

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	:   List[Any]	=					logging.get_logger(__name__)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Optional[Any]=False      ,  _A					:							Any=False		)				->Dict:



							"""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"""),
							    ]		)

							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        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Optional[Any]      ,  _A					:							Any=False      ,  _A					:							Optional[int]=False		)				->List[Any]:



							"""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

														# 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        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							List[Any]      ,  _A					:							int		)				->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_								=dct.pop(_A		)
							lowerCamelCase_								=val




def        __UpperCamelCase     (      )				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_								=Image.open(requests.get(_A      ,  stream=_A		).raw		)
							return im




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							List[Any]      ,  _A					:							Optional[Any]		)				->Union[str, Any]:



							"""simple docstring"""
							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(_A      ,  _A      ,  repo_type="""dataset"""		)      ,  """r"""		)		)
														lowerCamelCase_								={int(_A		): 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(_A      ,  _A      ,  repo_type="""dataset"""		)      ,  """r"""		)		)
														lowerCamelCase_								={int(_A		): 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(_A      ,  _A      ,  repo_type="""dataset"""		)      ,  """r"""		)		)
														lowerCamelCase_								={int(_A		): 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(_A      ,  map_location="""cpu"""      ,  check_hash=_A		)
							lowerCamelCase_								=state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

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

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

							# Check outputs on an image
							if is_semantic:
														lowerCamelCase_								=BeitImageProcessor(size=config.image_size      ,  do_center_crop=_A		)
														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=_A		)
														lowerCamelCase_								=prepare_img()

							lowerCamelCase_								=image_processor(images=_A      ,  return_tensors="""pt"""		)
							lowerCamelCase_								=encoding["""pixel_values"""]

							lowerCamelCase_								=model(_A		)
							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([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]		)
														lowerCamelCase_								=2397
							elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""		):
														lowerCamelCase_								=torch.Size([1, 21841]		)
														lowerCamelCase_								=torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]		)
														lowerCamelCase_								=2396
							elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""		):
														lowerCamelCase_								=torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]		)
														lowerCamelCase_								=285
							elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""		):
														lowerCamelCase_								=torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]		)
														lowerCamelCase_								=281
							elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""		):
														lowerCamelCase_								=torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]		)
														lowerCamelCase_								=761
							elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""		):
														lowerCamelCase_								=torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]		)
														lowerCamelCase_								=761
							elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""		):
														lowerCamelCase_								=torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]		)
														lowerCamelCase_								=761
							elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""		):
														lowerCamelCase_								=torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]		)
														lowerCamelCase_								=761
							elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""		):
														lowerCamelCase_								=torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]		)
														lowerCamelCase_								=761
							elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""		):
														lowerCamelCase_								=(1, 150, 160, 160)
														lowerCamelCase_								=torch.tensor(
														    [
														        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
														        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
														        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
														    ]		)
							elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""		):
														lowerCamelCase_								=(1, 150, 160, 160)
														lowerCamelCase_								=torch.tensor(
														    [
														        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
														        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
														        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
														    ]		)
							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]      ,  _A      ,  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]      ,  _A      ,  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(_A		).mkdir(exist_ok=_A		)
							print(f'Saving model to {pytorch_dump_folder_path}'		)
							model.save_pretrained(_A		)
							print(f'Saving image processor to {pytorch_dump_folder_path}'		)
							image_processor.save_pretrained(_A		)


if __name__ == "__main__":
	__A	:   int	=					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	:   Optional[int]	=					parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



from math import factorial




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Tuple		)				->Any:



							"""simple docstring"""
							return factorial(_A		) / (factorial(_A		) * factorial(n - r		))




def        __UpperCamelCase     (      )				->Any:



							"""simple docstring"""
							lowerCamelCase_								=0

							for i in range(1      ,  101		):
														for j in range(1      ,  i + 1		):
																					if combinations(_A      ,  _A		) > 1E6:
																												total += 1
							return total


if __name__ == "__main__":
	print(solution())
