





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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase     :		Tuple								=       logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=False						)       ->	Optional[Any]:



       """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"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
              rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
              rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
              rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
              rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")						)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('''cls_token''', '''deit.embeddings.cls_token'''),
               ('''dist_token''', '''deit.embeddings.distillation_token'''),
               ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
               ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
               ('''pos_embed''', '''deit.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 "deit" from all keys that start with "deit"
              A__							=   [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''						) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''deit.layernorm.weight'''),
                      ('''norm.bias''', '''deit.layernorm.bias'''),
                      ('''head.weight''', '''cls_classifier.weight'''),
                      ('''head.bias''', '''cls_classifier.bias'''),
                      ('''head_dist.weight''', '''distillation_classifier.weight'''),
                      ('''head_dist.bias''', '''distillation_classifier.bias'''),
                  ]						)

       return rename_keys







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=False						)       ->	int:



       """simple docstring"""

       for i in range(config.num_hidden_layers						):
              if base_model:
                     A__							=   ''''''
              else:
                     A__							=   '''deit.'''
              # 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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   dct.pop(lowercase_						)
       A__							=   val







def SCREAMING_SNAKE_CASE    (	)       ->	int:



       """simple docstring"""

       A__							=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__							=   Image.open(requests.get(lowercase_			,    stream=lowercase_						).raw						)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Optional[int]:



       """simple docstring"""

       A__							=   DeiTConfig()
       # all deit models have fine-tuned heads
       A__							=   False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       A__							=   1_000
       A__							=   '''huggingface/label-files'''
       A__							=   '''imagenet-1k-id2label.json'''
       A__							=   json.load(open(hf_hub_download(lowercase_			,    lowercase_			,    repo_type='''dataset'''						)			,    '''r'''						)						)
       A__							=   {int(lowercase_						): v for k, v in idalabel.items()}
       A__							=   idalabel
       A__							=   {v: k for k, v in idalabel.items()}
       A__							=   int(deit_name[-6:-4]						)
       A__							=   int(deit_name[-3:]						)
       # size of the architecture
       if deit_name[9:].startswith('''tiny'''						):
              A__							=   192
              A__							=   768
              A__							=   12
              A__							=   3
       elif deit_name[9:].startswith('''small'''						):
              A__							=   384
              A__							=   1_536
              A__							=   12
              A__							=   6
       if deit_name[9:].startswith('''base'''						):
              pass
       elif deit_name[4:].startswith('''large'''						):
              A__							=   1_024
              A__							=   4_096
              A__							=   24
              A__							=   16

       # load original model from timm
       A__							=   timm.create_model(lowercase_			,    pretrained=lowercase_						)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       A__							=   timm_model.state_dict()
       A__							=   create_rename_keys(lowercase_			,    lowercase_						)
       for src, dest in rename_keys:
              rename_key(lowercase_			,    lowercase_			,    lowercase_						)
       read_in_q_k_v(lowercase_			,    lowercase_			,    lowercase_						)

       # load HuggingFace model
       A__							=   DeiTForImageClassificationWithTeacher(lowercase_						).eval()
       model.load_state_dict(lowercase_						)

       # Check outputs on an image, prepared by DeiTImageProcessor
       A__							=   int(
           (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       A__							=   DeiTImageProcessor(size=lowercase_			,    crop_size=config.image_size						)
       A__							=   image_processor(images=prepare_img()			,    return_tensors='''pt'''						)
       A__							=   encoding['''pixel_values''']
       A__							=   model(lowercase_						)

       A__							=   timm_model(lowercase_						)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(lowercase_			,    outputs.logits			,    atol=1E-3						)

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


if __name__ == "__main__":
  _lowerCamelCase     :		Dict								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--deit_name""",
      default="""vit_deit_base_distilled_patch16_224""",
      type=str,
      help="""Name of the DeiT 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."""
  )

  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








from __future__ import annotations

from typing import Any


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     float = 0)						->None:





           '''simple docstring'''

           A__			,							A__							=   row, column
           A__							=   [[default_value for c in range(UpperCAmelCase__)] for r in range(UpperCAmelCase__)]


    def __str__(      self				:     List[str])						->str:





           '''simple docstring'''

