




"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A 		= logging.get_logger(__name__)



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=False					,				__UpperCamelCase=False )       ->				str:
	_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"""),
	    ] )

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ("""mask_token""", """beit.embeddings.mask_token"""),
		        (
		            """rel_pos_bias.relative_position_bias_table""",
		            """beit.encoder.relative_position_bias.relative_position_bias_table""",
		        ),
		        (
		            """rel_pos_bias.relative_position_index""",
		            """beit.encoder.relative_position_bias.relative_position_index""",
		        ),
		        ("""norm.weight""", """layernorm.weight"""),
		        ("""norm.bias""", """layernorm.bias"""),
		    ] )
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
		        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
		        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
		        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.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(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase=False					,				__UpperCamelCase=False )       ->				Optional[Any]:







	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

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			_lowerCAmelCase			=state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table''' )
			_lowerCAmelCase			=state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index''' )

			_lowerCAmelCase			=table
			_lowerCAmelCase			=index



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Tuple:
	_lowerCAmelCase			=dct.pop(__UpperCamelCase )
	_lowerCAmelCase			=val



def 					_lowerCamelCase()       ->				str:
	_lowerCAmelCase			='http://images.cocodataset.org/val2017/000000039769.jpg'
	_lowerCAmelCase			=Image.open(requests.get(__UpperCamelCase					,				stream=__UpperCamelCase ).raw )
	return im



@torch.no_grad()
def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Any:
	_lowerCAmelCase			=BeitConfig()
	_lowerCAmelCase			=False
	_lowerCAmelCase			=False
	_lowerCAmelCase			='huggingface/label-files'
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		_lowerCAmelCase			=True
		_lowerCAmelCase			=True
		_lowerCAmelCase			=True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		_lowerCAmelCase			=True
		_lowerCAmelCase			=21841
		_lowerCAmelCase			='imagenet-22k-id2label.json'
		_lowerCAmelCase			=json.load(open(hf_hub_download(__UpperCamelCase					,				__UpperCamelCase					,				repo_type="""dataset""" )					,				"""r""" ) )
		_lowerCAmelCase			={int(__UpperCamelCase ): v for k, v in idalabel.items()}
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9205]
		del idalabel[15027]
		_lowerCAmelCase			=idalabel
		_lowerCAmelCase			={v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		_lowerCAmelCase			=True
		_lowerCAmelCase			=1000
		_lowerCAmelCase			='imagenet-1k-id2label.json'
		_lowerCAmelCase			=json.load(open(hf_hub_download(__UpperCamelCase					,				__UpperCamelCase					,				repo_type="""dataset""" )					,				"""r""" ) )
		_lowerCAmelCase			={int(__UpperCamelCase ): v for k, v in idalabel.items()}
		_lowerCAmelCase			=idalabel
		_lowerCAmelCase			={v: k for k, v in idalabel.items()}
		if "384" in checkpoint_url:
			_lowerCAmelCase			=384
		if "512" in checkpoint_url:
			_lowerCAmelCase			=512
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		_lowerCAmelCase			=True
		_lowerCAmelCase			=150
		_lowerCAmelCase			='ade20k-id2label.json'
		_lowerCAmelCase			=json.load(open(hf_hub_download(__UpperCamelCase					,				__UpperCamelCase					,				repo_type="""dataset""" )					,				"""r""" ) )
		_lowerCAmelCase			={int(__UpperCamelCase ): v for k, v in idalabel.items()}
		_lowerCAmelCase			=idalabel
		_lowerCAmelCase			={v: k for k, v in idalabel.items()}
		_lowerCAmelCase			=640
		_lowerCAmelCase			=True
	else:
		raise ValueError("""Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\'""" )

	# size of the architecture
	if "base" in checkpoint_url:
		pass
	elif "large" in checkpoint_url:
		_lowerCAmelCase			=1024
		_lowerCAmelCase			=4096
		_lowerCAmelCase			=24
		_lowerCAmelCase			=16
		if "ade20k" in checkpoint_url:
			_lowerCAmelCase			=640
			_lowerCAmelCase			=[7, 11, 15, 23]
	else:
		raise ValueError("""Should either find \'base\' or \'large\' in checkpoint URL""" )

	# load state_dict of original model, remove and rename some keys
	_lowerCAmelCase			=torch.hub.load_state_dict_from_url(__UpperCamelCase					,				map_location="""cpu"""					,				check_hash=__UpperCamelCase )
	_lowerCAmelCase			=state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

