


"""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()
snake_case__						:   Any			 = logging.get_logger(__name__)


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


def    _snake_case  (  _snake_case :	Tuple	):
      lowerCAmelCase    :		List[Any]				=      ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Any       ,      _snake_case :	List[Any]	):
      lowerCAmelCase    :		Optional[int]				=      dct.pop(_snake_case	)
      lowerCAmelCase    :		Union[str, Any]				=      val


def    _snake_case  (  ):
      lowerCAmelCase    :		Any				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		Any				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)
      return im


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

    # load original model from timm
      lowerCAmelCase    :		Any				=      timm.create_model(_snake_case       ,      pretrained=_snake_case	)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCAmelCase    :		int				=      timm_model.state_dict()
      if base_model:
            remove_classification_head_(_snake_case	)
      lowerCAmelCase    :		Optional[Any]				=      create_rename_keys(_snake_case       ,      _snake_case	)
      for src, dest in rename_keys:
            rename_key(_snake_case       ,      _snake_case       ,      _snake_case	)
      read_in_q_k_v(_snake_case       ,      _snake_case       ,      _snake_case	)

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

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

      if base_model:
            lowerCAmelCase    :		Dict				=      timm_model.forward_features(_snake_case	)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(_snake_case       ,      outputs.pooler_output       ,      atol=1E-3	)
      else:
            lowerCAmelCase    :		Dict				=      timm_model(_snake_case	)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(_snake_case       ,      outputs.logits       ,      atol=1E-3	)

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


if __name__ == "__main__":
 snake_case__						:   Union[str, 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.'''
 )

 snake_case__						:   int			 = parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






"""simple docstring"""





import mpmath  # for roots of unity
import numpy as np



class 						snake_case_:






     def __init__(		self     :    str    , UpperCamelCase_     :    int=None    , UpperCamelCase_     :    List[str]=None ):
           # Input as list
           lowerCAmelCase    :		str				=      list(poly_a or [0] )[:]
           lowerCAmelCase    :		Any				=      list(poly_b or [0] )[:]

           # Remove leading zero coefficients
           while self.polyA[-1] == 0:
                 self.polyA.pop()
           lowerCAmelCase    :		Optional[int]				=      len(self.polyA )

           while self.polyB[-1] == 0:
                 self.polyB.pop()
           lowerCAmelCase    :		Union[str, Any]				=      len(self.polyB )

           # Add 0 to make lengths equal a power of 2
           lowerCAmelCase    :		str				=      int(
               2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )

           while len(self.polyA ) < self.c_max_length:
                 self.polyA.append(0 )
           while len(self.polyB ) < self.c_max_length:
                 self.polyB.append(0 )
           # A complex root used for the fourier transform
           lowerCAmelCase    :		int				=      complex(mpmath.root(x=1    , n=self.c_max_length    , k=1 ) )

           # The product
           lowerCAmelCase    :		int				=      self.__multiply()






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    str ):
           lowerCAmelCase    :		Optional[Any]				=      [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
           # Corner case
           if len(UpperCamelCase_ ) <= 1:
                 return dft[0]
           #
           lowerCAmelCase    :		Tuple				=      self.c_max_length // 2
           while next_ncol > 0:
                 lowerCAmelCase    :		Dict				=      [[] for i in range(UpperCamelCase_ )]
                 lowerCAmelCase    :		List[Any]				=      self.root**next_ncol

                 # First half of next step
                 lowerCAmelCase    :		Dict				=      1
                 for j in range(self.c_max_length // (next_ncol * 2) ):
                       for i in range(UpperCamelCase_ ):
                             new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
                       current_root *= root
                 # Second half of next step
                 lowerCAmelCase    :		int				=      1
                 for j in range(self.c_max_length // (next_ncol * 2) ):
                       for i in range(UpperCamelCase_ ):
                             new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
                       current_root *= root
                 # Update
                 lowerCAmelCase    :		Optional[Any]				=      new_dft
                 lowerCAmelCase    :		Union[str, Any]				=      next_ncol // 2
           return dft[0]






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Optional[Any]				=      self.__dft('''A''' )
           lowerCAmelCase    :		Optional[int]				=      self.__dft('''B''' )
           lowerCAmelCase    :		Any				=      [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
           del dft_a
           del dft_b

           # Corner Case
           if len(inverce_c[0] ) <= 1:
                 return inverce_c[0]
           # Inverse DFT
           lowerCAmelCase    :		str				=      2
           while next_ncol <= self.c_max_length:
                 lowerCAmelCase    :		Union[str, Any]				=      [[] for i in range(UpperCamelCase_ )]
                 lowerCAmelCase    :		Optional[Any]				=      self.root ** (next_ncol // 2)
                 lowerCAmelCase    :		Tuple				=      1
                 # First half of next step
                 for j in range(self.c_max_length // next_ncol ):
                       for i in range(next_ncol // 2 ):
                             # Even positions
                             new_inverse_c[i].append(
                                 (
                                     inverce_c[i][j]
                                     + inverce_c[i][j + self.c_max_length // next_ncol]
                                 )
                                 / 2 )
                             # Odd positions
                             new_inverse_c[i + next_ncol // 2].append(
                                 (
                                     inverce_c[i][j]
                                     - inverce_c[i][j + self.c_max_length // next_ncol]
                                 )
                                 / (2 * current_root) )
                       current_root *= root
                 # Update
                 lowerCAmelCase    :		Any				=      new_inverse_c
                 next_ncol *= 2
           # Unpack
           lowerCAmelCase    :		Optional[int]				=      [round(x[0].real    , 8 ) + round(x[0].imag    , 8 ) * 1j for x in inverce_c]

           # Remove leading 0's
           while inverce_c[-1] == 0:
                 inverce_c.pop()
           return inverce_c






     def __str__(		self     :    int ):
           lowerCAmelCase    :		int				=      '''A = ''' + ''' + '''.join(
               F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
           lowerCAmelCase    :		str				=      '''B = ''' + ''' + '''.join(
               F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
           lowerCAmelCase    :		int				=      '''A*B = ''' + ''' + '''.join(
               F'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )

           return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()


