
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 				a__			(					lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Optional[int]=False							,     lowerCAmelCase						:							Tuple=False		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    "backbone." if is_semantic else ""

     UpperCAmelCase__				:			Optional[int]							=    []
     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"beit.encoder.layer.{i}.layernorm_before.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")		)
          rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")		)
          rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")		)
          rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")		)

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

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ("mask_token", "beit.embeddings.mask_token"),
                  (
                      "rel_pos_bias.relative_position_bias_table",
                      "beit.encoder.relative_position_bias.relative_position_bias_table",
                  ),
                  (
                      "rel_pos_bias.relative_position_index",
                      "beit.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", "beit.pooler.layernorm.weight"),
                  ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]		)

     return rename_keys



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							int							,     lowerCAmelCase						:							Any=False							,     lowerCAmelCase						:							Optional[Any]=False		):






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

          UpperCAmelCase__				:			List[str]							=    in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__				:			int							=    q_bias
          UpperCAmelCase__				:			List[Any]							=    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__				:			Optional[int]							=    in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__				:			Optional[int]							=    v_bias

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

          UpperCAmelCase__				:			str							=    gamma_a
          UpperCAmelCase__				:			List[str]							=    gamma_a

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

               UpperCAmelCase__				:			str							=    table
               UpperCAmelCase__				:			int							=    index



def 				a__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							Dict		):






     '''simple docstring'''
     UpperCAmelCase__				:			Tuple							=    dct.pop(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    val



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__				:			Optional[Any]							=    Image.open(requests.get(lowerCAmelCase							,     stream=lowerCAmelCase		).raw		)
     return im



@torch.no_grad()
def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    BeitConfig()
     UpperCAmelCase__				:			Optional[int]							=    False
     UpperCAmelCase__				:			Tuple							=    False
     UpperCAmelCase__				:			Dict							=    "huggingface/label-files"
     # set config parameters based on URL
     if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          UpperCAmelCase__				:			Tuple							=    True
          UpperCAmelCase__				:			Dict							=    True
          UpperCAmelCase__				:			List[str]							=    True
     elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          UpperCAmelCase__				:			Optional[int]							=    True
          UpperCAmelCase__				:			int							=    2_1841
          UpperCAmelCase__				:			Optional[Any]							=    "imagenet-22k-id2label.json"
          UpperCAmelCase__				:			Union[str, Any]							=    json.load(open(hf_hub_download(lowerCAmelCase							,     lowerCAmelCase							,     repo_type="dataset"		)							,     "r"		)		)
          UpperCAmelCase__				:			List[str]							=    {int(lowerCAmelCase		): v for k, v in idalabel.items()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[1_5027]
          UpperCAmelCase__				:			Optional[Any]							=    idalabel
          UpperCAmelCase__				:			List[Any]							=    {v: k for k, v in idalabel.items()}
     elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          UpperCAmelCase__				:			int							=    True
          UpperCAmelCase__				:			Union[str, Any]							=    1000
          UpperCAmelCase__				:			Dict							=    "imagenet-1k-id2label.json"
          UpperCAmelCase__				:			Tuple							=    json.load(open(hf_hub_download(lowerCAmelCase							,     lowerCAmelCase							,     repo_type="dataset"		)							,     "r"		)		)
          UpperCAmelCase__				:			int							=    {int(lowerCAmelCase		): v for k, v in idalabel.items()}
          UpperCAmelCase__				:			Union[str, Any]							=    idalabel
          UpperCAmelCase__				:			str							=    {v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
               UpperCAmelCase__				:			Dict							=    384
          if "512" in checkpoint_url:
               UpperCAmelCase__				:			Optional[int]							=    512
     elif "ade20k" in checkpoint_url:
          # fine-tuning
          UpperCAmelCase__				:			Dict							=    True
          UpperCAmelCase__				:			List[str]							=    150
          UpperCAmelCase__				:			Dict							=    "ade20k-id2label.json"
          UpperCAmelCase__				:			str							=    json.load(open(hf_hub_download(lowerCAmelCase							,     lowerCAmelCase							,     repo_type="dataset"		)							,     "r"		)		)
          UpperCAmelCase__				:			int							=    {int(lowerCAmelCase		): v for k, v in idalabel.items()}
          UpperCAmelCase__				:			Optional[Any]							=    idalabel
          UpperCAmelCase__				:			List[str]							=    {v: k for k, v in idalabel.items()}
          UpperCAmelCase__				:			Any							=    640
          UpperCAmelCase__				:			Optional[int]							=    True
     else:
          raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"		)

