

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_		(       UpperCamelCase_   :							Optional[Any]   ,					UpperCamelCase_   :							int=False   ,					UpperCamelCase_   :							Union[str, Any]=False  )							->   List[Any]:







 """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 a_		(       UpperCamelCase_   :							int   ,					UpperCamelCase_   :							Union[str, Any]   ,					UpperCamelCase_   :							List[str]=False   ,					UpperCamelCase_   :							Tuple=False  )							->   Union[str, 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 a_		(       UpperCamelCase_   :							Any   ,					UpperCamelCase_   :							str   ,					UpperCamelCase_   :							str  )							->   List[Any]:







 """simple docstring"""

 lowerCamelCase      						=    dct.pop(UpperCamelCase_  )
 lowerCamelCase      						=    val







def a_		(       )							->   List[str]:







 """simple docstring"""

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







@torch.no_grad()
def a_		(       UpperCamelCase_   :							Optional[int]   ,					UpperCamelCase_   :							List[Any]  )							->   Optional[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      						=    2_1_8_4_1
  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[9_2_0_5]
  del idalabel[1_5_0_2_7]
  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      						=    1_0_0_0
  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      						=    3_8_4
  if "512" in checkpoint_url:
   lowerCamelCase      						=    5_1_2
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  lowerCamelCase      						=    True
  lowerCamelCase      						=    1_5_0
  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      						=    6_4_0
  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      						=    1_0_2_4
  lowerCamelCase      						=    4_0_9_6
  lowerCamelCase      						=    2_4
  lowerCamelCase      						=    1_6
  if "ade20k" in checkpoint_url:
   lowerCamelCase      						=    6_4_0
   lowerCamelCase      						=    [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
 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, 1_0_0_0]  )
 if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'  ):
  lowerCamelCase      						=    torch.Size([1, 1_9_6, 8_1_9_2]  )
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'  ):
  lowerCamelCase      						=    torch.Size([1, 1_9_6, 8_1_9_2]  )
 elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'  ):
  lowerCamelCase      						=    torch.Size([1, 2_1_8_4_1]  )
  lowerCamelCase      						=    torch.tensor([2.2_288, 2.4_671, 0.7_395]  )
  lowerCamelCase      						=    2_3_9_7
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'  ):
  lowerCamelCase      						=    torch.Size([1, 2_1_8_4_1]  )
  lowerCamelCase      						=    torch.tensor([1.6_881, -0.2_787, 0.5_901]  )
  lowerCamelCase      						=    2_3_9_6
 elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'  ):
  lowerCamelCase      						=    torch.tensor([0.1_241, 0.0_798, -0.6_569]  )
  lowerCamelCase      						=    2_8_5
 elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'  ):
  lowerCamelCase      						=    torch.tensor([-1.2_385, -1.0_987, -1.0_108]  )
  lowerCamelCase      						=    2_8_1
 elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'  ):
  lowerCamelCase      						=    torch.tensor([-1.5_303, -0.9_484, -0.3_147]  )
  lowerCamelCase      						=    7_6_1
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'  ):
  lowerCamelCase      						=    torch.tensor([0.4_610, -0.0_928, 0.2_086]  )
  lowerCamelCase      						=    7_6_1
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'  ):
  lowerCamelCase      						=    torch.tensor([-0.4_804, 0.6_257, -0.1_837]  )
  lowerCamelCase      						=    7_6_1
 elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'  ):
  lowerCamelCase      						=    torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]  )
  lowerCamelCase      						=    7_6_1
 elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'  ):
  lowerCamelCase      						=    torch.tensor([-0.3_062, 0.7_261, 0.4_852]  )
  lowerCamelCase      						=    7_6_1
 elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'  ):
  lowerCamelCase      						=    (1, 1_5_0, 1_6_0, 1_6_0)
  lowerCamelCase      						=    torch.tensor(
      [
          [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
          [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
          [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
      ]  )
 elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'  ):
  lowerCamelCase      						=    (1, 1_5_0, 1_6_0, 1_6_0)
  lowerCamelCase      						=    torch.tensor(
      [
          [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
          [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
          [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
      ]  )
 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__":
     _lowerCAmelCase :	Any     				=    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 :	List[Any]     				=    parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







from __future__ import annotations


class     lowerCAmelCase				:


       '''simple docstring'''







       def __init__(	self					:  Dict						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    vertices
        lowerCamelCase      						=    [[0] * vertices for _ in range(__snake_case     )]

       def   lowerCamelCase__						(	self					:  List[str]						,	__snake_case					:  list[int]     )   -> None:







        '''simple docstring'''

        print('Vertex \t Distance from Source'     )
        for vertex in range(self.vertices     ):
         print(__snake_case						,	'\t\t'						,	distances_from_source[vertex]     )

       def   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  list[int]						,	__snake_case					:  list[bool]     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    1e7
        lowerCamelCase      						=    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:
          lowerCamelCase      						=    distances_from_source[vertex]
          lowerCamelCase      						=    vertex
        return min_index





       def   lowerCamelCase__						(	self					:  Optional[Any]						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    [int(1e7     )] * self.vertices  # distances from the source
        lowerCamelCase      						=    0
        lowerCamelCase      						=    [False] * self.vertices

        for _ in range(self.vertices     ):
         lowerCamelCase      						=    self.minimum_distance(__snake_case						,	__snake_case     )
         lowerCamelCase      						=    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]
          ):
           lowerCamelCase      						=    distances[u] + self.graph[u][v]

        self.print_solution(__snake_case     )


if __name__ == "__main__":
     _lowerCAmelCase :	Dict     				=    Graph(9)
     _lowerCAmelCase :	int     				=    [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)




