






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






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






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
   SCREAMING_SNAKE_CASE						= dct.pop(_UpperCAmelCase)
   SCREAMING_SNAKE_CASE						= val






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






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

   # load original model from timm
   SCREAMING_SNAKE_CASE						= timm.create_model(_UpperCAmelCase							,       pretrained=_UpperCAmelCase)
   timm_model.eval()

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

   # Check outputs on an image, prepared by DeiTImageProcessor
   SCREAMING_SNAKE_CASE						= 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
   SCREAMING_SNAKE_CASE						= DeiTImageProcessor(size=_UpperCAmelCase							,       crop_size=config.image_size)
   SCREAMING_SNAKE_CASE						= image_processor(images=prepare_img()							,       return_tensors='pt')
   SCREAMING_SNAKE_CASE						= encoding['pixel_values']
   SCREAMING_SNAKE_CASE						= model(_UpperCAmelCase)

   SCREAMING_SNAKE_CASE						= 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__":
    a_		:						Union[str, Any]             =    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.'
    )

    a_		:						int             =    parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












from __future__ import annotations

import time

a_		:						Tuple             =    list[tuple[int, int]]

a_		:						Optional[int]             =    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a_		:						Dict             =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= pos_x
						SCREAMING_SNAKE_CASE						= pos_y
						SCREAMING_SNAKE_CASE						= (pos_y, pos_x)
						SCREAMING_SNAKE_CASE						= goal_x
						SCREAMING_SNAKE_CASE						= goal_y
						SCREAMING_SNAKE_CASE						= parent



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= Node(start[1]		,					start[0]		,					goal[1]		,					goal[0]		,					a)
						SCREAMING_SNAKE_CASE						= Node(goal[1]		,					goal[0]		,					goal[1]		,					goal[0]		,					a)

						SCREAMING_SNAKE_CASE						= [self.start]
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.node_queue:
									SCREAMING_SNAKE_CASE						= self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_path(a)

									SCREAMING_SNAKE_CASE						= self.get_successors(a)

									for node in successors:
												self.node_queue.append(a)

						if not self.reached:
									return [self.start.pos]
						return None






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       list[Node]:
						SCREAMING_SNAKE_CASE						= []
						for action in delta:
									SCREAMING_SNAKE_CASE						= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE						= parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(a) - 1):
												continue

									if grid[pos_y][pos_x] != 0:
												continue

									successors.append(
									    Node(a		,					a		,					self.target.pos_y		,					self.target.pos_x		,					a))
						return successors







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= node
						SCREAMING_SNAKE_CASE						= []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									SCREAMING_SNAKE_CASE						= current_node.parent
						path.reverse()
						return path



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									SCREAMING_SNAKE_CASE						= self.fwd_bfs.node_queue.pop(0)
									SCREAMING_SNAKE_CASE						= self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_bidirectional_path(
												    a		,					a)

									SCREAMING_SNAKE_CASE						= current_bwd_node
									SCREAMING_SNAKE_CASE						= current_fwd_node

									SCREAMING_SNAKE_CASE						= {
									    self.fwd_bfs: self.fwd_bfs.get_successors(a),
									    self.bwd_bfs: self.bwd_bfs.get_successors(a),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
												for node in successors[bfs]:
															bfs.node_queue.append(a)

						if not self.reached:
									return [self.fwd_bfs.start.pos]
						return None







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= self.fwd_bfs.retrace_path(a)
						SCREAMING_SNAKE_CASE						= self.bwd_bfs.retrace_path(a)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE						= fwd_path + bwd_path
						return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				import doctest

				doctest.testmod()
				a_		:						Union[str, Any]             =    (0, 0)
				a_		:						Any             =    (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				a_		:						Optional[Any]             =    time.time()
				a_		:						Union[str, Any]             =    BreadthFirstSearch(init, goal)
				a_		:						Optional[Any]             =    bfs.search()
				a_		:						List[str]             =    time.time() - start_bfs_time

				print('Unidirectional BFS computation time : ', bfs_time)

				a_		:						Optional[int]             =    time.time()
				a_		:						Union[str, Any]             =    BidirectionalBreadthFirstSearch(init, goal)
				a_		:						List[Any]             =    bd_bfs.search()
				a_		:						List[str]             =    time.time() - start_bd_bfs_time

				print('Bidirectional BFS computation time : ', bd_bfs_time)