	_lowerCAmelCase			=create_rename_keys(__UpperCamelCase					,				has_lm_head=__UpperCamelCase					,				is_semantic=__UpperCamelCase )
	for src, dest in rename_keys:
		rename_key(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )
	read_in_q_k_v(__UpperCamelCase					,				__UpperCamelCase					,				has_lm_head=__UpperCamelCase					,				is_semantic=__UpperCamelCase )
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
			if key.startswith("""backbone.fpn""" ):
				_lowerCAmelCase			=key.replace("""backbone.fpn"""					,				"""fpn""" )
			_lowerCAmelCase			=val

    # load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		_lowerCAmelCase			=BeitForMaskedImageModeling(__UpperCamelCase )
	elif "ade20k" in checkpoint_url:
		_lowerCAmelCase			=BeitForSemanticSegmentation(__UpperCamelCase )
	else:
		_lowerCAmelCase			=BeitForImageClassification(__UpperCamelCase )
	model.eval()
	model.load_state_dict(__UpperCamelCase )

	# Check outputs on an image
	if is_semantic:
		_lowerCAmelCase			=BeitImageProcessor(size=config.image_size					,				do_center_crop=__UpperCamelCase )
		_lowerCAmelCase			=load_dataset("""hf-internal-testing/fixtures_ade20k"""					,				split="""test""" )
		_lowerCAmelCase			=Image.open(ds[0]["""file"""] )
	else:
		_lowerCAmelCase			=BeitImageProcessor(
		    size=config.image_size					,				resample=PILImageResampling.BILINEAR					,				do_center_crop=__UpperCamelCase )
		_lowerCAmelCase			=prepare_img()

	_lowerCAmelCase			=image_processor(images=__UpperCamelCase					,				return_tensors="""pt""" )
	_lowerCAmelCase			=encoding['pixel_values']

	_lowerCAmelCase			=model(__UpperCamelCase )
	_lowerCAmelCase			=outputs.logits

	# verify logits
	_lowerCAmelCase			=torch.Size([1, 1000] )
	if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k""" ):
		_lowerCAmelCase			=torch.Size([1, 196, 8192] )
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k""" ):
		_lowerCAmelCase			=torch.Size([1, 196, 8192] )
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k""" ):
		_lowerCAmelCase			=torch.Size([1, 21841] )
		_lowerCAmelCase			=torch.tensor([2.22_88, 2.46_71, 0.73_95] )
		_lowerCAmelCase			=2397
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k""" ):
		_lowerCAmelCase			=torch.Size([1, 21841] )
		_lowerCAmelCase			=torch.tensor([1.68_81, -0.27_87, 0.59_01] )
		_lowerCAmelCase			=2396
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k""" ):
		_lowerCAmelCase			=torch.tensor([0.12_41, 0.07_98, -0.65_69] )
		_lowerCAmelCase			=285
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k""" ):
		_lowerCAmelCase			=torch.tensor([-1.23_85, -1.09_87, -1.01_08] )
		_lowerCAmelCase			=281
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k""" ):
		_lowerCAmelCase			=torch.tensor([-1.53_03, -0.94_84, -0.31_47] )
		_lowerCAmelCase			=761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k""" ):
		_lowerCAmelCase			=torch.tensor([0.46_10, -0.09_28, 0.20_86] )
		_lowerCAmelCase			=761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k""" ):
		_lowerCAmelCase			=torch.tensor([-0.48_04, 0.62_57, -0.18_37] )
		_lowerCAmelCase			=761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k""" ):
		_lowerCAmelCase			=torch.tensor([[-0.51_22, 0.51_17, -0.21_13]] )
		_lowerCAmelCase			=761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k""" ):
		_lowerCAmelCase			=torch.tensor([-0.30_62, 0.72_61, 0.48_52] )
		_lowerCAmelCase			=761
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k""" ):
		_lowerCAmelCase			=(1, 150, 160, 160)
		_lowerCAmelCase			=torch.tensor(
		    [
		        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
		        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
		        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
		    ] )
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k""" ):
		_lowerCAmelCase			=(1, 150, 160, 160)
		_lowerCAmelCase			=torch.tensor(
		    [
		        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
		        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
		        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
		    ] )
	else:
		raise ValueError("""Can\'t verify logits as model is not supported""" )

	assert logits.shape == expected_shape, "Shape of logits not as expected"
	if not has_lm_head:
		if is_semantic:
			assert torch.allclose(
			    logits[0, :3, :3, :3]					,				__UpperCamelCase					,				atol=1E-3 ), "First elements of logits not as expected"
		else:
			print("""Predicted class idx:"""					,				logits.argmax(-1 ).item() )
			assert torch.allclose(
			    logits[0, :3]					,				__UpperCamelCase					,				atol=1E-3 ), "First elements of logits not as expected"
			assert logits.argmax(-1 ).item() == expected_class_idx, "Predicted class index not as expected"

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


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_url',
			    default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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.'
			)
			__A 		= parser.parse_args()
			convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""


def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative""" )
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer""" )
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)



