"""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__  :  Union[str, Any]											=     logging.get_logger(__name__)



def   a_						( lowerCamelCase ,      lowerCamelCase=False ,      lowerCamelCase=False	):
				UpperCAmelCase__					=							'backbone.' if is_semantic else ''

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

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

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

								UpperCAmelCase__					=							gamma_a
								UpperCAmelCase__					=							gamma_a

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

												UpperCAmelCase__					=							table
												UpperCAmelCase__					=							index



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							dct.pop(lowerCamelCase	)
				UpperCAmelCase__					=							val



def   a_						( ):
				UpperCAmelCase__					=							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__					=							Image.open(requests.get(lowerCamelCase ,      stream=lowerCamelCase	).raw	)
				return im



@torch.no_grad()
def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							BeitConfig()
				UpperCAmelCase__					=							False
				UpperCAmelCase__					=							False
				UpperCAmelCase__					=							'huggingface/label-files'
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								UpperCAmelCase__					=							True
								UpperCAmelCase__					=							True
								UpperCAmelCase__					=							True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								UpperCAmelCase__					=							True
								UpperCAmelCase__					=							2_1_8_4_1
								UpperCAmelCase__					=							'imagenet-22k-id2label.json'
								UpperCAmelCase__					=							json.load(open(hf_hub_download(lowerCamelCase ,      lowerCamelCase ,      repo_type='dataset'	) ,      'r'	)	)
								UpperCAmelCase__					=							{int(lowerCamelCase	): 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_2_0_5]
								del idalabel[1_5_0_2_7]
								UpperCAmelCase__					=							idalabel
								UpperCAmelCase__					=							{v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								UpperCAmelCase__					=							True
								UpperCAmelCase__					=							1_0_0_0
								UpperCAmelCase__					=							'imagenet-1k-id2label.json'
								UpperCAmelCase__					=							json.load(open(hf_hub_download(lowerCamelCase ,      lowerCamelCase ,      repo_type='dataset'	) ,      'r'	)	)
								UpperCAmelCase__					=							{int(lowerCamelCase	): v for k, v in idalabel.items()}
								UpperCAmelCase__					=							idalabel
								UpperCAmelCase__					=							{v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												UpperCAmelCase__					=							3_8_4
								if "512" in checkpoint_url:
												UpperCAmelCase__					=							5_1_2
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								UpperCAmelCase__					=							True
								UpperCAmelCase__					=							1_5_0
								UpperCAmelCase__					=							'ade20k-id2label.json'
								UpperCAmelCase__					=							json.load(open(hf_hub_download(lowerCamelCase ,      lowerCamelCase ,      repo_type='dataset'	) ,      'r'	)	)
								UpperCAmelCase__					=							{int(lowerCamelCase	): v for k, v in idalabel.items()}
								UpperCAmelCase__					=							idalabel
								UpperCAmelCase__					=							{v: k for k, v in idalabel.items()}
								UpperCAmelCase__					=							6_4_0
								UpperCAmelCase__					=							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:
								UpperCAmelCase__					=							1_0_2_4
								UpperCAmelCase__					=							4_0_9_6
								UpperCAmelCase__					=							2_4
								UpperCAmelCase__					=							1_6
								if "ade20k" in checkpoint_url:
												UpperCAmelCase__					=							6_4_0
												UpperCAmelCase__					=							[7, 1_1, 1_5, 2_3]
				else:
								raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'	)

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

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

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

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

				UpperCAmelCase__					=							image_processor(images=lowerCamelCase ,      return_tensors='pt'	)
				UpperCAmelCase__					=							encoding['pixel_values']

				UpperCAmelCase__					=							model(lowerCamelCase	)
				UpperCAmelCase__					=							outputs.logits