     # size of the architecture
     if "base" in checkpoint_url:
          pass
     elif "large" in checkpoint_url:
          UpperCAmelCase__				:			List[str]							=    1024
          UpperCAmelCase__				:			Optional[Any]							=    4096
          UpperCAmelCase__				:			List[str]							=    24
          UpperCAmelCase__				:			str							=    16
          if "ade20k" in checkpoint_url:
               UpperCAmelCase__				:			Dict							=    640
               UpperCAmelCase__				:			int							=    [7, 11, 15, 23]
     else:
          raise ValueError("Should either find 'base' or 'large' in checkpoint URL"		)

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__				:			Optional[int]							=    torch.hub.load_state_dict_from_url(lowerCAmelCase							,     map_location="cpu"							,     check_hash=lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

     UpperCAmelCase__				:			Optional[int]							=    create_rename_keys(lowerCAmelCase							,     has_lm_head=lowerCAmelCase							,     is_semantic=lowerCAmelCase		)
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)
     read_in_q_k_v(lowerCAmelCase							,     lowerCAmelCase							,     has_lm_head=lowerCAmelCase							,     is_semantic=lowerCAmelCase		)
     if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
               UpperCAmelCase__				:			Any							=    state_dict.pop(lowerCAmelCase		)
               if key.startswith("backbone.fpn"		):
                    UpperCAmelCase__				:			Optional[int]							=    key.replace("backbone.fpn"							,     "fpn"		)
               UpperCAmelCase__				:			int							=    val

    # load HuggingFace model
     if checkpoint_url[-9:-4] == "pt22k":
          UpperCAmelCase__				:			Tuple							=    BeitForMaskedImageModeling(lowerCAmelCase		)
     elif "ade20k" in checkpoint_url:
          UpperCAmelCase__				:			Optional[int]							=    BeitForSemanticSegmentation(lowerCAmelCase		)
     else:
          UpperCAmelCase__				:			Dict							=    BeitForImageClassification(lowerCAmelCase		)
     model.eval()
     model.load_state_dict(lowerCAmelCase		)

     # Check outputs on an image
     if is_semantic:
          UpperCAmelCase__				:			int							=    BeitImageProcessor(size=config.image_size							,     do_center_crop=lowerCAmelCase		)
          UpperCAmelCase__				:			int							=    load_dataset("hf-internal-testing/fixtures_ade20k"							,     split="test"		)
          UpperCAmelCase__				:			str							=    Image.open(ds[0]["file"]		)
     else:
          UpperCAmelCase__				:			Optional[Any]							=    BeitImageProcessor(
              size=config.image_size							,     resample=PILImageResampling.BILINEAR							,     do_center_crop=lowerCAmelCase		)
          UpperCAmelCase__				:			Tuple							=    prepare_img()

     UpperCAmelCase__				:			Optional[Any]							=    image_processor(images=lowerCAmelCase							,     return_tensors="pt"		)
     UpperCAmelCase__				:			Union[str, Any]							=    encoding["pixel_values"]

     UpperCAmelCase__				:			int							=    model(lowerCAmelCase		)
     UpperCAmelCase__				:			Tuple							=    outputs.logits

