






'''simple docstring'''



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()
__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						int=False     )	->      List[str]:

   """simple docstring"""
   __a					  =     []
   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"
      __a					  =     [(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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						Any=False     )	->      Optional[int]:

   """simple docstring"""
   for i in range(config.num_hidden_layers     ):
      if base_model:
         __a					  =     ''
      else:
         __a					  =     'vit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      __a					  =     state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""     )
      __a					  =     state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      __a					  =     in_proj_weight[
          : config.hidden_size, :
      ]
      __a					  =     in_proj_bias[: config.hidden_size]
      __a					  =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __a					  =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __a					  =     in_proj_weight[
          -config.hidden_size :, :
      ]
      __a					  =     in_proj_bias[-config.hidden_size :]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]     )	->      Any:

   """simple docstring"""
   __a					  =     ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any]     )	->      Optional[int]:

   """simple docstring"""
   __a					  =     dct.pop(SCREAMING_SNAKE_CASE__     )
   __a					  =     val



def  __UpperCAmelCase						(					)	->      Optional[Any]:

   """simple docstring"""
   __a					  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __a					  =     Image.open(requests.get(SCREAMING_SNAKE_CASE__,       stream=SCREAMING_SNAKE_CASE__     ).raw     )
   return im



@torch.no_grad()
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Union[str, Any]     )	->      Tuple:

   """simple docstring"""
   __a					  =     ViTConfig()
   __a					  =     False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      __a					  =     True
      __a					  =     int(vit_name[-12:-10]     )
      __a					  =     int(vit_name[-9:-6]     )
   else:
      __a					  =     1000
      __a					  =     'huggingface/label-files'
      __a					  =     'imagenet-1k-id2label.json'
      __a					  =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       repo_type='dataset'     ),       'r'     )     )
      __a					  =     {int(SCREAMING_SNAKE_CASE__     ): v for k, v in idalabel.items()}
      __a					  =     idalabel
      __a					  =     {v: k for k, v in idalabel.items()}
      __a					  =     int(vit_name[-6:-4]     )
      __a					  =     int(vit_name[-3:]     )
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith('tiny'     ):
         __a					  =     192
         __a					  =     768
         __a					  =     12
         __a					  =     3
      elif vit_name[9:].startswith('small'     ):
         __a					  =     384
         __a					  =     1536
         __a					  =     12
         __a					  =     6
      else:
         pass
   else:
      if vit_name[4:].startswith('small'     ):
         __a					  =     768
         __a					  =     2304
         __a					  =     8
         __a					  =     8
      elif vit_name[4:].startswith('base'     ):
         pass
      elif vit_name[4:].startswith('large'     ):
         __a					  =     1024
         __a					  =     4096
         __a					  =     24
         __a					  =     16
      elif vit_name[4:].startswith('huge'     ):
         __a					  =     1280
         __a					  =     5120
         __a					  =     32
         __a					  =     16

    # load original model from timm
   __a					  =     timm.create_model(SCREAMING_SNAKE_CASE__,       pretrained=SCREAMING_SNAKE_CASE__     )
   timm_model.eval()

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

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

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

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

   Path(SCREAMING_SNAKE_CASE__     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )
   print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(SCREAMING_SNAKE_CASE__     )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   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."""
 )

 __UpperCamelCase   :							Union[str, Any]         =   parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

   return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE__     )     )]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

   __a					  =     all_rotations(SCREAMING_SNAKE_CASE__     )
   rotations.sort()  # sort the list of rotations in alphabetically order
   # make a string composed of the last char of each rotation
   __a					  =     {
       "bwt_string": "".join([word[-1] for word in rotations]     ),
       "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE__     ),
   }
   return response



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter bwt_string type must be str.'     )
   if not bwt_string:
      raise ValueError('The parameter bwt_string must not be empty.'     )
   try:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   except ValueError:
      raise TypeError(
          'The parameter idx_original_string type must be int or passive'
          ' of cast to int.'     )
   if idx_original_string < 0:
      raise ValueError('The parameter idx_original_string must not be lower than 0.'     )
   if idx_original_string >= len(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
 print(
     f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
     f"""we get original string '{original_string}'"""
 )