           A__							=   f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

           # Make string identifier
           A__							=   0
           for row_vector in self.array:
                  for obj in row_vector:
                         A__							=   max(UpperCAmelCase__ ,			len(str(UpperCAmelCase__)))
           A__							=   f"""%{max_element_length}s"""

           # Make string and return
           def single_line(UpperCAmelCase__				:     list[float]) -> str:
                  nonlocal string_format_identifier
                  A__							=   '''['''
                  line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
                  line += "]"
                  return line

           s += "\n".join(single_line(UpperCAmelCase__) for row_vector in self.array)
           return s


    def __repr__(      self				:     Tuple)						->str:





           '''simple docstring'''

           return str(self)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     tuple[int, int])						->bool:





           '''simple docstring'''

           if not (isinstance(UpperCAmelCase__ ,			(list, tuple)) and len(UpperCAmelCase__) == 2):
                  return False
           elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
                  return False
           else:
                  return True


    def __getitem__(      self				:     List[Any] ,			UpperCAmelCase__				:     tuple[int, int])						->Any:





           '''simple docstring'''

           assert self.validate_indicies(UpperCAmelCase__)
           return self.array[loc[0]][loc[1]]


    def __setitem__(      self				:     List[Any] ,			UpperCAmelCase__				:     tuple[int, int] ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           assert self.validate_indicies(UpperCAmelCase__)
           A__							=   value


    def __add__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Matrix)						->Matrix:





           '''simple docstring'''

           assert isinstance(UpperCAmelCase__ ,			UpperCAmelCase__)
           assert self.row == another.row and self.column == another.column

           # Add
           A__							=   Matrix(self.row ,			self.column)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   self[r, c] + another[r, c]
           return result


    def __neg__(      self				:     str)						->Matrix:





           '''simple docstring'''

           A__							=   Matrix(self.row ,			self.column)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   -self[r, c]
           return result


    def __sub__(      self				:     str ,			UpperCAmelCase__				:     Matrix)						->Matrix:





           '''simple docstring'''

           return self + (-another)


    def __mul__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     int | float | Matrix)						->Matrix:





           '''simple docstring'''

           if isinstance(UpperCAmelCase__ ,			(int, float)):  # Scalar multiplication
                  A__							=   Matrix(self.row ,			self.column)
                  for r in range(self.row):
                         for c in range(self.column):
                                A__							=   self[r, c] * another
                  return result
           elif isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):  # Matrix multiplication
                  assert self.column == another.row
                  A__							=   Matrix(self.row ,			another.column)
                  for r in range(self.row):
                         for c in range(another.column):
                                for i in range(self.column):
                                       result[r, c] += self[r, i] * another[i, c]
                  return result
           else:
                  A__							=   f"""Unsupported type given for another ({type(UpperCAmelCase__)})"""
                  raise TypeError(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Matrix:





           '''simple docstring'''

           A__							=   Matrix(self.column ,			self.row)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   self[r, c]
           return result





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Matrix ,			UpperCAmelCase__				:     Matrix)						->Any:





           '''simple docstring'''

           assert isinstance(UpperCAmelCase__ ,			UpperCAmelCase__) and isinstance(UpperCAmelCase__ ,			UpperCAmelCase__)
           assert self.row == self.column == u.row == v.row  # u, v should be column vector
           assert u.column == v.column == 1  # u, v should be column vector

           # Calculate
           A__							=   v.transpose()
           A__							=   (v_t * self * u)[0, 0] + 1
           if numerator_factor == 0:
                  return None  # It's not invertable
           return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE    (	)       ->	None:



         """simple docstring"""

         A__							=   Matrix(3			,    3			,    0						)
         for i in range(3						):
                A__							=   1
         print(f"""a^(-1) is {ainv}"""						)
         # u, v
         A__							=   Matrix(3			,    1			,    0						)
         A__			,							A__			,							A__							=   1, 2, -3
         A__							=   Matrix(3			,    1			,    0						)
         A__			,							A__			,							A__							=   4, -2, 5
         print(f"""u is {u}"""						)
         print(f"""v is {v}"""						)
         print(f"""uv^T is {u * v.transpose()}"""						)
         # Sherman Morrison
         print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(lowercase_			,    lowercase_						)}"""						)







  def SCREAMING_SNAKE_CASE    (	)       ->	None:



         """simple docstring"""

         import doctest

         doctest.testmod()
  testa()