     # verify logits
     UpperCAmelCase__				:			str							=    torch.Size([1, 1000]		)
     if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"		):
          UpperCAmelCase__				:			Tuple							=    torch.Size([1, 196, 8192]		)
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"		):
          UpperCAmelCase__				:			Union[str, Any]							=    torch.Size([1, 196, 8192]		)
     elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"		):
          UpperCAmelCase__				:			Dict							=    torch.Size([1, 2_1841]		)
          UpperCAmelCase__				:			Dict							=    torch.tensor([2.2288, 2.4671, 0.7395]		)
          UpperCAmelCase__				:			str							=    2397
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"		):
          UpperCAmelCase__				:			str							=    torch.Size([1, 2_1841]		)
          UpperCAmelCase__				:			Optional[int]							=    torch.tensor([1.6881, -0.2787, 0.5901]		)
          UpperCAmelCase__				:			int							=    2396
     elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"		):
          UpperCAmelCase__				:			Dict							=    torch.tensor([0.1241, 0.0798, -0.6569]		)
          UpperCAmelCase__				:			List[Any]							=    285
     elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"		):
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor([-1.2385, -1.0987, -1.0108]		)
          UpperCAmelCase__				:			Tuple							=    281
     elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"		):
          UpperCAmelCase__				:			Dict							=    torch.tensor([-1.5303, -0.9484, -0.3147]		)
          UpperCAmelCase__				:			List[str]							=    761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"		):
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor([0.4610, -0.0928, 0.2086]		)
          UpperCAmelCase__				:			Any							=    761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"		):
          UpperCAmelCase__				:			Optional[int]							=    torch.tensor([-0.4804, 0.6257, -0.1837]		)
          UpperCAmelCase__				:			Optional[Any]							=    761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"		):
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
          UpperCAmelCase__				:			Union[str, Any]							=    761
     elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"		):
          UpperCAmelCase__				:			Tuple							=    torch.tensor([-0.3062, 0.7261, 0.4852]		)
          UpperCAmelCase__				:			Union[str, Any]							=    761
     elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"		):
          UpperCAmelCase__				:			List[str]							=    (1, 150, 160, 160)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              [
                  [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                  [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                  [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
              ]		)
     elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"		):
          UpperCAmelCase__				:			List[str]							=    (1, 150, 160, 160)
          UpperCAmelCase__				:			int							=    torch.tensor(
              [
                  [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                  [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                  [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
              ]		)
     else:
          raise ValueError("Can't verify logits as model is not supported"		)

     assert logits.shape == expected_shape, "Shape of logits not as expected"
     if not has_lm_head:
          if is_semantic:
               assert torch.allclose(
                   logits[0, :3, :3, :3]							,     lowerCAmelCase							,     atol=1E-3		), "First elements of logits not as expected"
          else:
               print("Predicted class idx:"							,     logits.argmax(-1		).item()		)
               assert torch.allclose(
                   logits[0, :3]							,     lowerCAmelCase							,     atol=1E-3		), "First elements of logits not as expected"
               assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

     Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
     print(F"Saving model 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__   :						Dict 							=     argparse.ArgumentParser()

    parser.add_argument(
        """--checkpoint_url""",
        default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
        type=str,
        help="""URL to the original PyTorch checkpoint (.pth file).""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    A__   :						Optional[int] 							=     parser.parse_args()
    convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							int = 13		):






     '''simple docstring'''
     UpperCAmelCase__				:			Any							=    ""
     for c in s:
          if "A" <= c <= "Z":
               out += chr(ord("A"		) + (ord(lowerCAmelCase		) - ord("A"		) + n) % 26		)
          elif "a" <= c <= "z":
               out += chr(ord("a"		) + (ord(lowerCAmelCase		) - ord("a"		) + n) % 26		)
          else:
               out += c
     return out



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    input("Enter message: "		)

     UpperCAmelCase__				:			Optional[Any]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Encryption:"							,     lowerCAmelCase		)

     UpperCAmelCase__				:			List[str]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Decryption: "							,     lowerCAmelCase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



