






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			:		Tuple   				=    logging.get_logger(__name__)





def 	lowerCAmelCase_		(lowercase__						:       Tuple     ,					lowercase__						:       Tuple=False     ,					lowercase__						:       List[Any]=False       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						'''backbone.''' if is_semantic else ''''''

				lowerCAmelCase__      			=						[]
				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__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Tuple=False     ,					lowercase__						:       List[str]=False       )		->    List[str]:


				'''simple docstring'''




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

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

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

								lowerCAmelCase__      			=						gamma_a
								lowerCAmelCase__      			=						gamma_a





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       str       )		->    List[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						dct.pop(lowercase__       )
				lowerCAmelCase__      			=						val





def 	lowerCAmelCase_		()		->    List[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowerCAmelCase__      			=						Image.open(requests.get(lowercase__     ,					stream=lowercase__       ).raw       )
				return im





@torch.no_grad()
def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       Dict     ,					lowercase__						:       Tuple=False       )		->    Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						False if '''rvlcdip''' in checkpoint_url else True
				lowerCAmelCase__      			=						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:
								lowerCAmelCase__      			=						10_24
								lowerCAmelCase__      			=						40_96
								lowerCAmelCase__      			=						24
								lowerCAmelCase__      			=						16

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

				# load state_dict of original model, remove and rename some keys
				lowerCAmelCase__      			=						torch.hub.load_state_dict_from_url(lowercase__     ,					map_location='''cpu'''       )['''model''']

				lowerCAmelCase__      			=						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
				lowerCAmelCase__      			=						BeitForMaskedImageModeling(lowercase__       ) if has_lm_head else BeitForImageClassification(lowercase__       )
				model.eval()
				model.load_state_dict(lowercase__       )

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

				lowerCAmelCase__      			=						image_processor(images=lowercase__     ,					return_tensors='''pt'''       )
				lowerCAmelCase__      			=						encoding['''pixel_values''']

				lowerCAmelCase__      			=						model(lowercase__       )
				lowerCAmelCase__      			=						outputs.logits

				# verify logits
				lowerCAmelCase__      			=						[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
				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:
												lowerCAmelCase__      			=						'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
								else:
												lowerCAmelCase__      			=						'''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__":
			_UpperCAmelCase			:		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",
			)
			_UpperCAmelCase			:		List[str]   				=    parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)













from math import isclose, sqrt





def 	lowerCAmelCase_		(lowercase__						:       float     ,					lowercase__						:       float     ,					lowercase__						:       float       )		->    tuple[float, float, float]:


				'''simple docstring'''




				lowerCAmelCase__      			=						point_y / 4 / point_x
				lowerCAmelCase__      			=						2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				lowerCAmelCase__      			=						(1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				lowerCAmelCase__      			=						(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				lowerCAmelCase__      			=						outgoing_gradient**2 + 4
				lowerCAmelCase__      			=						2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				lowerCAmelCase__      			=						(point_y - outgoing_gradient * point_x) ** 2 - 1_00

				lowerCAmelCase__      			=						(
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
				) / (2 * quadratic_term)
				lowerCAmelCase__      			=						(
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				lowerCAmelCase__      			=						x_minus if isclose(lowercase__     ,					lowercase__       ) else x_plus
				lowerCAmelCase__      			=						point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient





def 	lowerCAmelCase_		(lowercase__						:       float = 1.4     ,					lowercase__						:       float = -9.6       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						0
				lowerCAmelCase__      			=						first_x_coord
				lowerCAmelCase__      			=						first_y_coord
				lowerCAmelCase__      			=						(10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						next_point(lowercase__     ,					lowercase__     ,					lowercase__       )
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
			print(F'''{solution() = }''')





