from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		logging.get_logger(__name__)
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""pixel_values"""]

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BICUBIC     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Union[int, float] = 1 / 2_55     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   super().__init__(**__SCREAMING_SNAKE_CASE       )
   __a       =  size if size is not None else {"shortest_edge": 2_24}
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )
   __a       =  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							param_name="crop_size"       )

   __a       =  do_resize
   __a       =  size
   __a       =  resample
   __a       =  do_center_crop
   __a       =  crop_size
   __a       =  do_rescale
   __a       =  rescale_factor
   __a       =  do_normalize
   __a       =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
   __a       =  image_std if image_std is not None else IMAGENET_DEFAULT_STD

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int]     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BICUBIC     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )
   # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
   if "shortest_edge" in size:
    __a       =  int((2_56 / 2_24) * size["shortest_edge"]       )
    __a       =  get_resize_output_image_size(__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )
    __a       =  {"height": output_size[0], "width": output_size[1]}
   if "height" not in size_dict or "width" not in size_dict:
    raise ValueError(
        f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""       )
   return resize(
       __SCREAMING_SNAKE_CASE     ,							size=(size_dict["height"], size_dict["width"])     ,							resample=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int]     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Any     ,							):
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   if "height" not in size or "width" not in size:
    raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""       )
   return center_crop(__SCREAMING_SNAKE_CASE     ,							size=(size["height"], size["width"])     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Union[int, float]     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   return rescale(__SCREAMING_SNAKE_CASE     ,							scale=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Union[float, List[float]]     ,							__SCREAMING_SNAKE_CASE :      Union[float, List[float]]     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Any     ,							):
   return normalize(__SCREAMING_SNAKE_CASE     ,							mean=__SCREAMING_SNAKE_CASE     ,							std=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      ImageInput     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Dict[str, int]] = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Dict[str, int]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[float] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, Iterable[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, Iterable[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[TensorType] = None     ,							__SCREAMING_SNAKE_CASE :      ChannelDimension = ChannelDimension.FIRST     ,							**__SCREAMING_SNAKE_CASE :      Tuple     ,							):
   __a       =  do_resize if do_resize is not None else self.do_resize
   __a       =  resample if resample is not None else self.resample
   __a       =  do_center_crop if do_center_crop is not None else self.do_center_crop
   __a       =  do_rescale if do_rescale is not None else self.do_rescale
   __a       =  rescale_factor if rescale_factor is not None else self.rescale_factor
   __a       =  do_normalize if do_normalize is not None else self.do_normalize
   __a       =  image_mean if image_mean is not None else self.image_mean
   __a       =  image_std if image_std is not None else self.image_std

   __a       =  size if size is not None else self.size
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )
   __a       =  crop_size if crop_size is not None else self.crop_size
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							param_name="crop_size"       )

   __a       =  make_list_of_images(__SCREAMING_SNAKE_CASE       )

   if not valid_images(__SCREAMING_SNAKE_CASE       ):
    raise ValueError(
        "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
        "torch.Tensor, tf.Tensor or jax.ndarray."       )

   if do_resize and size is None:
    raise ValueError("Size must be specified if do_resize is True."       )

   if do_center_crop and crop_size is None:
    raise ValueError("Crop size must be specified if do_center_crop is True."       )

   if do_rescale and rescale_factor is None:
    raise ValueError("Rescale factor must be specified if do_rescale is True."       )

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("Image mean and std must be specified if do_normalize is True."       )

   # All transformations expect numpy arrays.
   __a       =  [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_resize:
    __a       =  [self.resize(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_center_crop:
    __a       =  [self.center_crop(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_rescale:
    __a       =  [self.rescale(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_normalize:
    __a       =  [self.normalize(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  {"pixel_values": images}
   return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,							tensor_type=__SCREAMING_SNAKE_CASE       )



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()
SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 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					):



 """simple docstring"""


 for i in range(config.num_hidden_layers					):
  __a       =  "backbone." if is_semantic else ""
  # queries, keys and values
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


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



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __a       =  torch.hub.load_state_dict_from_url(_A							,				map_location="cpu"					)["model"]

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape of logits not as expected"

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

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "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(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


