






"""simple docstring"""






import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)



def       _A    (__a					,       __a=False					,       __a=False							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	'''backbone.''' if is_semantic else ''''''

    SCREAMING_SNAKE_CASE_   :						List[Any]							=	[]
    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'''),
            (f'{prefix}pos_embed', '''beit.embeddings.position_embeddings'''),
        ]							)

    if has_lm_head:
        # mask token + layernorm
        rename_keys.extend(
            [
                ('''mask_token''', '''beit.embeddings.mask_token'''),
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.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    (__a					,       __a					,       __a=False					,       __a=False							)				-> List[Any]:
    """simple docstring"""






    for i in range(config.num_hidden_layers							):
        SCREAMING_SNAKE_CASE_   :						Any							=	'''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'							)
        SCREAMING_SNAKE_CASE_   :						List[str]							=	state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'							)
        SCREAMING_SNAKE_CASE_   :						int							=	state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'							)

        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_weight[
            : config.hidden_size, :
        ]
        SCREAMING_SNAKE_CASE_   :						str							=	q_bias
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE_   :						str							=	in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE_   :						Dict							=	v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	state_dict.pop(f'{prefix}blocks.{i}.gamma_1'							)
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	state_dict.pop(f'{prefix}blocks.{i}.gamma_2'							)

        SCREAMING_SNAKE_CASE_   :						Any							=	gamma_a
        SCREAMING_SNAKE_CASE_   :						List[str]							=	gamma_a



def       _A    (__a					,       __a					,       __a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	dct.pop(_UpperCamelCase							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	val



def       _A    ()				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Any							=	Image.open(requests.get(_UpperCamelCase					,       stream=_UpperCamelCase							).raw							)
    return im



@torch.no_grad()
def       _A    (__a					,       __a					,       __a=False							)				-> Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	False if '''rvlcdip''' in checkpoint_url else True
    SCREAMING_SNAKE_CASE_   :						Tuple							=	BeitConfig(use_absolute_position_embeddings=_UpperCamelCase					,       use_mask_token=_UpperCamelCase							)

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	10_24
        SCREAMING_SNAKE_CASE_   :						int							=	40_96
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	24
        SCREAMING_SNAKE_CASE_   :						Any							=	16

    # labels
    if "rvlcdip" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	16
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	'''rvlcdip-id2label.json'''
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	json.load(open(hf_hub_download(_UpperCamelCase					,       _UpperCamelCase					,       repo_type='''dataset'''							)					,       '''r'''							)							)
        SCREAMING_SNAKE_CASE_   :						int							=	{int(_UpperCamelCase							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Dict							=	idalabel
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    SCREAMING_SNAKE_CASE_   :						int							=	torch.hub.load_state_dict_from_url(_UpperCamelCase					,       map_location='''cpu'''							)['''model''']

    SCREAMING_SNAKE_CASE_   :						int							=	create_rename_keys(_UpperCamelCase					,       has_lm_head=_UpperCamelCase							)
    for src, dest in rename_keys:
        rename_key(_UpperCamelCase					,       _UpperCamelCase					,       _UpperCamelCase							)
    read_in_q_k_v(_UpperCamelCase					,       _UpperCamelCase					,       has_lm_head=_UpperCamelCase							)

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_   :						str							=	BeitForMaskedImageModeling(_UpperCamelCase							) if has_lm_head else BeitForImageClassification(_UpperCamelCase							)
    model.eval()
    model.load_state_dict(_UpperCamelCase							)

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	BeitImageProcessor(
        size=config.image_size					,       resample=PILImageResampling.BILINEAR					,       do_center_crop=_UpperCamelCase							)
    SCREAMING_SNAKE_CASE_   :						Any							=	prepare_img()

    SCREAMING_SNAKE_CASE_   :						str							=	image_processor(images=_UpperCamelCase					,       return_tensors='''pt'''							)
    SCREAMING_SNAKE_CASE_   :						Any							=	encoding['''pixel_values''']

    SCREAMING_SNAKE_CASE_   :						Any							=	model(_UpperCamelCase							)
    SCREAMING_SNAKE_CASE_   :						str							=	outputs.logits

    # verify logits
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
    assert logits.shape == torch.Size(_UpperCamelCase							), "Shape of logits not as expected"

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

    if push_to_hub:
        if has_lm_head:
            SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            SCREAMING_SNAKE_CASE_   :						str							=	'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
        image_processor.push_to_hub(
            repo_path_or_name=Path(_UpperCamelCase					,       _UpperCamelCase							)					,       organization='''nielsr'''					,       commit_message='''Add image processor'''					,       use_temp_dir=_UpperCamelCase					,       )
        model.push_to_hub(
            repo_path_or_name=Path(_UpperCamelCase					,       _UpperCamelCase							)					,       organization='''nielsr'''					,       commit_message='''Add model'''					,       use_temp_dir=_UpperCamelCase					,       )


if __name__ == "__main__":
  UpperCAmelCase_     :				Dict         =				argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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."""
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  UpperCAmelCase_     :				int         =				parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray



def       _A    (__a					,       __a					,       __a = False							)				-> list[float]:
    """simple docstring"""






    if radian_mode:
        return [magnitude * cos(__a							), magnitude * sin(__a							)]
    return [magnitude * cos(radians(__a							)							), magnitude * sin(radians(__a							)							)]



def       _A    (__a					,       __a					,       __a = 10**-1							)				-> bool:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						NDArray[floataa]							=	cross(__a					,       __a							)
    SCREAMING_SNAKE_CASE_   :						float							=	sum(__a							)
    return abs(__a							) < eps


if __name__ == "__main__":
  # Test to check if it works
  UpperCAmelCase_     :				Union[str, Any]         =				array(
      [
          polar_force(7_1_8.4, 180 - 30),
          polar_force(8_7_9.5_4, 45),
          polar_force(100, -90),
      ]
  )

  UpperCAmelCase_     :				NDArray[floataa]         =				array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  UpperCAmelCase_     :				Union[str, Any]         =				array(
      [
          polar_force(30 * 9.8_1, 15),
          polar_force(215, 180 - 45),
          polar_force(264, 90 - 30),
      ]
  )

  UpperCAmelCase_     :				Optional[int]         =				array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  UpperCAmelCase_     :				List[str]         =				array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

  UpperCAmelCase_     :				Any         =				array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()