				# verify logits
				UpperCAmelCase__					=							torch.Size([1, 1_0_0_0]	)
				if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'	):
								UpperCAmelCase__					=							torch.Size([1, 1_9_6, 8_1_9_2]	)
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'	):
								UpperCAmelCase__					=							torch.Size([1, 1_9_6, 8_1_9_2]	)
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'	):
								UpperCAmelCase__					=							torch.Size([1, 2_1_8_4_1]	)
								UpperCAmelCase__					=							torch.tensor([2.2288, 2.4671, 0.7395]	)
								UpperCAmelCase__					=							2_3_9_7
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'	):
								UpperCAmelCase__					=							torch.Size([1, 2_1_8_4_1]	)
								UpperCAmelCase__					=							torch.tensor([1.6881, -0.2787, 0.5901]	)
								UpperCAmelCase__					=							2_3_9_6
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'	):
								UpperCAmelCase__					=							torch.tensor([0.1241, 0.0798, -0.6569]	)
								UpperCAmelCase__					=							2_8_5
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'	):
								UpperCAmelCase__					=							torch.tensor([-1.2385, -1.0987, -1.0108]	)
								UpperCAmelCase__					=							2_8_1
				elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'	):
								UpperCAmelCase__					=							torch.tensor([-1.5303, -0.9484, -0.3147]	)
								UpperCAmelCase__					=							7_6_1
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'	):
								UpperCAmelCase__					=							torch.tensor([0.4610, -0.0928, 0.2086]	)
								UpperCAmelCase__					=							7_6_1
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'	):
								UpperCAmelCase__					=							torch.tensor([-0.4804, 0.6257, -0.1837]	)
								UpperCAmelCase__					=							7_6_1
				elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'	):
								UpperCAmelCase__					=							torch.tensor([[-0.5122, 0.5117, -0.2113]]	)
								UpperCAmelCase__					=							7_6_1
				elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'	):
								UpperCAmelCase__					=							torch.tensor([-0.3062, 0.7261, 0.4852]	)
								UpperCAmelCase__					=							7_6_1
				elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'	):
								UpperCAmelCase__					=							(1, 1_5_0, 1_6_0, 1_6_0)
								UpperCAmelCase__					=							torch.tensor(
								    [
								        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
								        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
								        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
								    ]	)
				elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'	):
								UpperCAmelCase__					=							(1, 1_5_0, 1_6_0, 1_6_0)
								UpperCAmelCase__					=							torch.tensor(
								    [
								        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
								        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
								        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
								    ]	)
				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] ,      lowerCamelCase ,      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] ,      lowerCamelCase ,      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(lowerCamelCase	).mkdir(exist_ok=lowerCamelCase	)
				print(f'''Saving model to {pytorch_dump_folder_path}'''	)
				model.save_pretrained(lowerCamelCase	)
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
				image_processor.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
						lowerCAmelCase__  :  List[str]											=     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__  :  Any											=     parser.parse_args()
						convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""






from __future__ import annotations
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Tuple   ,lowerCamelCase__       :     int      ):
									UpperCAmelCase__					=							vertices
									UpperCAmelCase__					=							[[0] * vertices for _ in range(lowerCamelCase__      )]







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     list[int]      ):
									print('Vertex \t Distance from Source'      )
									for vertex in range(self.vertices      ):
													print(lowerCamelCase__   ,'\t\t'   ,distances_from_source[vertex]      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     list[int]   ,lowerCamelCase__       :     list[bool]      ):
									UpperCAmelCase__					=							1e7
									UpperCAmelCase__					=							0

									# Search not nearest vertex not in the shortest path tree
									for vertex in range(self.vertices      ):
													if distances_from_source[vertex] < minimum and visited[vertex] is False:
																	UpperCAmelCase__					=							distances_from_source[vertex]
																	UpperCAmelCase__					=							vertex
									return min_index







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     int      ):
									UpperCAmelCase__					=							[int(1e7      )] * self.vertices  # distances from the source
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							[False] * self.vertices

									for _ in range(self.vertices      ):
													UpperCAmelCase__					=							self.minimum_distance(lowerCamelCase__   ,lowerCamelCase__      )
													UpperCAmelCase__					=							True

													# Update dist value of the adjacent vertices
													# of the picked vertex only if the current
													# distance is greater than new distance and
													# the vertex in not in the shortest path tree
													for v in range(self.vertices      ):
																	if (
																	    self.graph[u][v] > 0
																	    and visited[v] is False
																	    and distances[v] > distances[u] + self.graph[u][v]
																	):
																					UpperCAmelCase__					=							distances[u] + self.graph[u][v]

									self.print_solution(lowerCamelCase__      )


if __name__ == "__main__":
						lowerCAmelCase__  :  List[str]											=     Graph(9)
						lowerCAmelCase__  :  Tuple											=     [
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)



