

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()
lowercase_			=  logging.get_logger(__name__)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]=False				)							->				Union[str, 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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     List[str]=False				)							->				Tuple:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Dict				)							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       dct.pop(SCREAMING_SNAKE_CASE__				)
    A__   				=       val
def 						_snake_case(   )							->				List[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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict				)							->				Tuple:





    '''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__   				=       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(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__   				=       1536
        A__   				=       12
        A__   				=       6
    if deit_name[9:].startswith('base'				):
        pass
    elif deit_name[4:].startswith('large'				):
        A__   				=       1024
        A__   				=       4096
        A__   				=       24
        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()
    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
    A__   				=       DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__				).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE__				)

    # 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=SCREAMING_SNAKE_CASE__   ,      crop_size=config.image_size				)
    A__   				=       image_processor(images=prepare_img()   ,      return_tensors='pt'				)
    A__   				=       encoding['pixel_values']
    A__   				=       model(SCREAMING_SNAKE_CASE__				)

    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 {deit_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__":
     lowercase_			=  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."
     )

     lowercase_			=  parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square_using_dp_array(
        SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       current_row

    return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

