"""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()
__lowerCamelCase									=	logging.get_logger(__name__)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=False							,     UpperCamelCase__=False					):






							"""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'),
							    ]					)

							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       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=False							,     UpperCamelCase__=False					):






							"""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

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

																					A__						  =				table
																					A__						  =				index






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				dct.pop(_SCREAMING_SNAKE_CASE					)
							A__						  =				val






def 	UpperCAmelCase       (						):






							"""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 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				BeitConfig()
							A__						  =				False
							A__						  =				False
							A__						  =				'huggingface/label-files'
							# set config parameters based on URL
							if checkpoint_url[-9:-4] == "pt22k":
														# masked image modeling
														A__						  =				True
														A__						  =				True
														A__						  =				True
							elif checkpoint_url[-9:-4] == "ft22k":
														# intermediate fine-tuning on ImageNet-22k
														A__						  =				True
														A__						  =				21_841
														A__						  =				'imagenet-22k-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()}
														# 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[9_205]
														del idalabel[15_027]
														A__						  =				idalabel
														A__						  =				{v: k for k, v in idalabel.items()}
							elif checkpoint_url[-8:-4] == "to1k":
														# fine-tuning on ImageNet-1k
														A__						  =				True
														A__						  =				1_000
														A__						  =				'imagenet-1k-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()}
														if "384" in checkpoint_url:
																					A__						  =				384
														if "512" in checkpoint_url:
																					A__						  =				512
							elif "ade20k" in checkpoint_url:
														# fine-tuning
														A__						  =				True
														A__						  =				150
														A__						  =				'ade20k-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()}
														A__						  =				640
														A__						  =				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:
														A__						  =				1_024
														A__						  =				4_096
														A__						  =				24
														A__						  =				16
														if "ade20k" in checkpoint_url:
																					A__						  =				640
																					A__						  =				[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
							A__						  =				torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE							,     map_location='cpu'							,     check_hash=_SCREAMING_SNAKE_CASE					)
							A__						  =				state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

							A__						  =				create_rename_keys(_SCREAMING_SNAKE_CASE							,     has_lm_head=_SCREAMING_SNAKE_CASE							,     is_semantic=_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							,     is_semantic=_SCREAMING_SNAKE_CASE					)
							if is_semantic:
														# add prefix to decoder keys
														for key, val in state_dict.copy().items():
																					A__						  =				state_dict.pop(_SCREAMING_SNAKE_CASE					)
																					if key.startswith('backbone.fpn'					):
																												A__						  =				key.replace('backbone.fpn'							,     'fpn'					)
																					A__						  =				val

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

							# Check outputs on an image
							if is_semantic:
														A__						  =				BeitImageProcessor(size=config.image_size							,     do_center_crop=_SCREAMING_SNAKE_CASE					)
														A__						  =				load_dataset('hf-internal-testing/fixtures_ade20k'							,     split='test'					)
														A__						  =				Image.open(ds[0]['file']					)
							else:
														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__						  =				torch.Size([1, 1_000]					)
							if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'					):
														A__						  =				torch.Size([1, 196, 8_192]					)
							elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'					):
														A__						  =				torch.Size([1, 196, 8_192]					)
							elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'					):
														A__						  =				torch.Size([1, 21_841]					)
														A__						  =				torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]					)
														A__						  =				2_397
							elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'					):
														A__						  =				torch.Size([1, 21_841]					)
														A__						  =				torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]					)
														A__						  =				2_396
							elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'					):
														A__						  =				torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]					)
														A__						  =				285
							elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'					):
														A__						  =				torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]					)
														A__						  =				281
							elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'					):
														A__						  =				torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]					)
														A__						  =				761
							elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'					):
														A__						  =				torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]					)
														A__						  =				761
							elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'					):
														A__						  =				torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]					)
														A__						  =				761
							elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'					):
														A__						  =				torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]					)
														A__						  =				761
							elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'					):
														A__						  =				torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]					)
														A__						  =				761
							elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'					):
														A__						  =				(1, 150, 160, 160)
														A__						  =				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'					):
														A__						  =				(1, 150, 160, 160)
														A__						  =				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]							,     _SCREAMING_SNAKE_CASE							,     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]							,     _SCREAMING_SNAKE_CASE							,     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(_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 __name__ == "__main__":
				__lowerCamelCase									=	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."
				)
				__lowerCamelCase									=	parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""
from __future__ import annotations

import sys







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->int:
  if n < 0:
    raise ValueError('Negative arguments are not supported'  )
  return _fib(_SCREAMING_SNAKE_CASE  )[0]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->tuple[int, int]:
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  a__				,       a__:	Optional[int]     				=      _fib(n // 2  )
  a__:	List[str]     				=      a * (b * 2 - a)
  a__:	Dict     				=      a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 lowercase__   			=      int(sys.argv[1])
 print(f"fibonacci({n}) is {fibonacci(n)}")


