




'''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()
lowerCamelCase_			=						logging.get_logger(__name__)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Tuple=False	,					__A    :			int=False  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				"backbone." if is_semantic else ""

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

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

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

      _SCREAMING_SNAKE_CASE	=				gamma_a
      _SCREAMING_SNAKE_CASE	=				gamma_a
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			List[str]	,					__A    :			Tuple  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				dct.pop(__A  )
   _SCREAMING_SNAKE_CASE	=				val
def 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				"http://images.cocodataset.org/val2017/000000039769.jpg"
   _SCREAMING_SNAKE_CASE	=				Image.open(requests.get(__A	,					stream=__A  ).raw  )
   return im
@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			Dict	,					__A    :			List[Any]=False  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				False if "rvlcdip" in checkpoint_url else True
   _SCREAMING_SNAKE_CASE	=				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:
      _SCREAMING_SNAKE_CASE	=				10_24
      _SCREAMING_SNAKE_CASE	=				40_96
      _SCREAMING_SNAKE_CASE	=				24
      _SCREAMING_SNAKE_CASE	=				16

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

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

   _SCREAMING_SNAKE_CASE	=				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
   _SCREAMING_SNAKE_CASE	=				BeitForMaskedImageModeling(__A  ) if has_lm_head else BeitForImageClassification(__A  )
   model.eval()
   model.load_state_dict(__A  )

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

   _SCREAMING_SNAKE_CASE	=				image_processor(images=__A	,					return_tensors="pt"  )
   _SCREAMING_SNAKE_CASE	=				encoding["pixel_values"]

   _SCREAMING_SNAKE_CASE	=				model(__A  )
   _SCREAMING_SNAKE_CASE	=				outputs.logits

   # verify logits
   _SCREAMING_SNAKE_CASE	=				[1, 16] if "rvlcdip" in checkpoint_url else [1, 1_96, 81_92]
   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:
         _SCREAMING_SNAKE_CASE	=				"dit-base" if "base" in checkpoint_url else "dit-large"
      else:
         _SCREAMING_SNAKE_CASE	=				"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__":
 lowerCamelCase_			=						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',
 )
 lowerCamelCase_			=						parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''



from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''efficientnet'''



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 6_0_0  ,				__lowerCamelCase :     float = 2.0  ,				__lowerCamelCase :     float = 3.1  ,				__lowerCamelCase :     int = 8  ,				__lowerCamelCase :     List[int] = [3, 3, 5, 3, 5, 5, 3]  ,				__lowerCamelCase :     List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]  ,				__lowerCamelCase :     List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]  ,				__lowerCamelCase :     List[int] = []  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 2, 1, 2, 1]  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 3, 3, 4, 1]  ,				__lowerCamelCase :     List[int] = [1, 6, 6, 6, 6, 6, 6]  ,				__lowerCamelCase :     float = 0.2_5  ,				__lowerCamelCase :     str = "swish"  ,				__lowerCamelCase :     int = 2_5_6_0  ,				__lowerCamelCase :     str = "mean"  ,				__lowerCamelCase :     float = 0.0_2  ,				__lowerCamelCase :     float = 0.0_0_1  ,				__lowerCamelCase :     float = 0.9_9  ,				__lowerCamelCase :     float = 0.5  ,				__lowerCamelCase :     float = 0.2  ,				**__lowerCamelCase :     Tuple  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				num_channels
      _SCREAMING_SNAKE_CASE	=				image_size
      _SCREAMING_SNAKE_CASE	=				width_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_divisor
      _SCREAMING_SNAKE_CASE	=				kernel_sizes
      _SCREAMING_SNAKE_CASE	=				in_channels
      _SCREAMING_SNAKE_CASE	=				out_channels
      _SCREAMING_SNAKE_CASE	=				depthwise_padding
      _SCREAMING_SNAKE_CASE	=				strides
      _SCREAMING_SNAKE_CASE	=				num_block_repeats
      _SCREAMING_SNAKE_CASE	=				expand_ratios
      _SCREAMING_SNAKE_CASE	=				squeeze_expansion_ratio
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dim
      _SCREAMING_SNAKE_CASE	=				pooling_type
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				batch_norm_eps
      _SCREAMING_SNAKE_CASE	=				batch_norm_momentum
      _SCREAMING_SNAKE_CASE	=				dropout_rate
      _SCREAMING_SNAKE_CASE	=				drop_connect_rate
      _SCREAMING_SNAKE_CASE	=				sum(__lowerCamelCase   ) * 4







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						version.parse('''1.11'''       )



   @property
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return 1e-5

