



'''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()
a     :    Tuple      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False			,       _lowercase=False	)   ->    Optional[Any]:
							UpperCAmelCase  :	Optional[int]							  =	"""backbone.""" if is_semantic else """"""

							UpperCAmelCase  :	List[Any]							  =	[]
							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						(      _lowercase			,       _lowercase			,       _lowercase=False			,       _lowercase=False	)   ->    Optional[int]:




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

														UpperCAmelCase  :	str							  =	in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCAmelCase  :	List[str]							  =	q_bias
														UpperCAmelCase  :	Tuple							  =	in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase  :	str							  =	in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase  :	Optional[Any]							  =	v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														UpperCAmelCase  :	List[str]							  =	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''	)
														UpperCAmelCase  :	Union[str, Any]							  =	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''	)

														UpperCAmelCase  :	Union[str, Any]							  =	gamma_a
														UpperCAmelCase  :	Dict							  =	gamma_a



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Tuple:
							UpperCAmelCase  :	str							  =	dct.pop(_lowercase	)
							UpperCAmelCase  :	int							  =	val



def    __lowerCamelCase						(      )   ->    int:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Optional[Any]							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)
							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=False	)   ->    str:
							UpperCAmelCase  :	Optional[Any]							  =	False if """rvlcdip""" in checkpoint_url else True
							UpperCAmelCase  :	Union[str, Any]							  =	BeitConfig(use_absolute_position_embeddings=_lowercase			,       use_mask_token=_lowercase	)

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														UpperCAmelCase  :	Optional[Any]							  =	1_0_2_4
														UpperCAmelCase  :	Optional[int]							  =	4_0_9_6
														UpperCAmelCase  :	Optional[Any]							  =	2_4
														UpperCAmelCase  :	Optional[Any]							  =	1_6

							# labels
							if "rvlcdip" in checkpoint_url:
														UpperCAmelCase  :	Tuple							  =	1_6
														UpperCAmelCase  :	int							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	Optional[int]							  =	"""rvlcdip-id2label.json"""
														UpperCAmelCase  :	int							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	List[Any]							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	str							  =	idalabel
														UpperCAmelCase  :	Optional[Any]							  =	{v: k for k, v in idalabel.items()}

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

							UpperCAmelCase  :	List[str]							  =	create_rename_keys(_lowercase			,       has_lm_head=_lowercase	)
							for src, dest in rename_keys:
														rename_key(_lowercase			,       _lowercase			,       _lowercase	)
							read_in_q_k_v(_lowercase			,       _lowercase			,       has_lm_head=_lowercase	)

							# load HuggingFace model
							UpperCAmelCase  :	Optional[int]							  =	BeitForMaskedImageModeling(_lowercase	) if has_lm_head else BeitForImageClassification(_lowercase	)
							model.eval()
							model.load_state_dict(_lowercase	)

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

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

							UpperCAmelCase  :	Optional[Any]							  =	model(_lowercase	)
							UpperCAmelCase  :	Any							  =	outputs.logits

							# verify logits
							UpperCAmelCase  :	Union[str, Any]							  =	[1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
							assert logits.shape == torch.Size(_lowercase	), "Shape of logits not as expected"

							Path(_lowercase	).mkdir(exist_ok=_lowercase	)
							print(F'''Saving model to {pytorch_dump_folder_path}'''	)
							model.save_pretrained(_lowercase	)
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
							image_processor.save_pretrained(_lowercase	)

							if push_to_hub:
														if has_lm_head:
																					UpperCAmelCase  :	Optional[Any]							  =	"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
														else:
																					UpperCAmelCase  :	Tuple							  =	"""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(_lowercase			,       _lowercase	)			,       organization="""nielsr"""			,       commit_message="""Add image processor"""			,       use_temp_dir=_lowercase			,       )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowercase			,       _lowercase	)			,       organization="""nielsr"""			,       commit_message="""Add model"""			,       use_temp_dir=_lowercase			,       )


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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

