




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_		:				Optional[Any]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False	,     lowerCamelCase=False   ):
				__magic_name__	:	Any								="""backbone.""" if is_semantic else """"""

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

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

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

								__magic_name__	:	Union[str, Any]								=gamma_a
								__magic_name__	:	Union[str, Any]								=gamma_a




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=dct.pop(lowerCamelCase   )
				__magic_name__	:	str								=val




def 		lowerCAmelCase_   (	):
				__magic_name__	:	str								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__	:	Any								=Image.open(requests.get(lowerCamelCase	,     stream=lowerCamelCase   ).raw   )
				return im




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__	:	Tuple								=False if """rvlcdip""" in checkpoint_url else True
				__magic_name__	:	Any								=BeitConfig(use_absolute_position_embeddings=lowerCamelCase	,     use_mask_token=lowerCamelCase   )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								__magic_name__	:	List[str]								=1024
								__magic_name__	:	Optional[int]								=4096
								__magic_name__	:	Optional[Any]								=24
								__magic_name__	:	Dict								=16

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

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

				__magic_name__	:	Union[str, Any]								=create_rename_keys(lowerCamelCase	,     has_lm_head=lowerCamelCase   )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				read_in_q_k_v(lowerCamelCase	,     lowerCamelCase	,     has_lm_head=lowerCamelCase   )

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

				# Check outputs on an image
				__magic_name__	:	str								=BeitImageProcessor(
				    size=config.image_size	,     resample=PILImageResampling.BILINEAR	,     do_center_crop=lowerCamelCase   )
				__magic_name__	:	Dict								=prepare_img()

				__magic_name__	:	int								=image_processor(images=lowerCamelCase	,     return_tensors="""pt"""   )
				__magic_name__	:	Dict								=encoding["""pixel_values"""]

				__magic_name__	:	Union[str, Any]								=model(lowerCamelCase   )
				__magic_name__	:	List[Any]								=outputs.logits

				# verify logits
				__magic_name__	:	Tuple								=[1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
				assert logits.shape == torch.Size(lowerCamelCase   ), "Shape of logits not as expected"

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

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


if __name__ == "__main__":
							UpperCAmelCase_		:				List[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",
							)
							UpperCAmelCase_		:				int									=     parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__snake_case )
										requires_backends(self    , """vision""" )
										self.check_model_type(
										    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
										    if self.framework == """tf"""
										    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )

						def     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}
										if top_k is not None:
														__magic_name__	:	str								=top_k
										return {}, {}, postprocess_params

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return super().__call__(__snake_case    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

										if self.framework == "pt":
														__magic_name__	:	int								=model_outputs.logits.softmax(-1 )[0]
														__magic_name__				, __magic_name__	:	str								=probs.topk(__snake_case )
										elif self.framework == "tf":
														__magic_name__	:	Union[str, Any]								=stable_softmax(model_outputs.logits    , axis=-1 )[0]
														__magic_name__	:	List[str]								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	str								=topk.values.numpy(), topk.indices.numpy()
										else:
														raise ValueError(f"Unsupported framework: {self.framework}" )

										__magic_name__	:	str								=scores.tolist()
										__magic_name__	:	Union[str, Any]								=ids.tolist()
										return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case    , __snake_case )]




