


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


def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Dict=False			):






 """simple docstring"""







 __lowercase						=						[]
 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"
  __lowercase						=						[(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 lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			Optional[Any]=False			):






 """simple docstring"""







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


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			Dict					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						dct.pop(UpperCamelCase__			)
 __lowercase						=						val


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 __lowercase						=						Image.open(requests.get(UpperCamelCase__					, stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]					, UpperCamelCase__						:			List[Any]			):






 """simple docstring"""







 __lowercase						=						DeiTConfig()
 # all deit models have fine-tuned heads
 __lowercase						=						False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 __lowercase						=						1000
 __lowercase						=						"""huggingface/label-files"""
 __lowercase						=						"""imagenet-1k-id2label.json"""
 __lowercase						=						json.load(open(hf_hub_download(UpperCamelCase__					, UpperCamelCase__					, repo_type="""dataset"""			)					, """r"""			)			)
 __lowercase						=						{int(UpperCamelCase__			): v for k, v in idalabel.items()}
 __lowercase						=						idalabel
 __lowercase						=						{v: k for k, v in idalabel.items()}
 __lowercase						=						int(deit_name[-6:-4]			)
 __lowercase						=						int(deit_name[-3:]			)
 # size of the architecture
 if deit_name[9:].startswith("""tiny"""			):
  __lowercase						=						192
  __lowercase						=						768
  __lowercase						=						12
  __lowercase						=						3
 elif deit_name[9:].startswith("""small"""			):
  __lowercase						=						384
  __lowercase						=						1536
  __lowercase						=						12
  __lowercase						=						6
 if deit_name[9:].startswith("""base"""			):
  pass
 elif deit_name[4:].startswith("""large"""			):
  __lowercase						=						1024
  __lowercase						=						4096
  __lowercase						=						24
  __lowercase						=						16

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

 # load state_dict of original model, remove and rename some keys
 __lowercase						=						timm_model.state_dict()
 __lowercase						=						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
 __lowercase						=						DeiTForImageClassificationWithTeacher(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

 # Check outputs on an image, prepared by DeiTImageProcessor
 __lowercase						=						int(
     (256 / 224) * config.image_size			)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
 __lowercase						=						DeiTImageProcessor(size=UpperCamelCase__					, crop_size=config.image_size			)
 __lowercase						=						image_processor(images=prepare_img()					, return_tensors="""pt"""			)
 __lowercase						=						encoding["""pixel_values"""]
 __lowercase						=						model(UpperCamelCase__			)

 __lowercase						=						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__":
       UpperCAmelCase__					=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."
       )

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




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """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__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(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__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

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

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

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

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

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

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