAmelCase__ :Any   				=					"""imagenet-1k-id2label.json"""
        lowerCAmelCase__ :Dict   				=					json.load(open(hf_hub_download(UpperCAmelCase     ,      UpperCAmelCase     ,      repo_type="dataset"							)     ,      "r"							)							)
        lowerCAmelCase__ :List[Any]   				=					{int(UpperCAmelCase							): v for k, v in idalabel.items()}
        lowerCAmelCase__ :str   				=					idalabel
        lowerCAmelCase__ :int   				=					{v: k for k, v in idalabel.items()}
        lowerCAmelCase__ :Any   				=					int(vit_name[-6:-4]							)
        lowerCAmelCase__ :Union[str, Any]   				=					int(vit_name[-3:]							)
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("tiny"							):
            lowerCAmelCase__ :Tuple   				=					1_9_2
            lowerCAmelCase__ :Tuple   				=					7_6_8
            lowerCAmelCase__ :str   				=					1_2
            lowerCAmelCase__ :int   				=					3
        elif vit_name[9:].startswith("small"							):
            lowerCAmelCase__ :Any   				=					3_8_4
            lowerCAmelCase__ :Union[str, Any]   				=					1_5_3_6
            lowerCAmelCase__ :Tuple   				=					1_2
            lowerCAmelCase__ :str   				=					6
        else:
            pass
    else:
        if vit_name[4:].startswith("small"							):
            lowerCAmelCase__ :Optional[int]   				=					7_6_8
            lowerCAmelCase__ :str   				=					2_3_0_4
            lowerCAmelCase__ :Optional[int]   				=					8
            lowerCAmelCase__ :List[Any]   				=					8
        elif vit_name[4:].startswith("base"							):
            pass
        elif vit_name[4:].startswith("large"							):
            lowerCAmelCase__ :List[str]   				=					1_0_2_4
            lowerCAmelCase__ :List[Any]   				=					4_0_9_6
            lowerCAmelCase__ :Dict   				=					2_4
            lowerCAmelCase__ :Optional[int]   				=					1_6
        elif vit_name[4:].startswith("huge"							):
            lowerCAmelCase__ :Any   				=					1_2_8_0
            lowerCAmelCase__ :List[str]   				=					5_1_2_0
            lowerCAmelCase__ :Any   				=					3_2
            lowerCAmelCase__ :List[Any]   				=					1_6

    # load original model from timm
    lowerCAmelCase__ :Union[str, Any]   				=					timm.create_model(UpperCAmelCase     ,      pretrained=UpperCAmelCase							)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    lowerCAmelCase__ :Tuple   				=					timm_model.state_dict()
    if base_model:
        remove_classification_head_(UpperCAmelCase							)
    lowerCAmelCase__ :str   				=					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
    if vit_name[-5:] == "in21k":
        lowerCAmelCase__ :Optional[Any]   				=					ViTModel(UpperCAmelCase							).eval()
    else:
        lowerCAmelCase__ :Optional[int]   				=					ViTForImageClassification(UpperCAmelCase							).eval()
    model.load_state_dict(UpperCAmelCase							)

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

    if base_model:
        lowerCAmelCase__ :Dict   				=					timm_model.forward_features(UpperCAmelCase							)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(UpperCAmelCase     ,      outputs.pooler_output     ,      atol=1E-3							)
    else:
        lowerCAmelCase__ :int   				=					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 {vit_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     : Optional[int]   	=      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     : List[Any]   	=      parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






"""simple docstring"""
_A							=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def 					a__						(						)	-> None:
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter message: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter key [alphanumeric]: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Encrypt/Decrypt [e/d]: """    )

      if mode.lower().startswith("""e"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """encrypt"""
            UpperCAmelCase__			:							List[Any]							=    encrypt_message(lowerCAmelCase			,	lowerCAmelCase    )
      elif mode.lower().startswith("""d"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """decrypt"""
            UpperCAmelCase__			:							Dict							=    decrypt_message(lowerCAmelCase			,	lowerCAmelCase    )

      print(F"""\n{mode.title()}ed message:"""    )
      print(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""encrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""decrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							Optional[Any]							=    []
      UpperCAmelCase__			:							int							=    0
      UpperCAmelCase__			:							str							=    key.upper()

      for symbol in message:
            UpperCAmelCase__			:							Optional[Any]							=    LETTERS.find(symbol.upper()    )
            if num != -1:
                  if mode == "encrypt":
                        num += LETTERS.find(key[key_index]    )
                  elif mode == "decrypt":
                        num -= LETTERS.find(key[key_index]    )

                  num %= len(lowerCAmelCase    )

                  if symbol.isupper():
                        translated.append(LETTERS[num]    )
                  elif symbol.islower():
                        translated.append(LETTERS[num].lower()    )

                  key_index += 1
                  if key_index == len(lowerCAmelCase    ):
                        UpperCAmelCase__			:							List[str]							=    0
            else:
                  translated.append(lowerCAmelCase    )
      return "".join(lowerCAmelCase    )


if __name__ == "__main__":
    main()

