

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Dict=False	,       SCREAMING_SNAKE_CASE_:							Dict=False      )			->      Optional[int]:




				'''simple docstring'''





				A__        =			"backbone." if is_semantic else ""

				A__        =			[]
				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__							(	SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Optional[int]=False	,       SCREAMING_SNAKE_CASE_:							int=False      )			->      Optional[int]:




				'''simple docstring'''





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

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

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

								A__        =			gamma_a
								A__        =			gamma_a



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							List[Any]      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			dct.pop(SCREAMING_SNAKE_CASE_      )
				A__        =			val



def 					lowerCAmelCase__							(	)			->      int:




				'''simple docstring'''





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



@torch.no_grad()
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							Any=False      )			->      Dict:




				'''simple docstring'''





				A__        =			False if "rvlcdip" in checkpoint_url else True
				A__        =			BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_	,       use_mask_token=SCREAMING_SNAKE_CASE_      )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								A__        =			1_0_2_4
								A__        =			4_0_9_6
								A__        =			2_4
								A__        =			1_6

				# labels
				if "rvlcdip" in checkpoint_url:
								A__        =			1_6
								A__        =			"huggingface/label-files"
								A__        =			"rvlcdip-id2label.json"
								A__        =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       repo_type="dataset"      )	,       "r"      )      )
								A__        =			{int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}
								A__        =			idalabel
								A__        =			{v: k for k, v in idalabel.items()}

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

				A__        =			create_rename_keys(SCREAMING_SNAKE_CASE_	,       has_lm_head=SCREAMING_SNAKE_CASE_      )
				for src, dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				read_in_q_k_v(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       has_lm_head=SCREAMING_SNAKE_CASE_      )

				# load HuggingFace model
				A__        =			BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_      ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_      )
				model.eval()
				model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

				A__        =			image_processor(images=SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      )
				A__        =			encoding["pixel_values"]

				A__        =			model(SCREAMING_SNAKE_CASE_      )
				A__        =			outputs.logits

				# verify logits
				A__        =			[1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_      ), "Shape of logits not as expected"

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

				if push_to_hub:
								if has_lm_head:
												A__        =			"dit-base" if "base" in checkpoint_url else "dit-large"
								else:
												A__        =			"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(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )	,       organization="nielsr"	,       commit_message="Add image processor"	,       use_temp_dir=SCREAMING_SNAKE_CASE_	,       )
								model.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )	,       organization="nielsr"	,       commit_message="Add model"	,       use_temp_dir=SCREAMING_SNAKE_CASE_	,       )


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)




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=0.9      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      )  ->						str:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 30}
							A__        =			crop_size if crop_size is not None else {"height": 30, "width": 30}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize_and_center_crop
							A__        =			size
							A__        =			crop_pct
							A__        =			crop_size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       PoolFormerImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			PoolFormerImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize_and_center_crop"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )
							self.assertTrue(hasattr(lowercase      ,      "crop_pct"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 30, "width": 30}       )

							A__        =			self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      Image.Image       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values

							self.assertEqual(
							    encoded_images.shape      ,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      numpify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      np.ndarray       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      torchify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      torch.Tensor       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

