


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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False		,							SCREAMING_SNAKE_CASE=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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False		,							SCREAMING_SNAKE_CASE=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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= dct.pop(SCREAMING_SNAKE_CASE	)
		lowercase__				= val
def      _a					( ):


		"""simple docstring"""




		lowercase__				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__				= Image.open(requests.get(SCREAMING_SNAKE_CASE		,							stream=SCREAMING_SNAKE_CASE	).raw	)
		return im
@torch.no_grad()
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False	):


		"""simple docstring"""




		lowercase__				= False if '''rvlcdip''' in checkpoint_url else True
		lowercase__				= 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:
				lowercase__				= 10_24
				lowercase__				= 40_96
				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(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							repo_type='''dataset'''	)		,							'''r'''	)	)
				lowercase__				= {int(SCREAMING_SNAKE_CASE	): 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(SCREAMING_SNAKE_CASE		,							map_location='''cpu'''	)['''model''']

		lowercase__				= 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
		lowercase__				= 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
		lowercase__				= BeitImageProcessor(
		    size=config.image_size		,							resample=PILImageResampling.BILINEAR		,							do_center_crop=SCREAMING_SNAKE_CASE	)
		lowercase__				= prepare_img()

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

		lowercase__				= model(SCREAMING_SNAKE_CASE	)
		lowercase__				= outputs.logits

		# verify logits
		lowercase__				= [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
		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:
						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(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)




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		)     ->						float:
    a__		=			[redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters     ):
        raise ValueError('All input parameters must be positive'     )

    if any(p > 1 for p in parameters[1:4]     ):
        raise ValueError('Relative densities cannot be greater than one'     )
    else:
        a__		=			1 - (matter_density + radiation_density + dark_energy)

        a__		=			(
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        a__		=			hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo LCDM approximation
    a		:       Any     						=       0.3

    print(
        hubble_parameter(
            hubble_constant=68.3,
            radiation_density=1e-4,
            matter_density=matter_density,
            dark_energy=1 - matter_density,
            redshift=0,
        )
    )


