


"""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__  							=logging.get_logger(__name__)


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: Union[str, Any]=False				,   UpperCamelCase__	: List[str]=False ):






 """simple docstring"""







 __lowercase									=	"""backbone.""" if is_semantic else """"""

 __lowercase									=	[]
 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 lowerCAmelCase_      (				UpperCamelCase__	: Any				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Tuple=False				,   UpperCamelCase__	: int=False ):






 """simple docstring"""







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

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

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

  __lowercase									=	gamma_a
  __lowercase									=	gamma_a


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	dct.pop(UpperCamelCase__ )
 __lowercase									=	val


def lowerCAmelCase_      (				):






 """simple docstring"""







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


@torch.no_grad()
def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: Any=False ):






 """simple docstring"""







 __lowercase									=	False if """rvlcdip""" in checkpoint_url else True
 __lowercase									=	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:
  __lowercase									=	1024
  __lowercase									=	4096
  __lowercase									=	24
  __lowercase									=	16

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

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

 __lowercase									=	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
 __lowercase									=	BeitForMaskedImageModeling(UpperCamelCase__ ) if has_lm_head else BeitForImageClassification(UpperCamelCase__ )
 model.eval()
 model.load_state_dict(UpperCamelCase__ )

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

 __lowercase									=	image_processor(images=UpperCamelCase__				,   return_tensors="""pt""" )
 __lowercase									=	encoding["""pixel_values"""]

 __lowercase									=	model(UpperCamelCase__ )
 __lowercase									=	outputs.logits

 # verify logits
 __lowercase									=	[1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
 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:
   __lowercase									=	"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
  else:
   __lowercase									=	"""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__  							=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__  							=parser.parse_args()
       convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""

import os

import numpy
import onnx


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	a.name
 __lowercase									=	b.name

 __lowercase									=	""""""
 __lowercase									=	""""""

 __lowercase									=	a == b

 __lowercase									=	name_a
 __lowercase									=	name_b

 return res


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







 for i, input_name in enumerate(node_proto.input ):
  if input_name == name:
   node_proto.input.insert(UpperCamelCase__				,   UpperCamelCase__ )
   node_proto.input.pop(i + 1 )

 if node_proto.op_type == "If":
  _graph_replace_input_with(node_proto.attribute[0].g				,   UpperCamelCase__				,   UpperCamelCase__ )
  _graph_replace_input_with(node_proto.attribute[1].g				,   UpperCamelCase__				,   UpperCamelCase__ )
 if node_proto.op_type == "Loop":
  _graph_replace_input_with(node_proto.attribute[0].g				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 for n in graph_proto.node:
  _node_replace_input_with(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	list(model.graph.initializer )
 __lowercase									=	list(model_without_ext.graph.initializer )
 for i, ref_i in ind_to_replace:
  assert inits_with_data[i].name == inits[i].name
  assert inits_with_data[ref_i].name == inits[ref_i].name
  assert i > ref_i

  __lowercase									=	inits[i].name
  __lowercase									=	inits[ref_i].name

  model_without_ext.graph.initializer.remove(inits[i] )

  # for n in model.graph.node:
  _graph_replace_input_with(model_without_ext.graph				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	os.path.dirname(UpperCamelCase__ )
 __lowercase									=	os.path.basename(UpperCamelCase__ )

 __lowercase									=	onnx.load(os.path.join(UpperCamelCase__				,   UpperCamelCase__ ) )

 __lowercase									=	list(model.graph.initializer )

 __lowercase									=	set()
 __lowercase									=	{}
 __lowercase									=	[]

 __lowercase									=	0

 for i in range(len(UpperCamelCase__ ) ):
  if i in dup_set:
   continue

  for j in range(i + 1				,   len(UpperCamelCase__ ) ):
   if j in dup_set:
    continue
   if _is_equal_tensor_proto(inits[i]				,   inits[j] ):
    dup_set.add(UpperCamelCase__ )
    dup_set.add(UpperCamelCase__ )

    __lowercase									=	inits[j].data_type
    __lowercase									=	numpy.prod(inits[j].dims )
    if dtype == 1:
     mem_size *= 4
    elif dtype == 6:
     mem_size *= 4
    elif dtype == 7 or dtype == 11:
     mem_size *= 8
    else:
     print("""unexpected data type: """				,   UpperCamelCase__ )
    total_reduced_size += mem_size

    __lowercase									=	inits[i].name
    __lowercase									=	inits[j].name

    if name_i in dup_map:
     dup_map[name_i].append(UpperCamelCase__ )
    else:
     __lowercase									=	[name_j]
    ind_to_replace.append((j, i) )

 print("""total reduced size: """				,   total_reduced_size / 1024 / 1024 / 1024				,   """GB""" )

 __lowercase									=	sorted(UpperCamelCase__ )
 _remove_dup_initializers_from_model(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	"""optimized_""" + model_file_name
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 onnx.save(UpperCamelCase__				,   UpperCamelCase__ )

 return new_model
