


"""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__	: Dict				,   UpperCamelCase__	: Optional[Any]=False				,   UpperCamelCase__	: int=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__	: str				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]=False				,   UpperCamelCase__	: str=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__	: int				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Tuple ):






	"""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__	: List[Any]				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str=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 pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def lowerCAmelCase_      (				UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 assert isinstance(UpperCamelCase__				,   UpperCamelCase__ )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""				,   [False, True] )
def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__				,   keep_in_memory=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize(
    """features"""				,   [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,   )
def lowerCAmelCase_      (				UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	features.copy() if features else default_expected_features
 __lowercase									=	(
     Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
 )
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   features=UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize("""split"""				,   [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Tuple ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__				,   split=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )
 assert dataset.split == split if split else "train"


@pytest.mark.parametrize("""path_type"""				,   [str, list] )
def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 if issubclass(UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	text_path
 elif issubclass(UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	[text_path]
 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any=("train",) ):






 """simple docstring"""







 assert isinstance(UpperCamelCase__				,   UpperCamelCase__ )
 for split in splits:
  __lowercase									=	dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""				,   [False, True] )
def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __lowercase									=	TextDatasetReader({"""train""": text_path}				,   cache_dir=UpperCamelCase__				,   keep_in_memory=UpperCamelCase__ ).read()
 _check_text_datasetdict(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize(
    """features"""				,   [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,   )
def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	features.copy() if features else default_expected_features
 __lowercase									=	(
     Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
 )
 __lowercase									=	TextDatasetReader({"""train""": text_path}				,   features=UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_datasetdict(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize("""split"""				,   [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: int				,   UpperCamelCase__	: int ):






 """simple docstring"""







 if split:
  __lowercase									=	{split: text_path}
 else:
  __lowercase									=	"""train"""
  __lowercase									=	{"""train""": text_path, """test""": text_path}
 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_datasetdict(UpperCamelCase__				,   UpperCamelCase__				,   splits=list(path.keys() ) )
 assert all(dataset[split].split == split for split in path.keys() )
