


"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				=				logging.get_logger(__name__)
def 					A_      (      _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Any=False				):






     """simple docstring"""

     _a					= []
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'blocks.{i}.norm1.weight', f'deit.encoder.layer.{i}.layernorm_before.weight')				)
          rename_keys.append((f'blocks.{i}.norm1.bias', f'deit.encoder.layer.{i}.layernorm_before.bias')				)
          rename_keys.append((f'blocks.{i}.attn.proj.weight', f'deit.encoder.layer.{i}.attention.output.dense.weight')				)
          rename_keys.append((f'blocks.{i}.attn.proj.bias', f'deit.encoder.layer.{i}.attention.output.dense.bias')				)
          rename_keys.append((f'blocks.{i}.norm2.weight', f'deit.encoder.layer.{i}.layernorm_after.weight')				)
          rename_keys.append((f'blocks.{i}.norm2.bias', f'deit.encoder.layer.{i}.layernorm_after.bias')				)
          rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'deit.encoder.layer.{i}.intermediate.dense.weight')				)
          rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'deit.encoder.layer.{i}.intermediate.dense.bias')				)
          rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'deit.encoder.layer.{i}.output.dense.weight')				)
          rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'deit.encoder.layer.{i}.output.dense.bias')				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''cls_token''', '''deit.embeddings.cls_token'''),
             ('''dist_token''', '''deit.embeddings.distillation_token'''),
             ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
             ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
             ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
         ]				)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
                  ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                  ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
              ]				)

          # if just the base model, we should remove "deit" from all keys that start with "deit"
          _a					= [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''				) else pair for pair in rename_keys]
     else:
          # layernorm + classification heads
          rename_keys.extend(
              [
                  ('''norm.weight''', '''deit.layernorm.weight'''),
                  ('''norm.bias''', '''deit.layernorm.bias'''),
                  ('''head.weight''', '''cls_classifier.weight'''),
                  ('''head.bias''', '''cls_classifier.bias'''),
                  ('''head_dist.weight''', '''distillation_classifier.weight'''),
                  ('''head_dist.bias''', '''distillation_classifier.bias'''),
              ]				)

     return rename_keys
def 					A_      (      _lowerCAmelCase   :    List[Any],   _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Union[str, Any]=False				):






     """simple docstring"""

     for i in range(config.num_hidden_layers				):
          if base_model:
               _a					= """"""
          else:
               _a					= """deit."""
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          _a					= state_dict.pop(f'blocks.{i}.attn.qkv.weight'				)
          _a					= state_dict.pop(f'blocks.{i}.attn.qkv.bias'				)
          # next, add query, keys and values (in that order) to the state dict
          _a					= in_proj_weight[
              : config.hidden_size, :
          ]
          _a					= in_proj_bias[: config.hidden_size]
          _a					= in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _a					= in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _a					= in_proj_weight[
              -config.hidden_size :, :
          ]
          _a					= in_proj_bias[-config.hidden_size :]
def 					A_      (      _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Tuple				):






     """simple docstring"""

     _a					= dct.pop(UpperCamelCase__				)
     _a					= val
def 					A_      (      ):






     """simple docstring"""

     _a					= """http://images.cocodataset.org/val2017/000000039769.jpg"""
     _a					= Image.open(requests.get(UpperCamelCase__,   stream=UpperCamelCase__				).raw				)
     return im
@torch.no_grad()
def 					A_      (      _lowerCAmelCase   :    Optional[Any],   _lowerCAmelCase   :    int				):






     """simple docstring"""

     _a					= DeiTConfig()
     # all deit models have fine-tuned heads
     _a					= False
     # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
     _a					= 10_00
     _a					= """huggingface/label-files"""
     _a					= """imagenet-1k-id2label.json"""
     _a					= json.load(open(hf_hub_download(UpperCamelCase__,   UpperCamelCase__,   repo_type='''dataset'''				),   '''r'''				)				)
     _a					= {int(UpperCamelCase__				): v for k, v in idalabel.items()}
     _a					= idalabel
     _a					= {v: k for k, v in idalabel.items()}
     _a					= int(deit_name[-6:-4]				)
     _a					= int(deit_name[-3:]				)
     # size of the architecture
     if deit_name[9:].startswith('''tiny'''				):
          _a					= 1_92
          _a					= 7_68
          _a					= 12
          _a					= 3
     elif deit_name[9:].startswith('''small'''				):
          _a					= 3_84
          _a					= 15_36
          _a					= 12
          _a					= 6
     if deit_name[9:].startswith('''base'''				):
          pass
     elif deit_name[4:].startswith('''large'''				):
          _a					= 10_24
          _a					= 40_96
          _a					= 24
          _a					= 16

     # load original model from timm
     _a					= timm.create_model(UpperCamelCase__,   pretrained=UpperCamelCase__				)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     _a					= timm_model.state_dict()
     _a					= create_rename_keys(UpperCamelCase__,   UpperCamelCase__				)
     for src, dest in rename_keys:
          rename_key(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__				)
     read_in_q_k_v(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__				)

     # load HuggingFace model
     _a					= DeiTForImageClassificationWithTeacher(UpperCamelCase__				).eval()
     model.load_state_dict(UpperCamelCase__				)

     # Check outputs on an image, prepared by DeiTImageProcessor
     _a					= int(
         (2_56 / 2_24) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
     _a					= DeiTImageProcessor(size=UpperCamelCase__,   crop_size=config.image_size				)
     _a					= image_processor(images=prepare_img(),   return_tensors='''pt'''				)
     _a					= encoding["""pixel_values"""]
     _a					= model(UpperCamelCase__				)

     _a					= timm_model(UpperCamelCase__				)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(UpperCamelCase__,   outputs.logits,   atol=1e-3				)

     Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
     print(f'Saving model {deit_name} 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__":
   __snake_case				=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--deit_name''',
       default='''vit_deit_base_distilled_patch16_224''',
       type=str,
       help='''Name of the DeiT timm model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )

   __snake_case				=				parser.parse_args()
   convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[str]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
    if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
      return True
  return False

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
        _a  :       Any             =	distance[u] + w

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

 _snake_case          =   [{} for _ in range(E)]

 for i in range(E):
  print('Edge ', i + 1)
  _snake_case      , _snake_case      , _snake_case          =   (
      int(x)
      for x in input('Enter source, destination, weight: ').strip().split(' ')
  )
  _snake_case          =   {'src': src, 'dst': dest, 'weight': weight}

 _snake_case          =   int(input('\nEnter shortest path source:').strip())
 _snake_case          =   bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)

