'''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()
__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Tuple=False			,	_UpperCAmelCase  :      Union[str, Any]=False    )    ->    Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    "backbone." if is_semantic else ""

       _UpperCAmelCase     :  Tuple							      =    []
       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 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      List[str]=False			,	_UpperCAmelCase  :      List[Any]=False    )    ->    int:


       """simple docstring"""







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

              _UpperCAmelCase     :  Dict							      =    in_proj_weight[
                  : config.hidden_size, :
              ]
              _UpperCAmelCase     :  Optional[int]							      =    q_bias
              _UpperCAmelCase     :  Any							      =    in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase     :  Any							      =    in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase     :  Dict							      =    v_bias

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

              _UpperCAmelCase     :  List[Any]							      =    gamma_a
              _UpperCAmelCase     :  Any							      =    gamma_a

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      List[str]    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    dct.pop(_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[int]							      =    val

def 	UpperCamelCase_							(					)    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    "http://images.cocodataset.org/val2017/000000039769.jpg"
       _UpperCAmelCase     :  List[str]							      =    Image.open(requests.get(_UpperCAmelCase			,	stream=_UpperCAmelCase    ).raw    )
       return im

@torch.no_grad()
def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      List[Any]=False    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    False if "rvlcdip" in checkpoint_url else True
       _UpperCAmelCase     :  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:
              _UpperCAmelCase     :  Optional[int]							      =    1_024
              _UpperCAmelCase     :  Union[str, Any]							      =    4_096
              _UpperCAmelCase     :  Tuple							      =    24
              _UpperCAmelCase     :  int							      =    16

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

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

       _UpperCAmelCase     :  Tuple							      =    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
       _UpperCAmelCase     :  Union[str, Any]							      =    BeitForMaskedImageModeling(_UpperCAmelCase    ) if has_lm_head else BeitForImageClassification(_UpperCAmelCase    )
       model.eval()
       model.load_state_dict(_UpperCAmelCase    )

       # Check outputs on an image
       _UpperCAmelCase     :  int							      =    BeitImageProcessor(
           size=config.image_size			,	resample=PILImageResampling.BILINEAR			,	do_center_crop=_UpperCAmelCase    )
       _UpperCAmelCase     :  Union[str, Any]							      =    prepare_img()

       _UpperCAmelCase     :  Any							      =    image_processor(images=_UpperCAmelCase			,	return_tensors="pt"    )
       _UpperCAmelCase     :  Optional[int]							      =    encoding["pixel_values"]

       _UpperCAmelCase     :  List[str]							      =    model(_UpperCAmelCase    )
       _UpperCAmelCase     :  str							      =    outputs.logits

       # verify logits
       _UpperCAmelCase     :  Any							      =    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
       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:
                     _UpperCAmelCase     :  Optional[int]							      =    "dit-base" if "base" in checkpoint_url else "dit-large"
              else:
                     _UpperCAmelCase     :  List[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__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  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       :     List[str]     =  parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


'''simple docstring'''




__SCREAMING_SNAKE_CASE       :     Dict     =  8.3_1_4_4_6_2  # Unit - J mol-1 K-1

def 	UpperCamelCase_							(					_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float    )    ->    float:


       """simple docstring"""







       if moles < 0 or kelvin < 0 or volume < 0:
              raise ValueError("Invalid inputs. Enter positive value."    )
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def 	UpperCamelCase_							(					_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float    )    ->    float:


       """simple docstring"""







       if moles < 0 or kelvin < 0 or pressure < 0:
              raise ValueError("Invalid inputs. Enter positive value."    )
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
  from doctest import testmod

  testmod()

