
import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__      :		List[Any]       							=  logging.get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""backbone.""" if is_semantic else """"""

   _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"{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"""),
           (F"{prefix}pos_embed", """beit.embeddings.position_embeddings"""),
       ]						)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", """beit.embeddings.mask_token"""),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Optional[int]:




   """simple docstring"""







   for i in range(config.num_hidden_layers						):
      _SCREAMING_SNAKE_CASE      =				"""backbone.""" if is_semantic else """"""
      # queries, keys and values
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"						)
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"						)
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"						)

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

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

      _SCREAMING_SNAKE_CASE      =				gamma_a
      _SCREAMING_SNAKE_CASE      =				gamma_a
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				dct.pop(__snake_case						)
   _SCREAMING_SNAKE_CASE      =				val
def    lowerCAmelCase_	(				)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _SCREAMING_SNAKE_CASE      =				Image.open(requests.get(__snake_case    ,      stream=__snake_case						).raw						)
   return im
@torch.no_grad()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				False if """rvlcdip""" in checkpoint_url else True
   _SCREAMING_SNAKE_CASE      =				BeitConfig(use_absolute_position_embeddings=__snake_case    ,      use_mask_token=__snake_case						)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				16

   # labels
   if "rvlcdip" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				"""huggingface/label-files"""
      _SCREAMING_SNAKE_CASE      =				"""rvlcdip-id2label.json"""
      _SCREAMING_SNAKE_CASE      =				json.load(open(hf_hub_download(__snake_case    ,      __snake_case    ,      repo_type="""dataset"""						)    ,      """r"""						)						)
      _SCREAMING_SNAKE_CASE      =				{int(__snake_case						): v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE      =				idalabel
      _SCREAMING_SNAKE_CASE      =				{v: k for k, v in idalabel.items()}

   # load state_dict of original model, remove and rename some keys
   _SCREAMING_SNAKE_CASE      =				torch.hub.load_state_dict_from_url(__snake_case    ,      map_location="""cpu"""						)["""model"""]

   _SCREAMING_SNAKE_CASE      =				create_rename_keys(__snake_case    ,      has_lm_head=__snake_case						)
   for src, dest in rename_keys:
      rename_key(__snake_case    ,      __snake_case    ,      __snake_case						)
   read_in_q_k_v(__snake_case    ,      __snake_case    ,      has_lm_head=__snake_case						)

   # load HuggingFace model
   _SCREAMING_SNAKE_CASE      =				BeitForMaskedImageModeling(__snake_case						) if has_lm_head else BeitForImageClassification(__snake_case						)
   model.eval()
   model.load_state_dict(__snake_case						)

   # Check outputs on an image
   _SCREAMING_SNAKE_CASE      =				BeitImageProcessor(
       size=config.image_size    ,      resample=PILImageResampling.BILINEAR    ,      do_center_crop=__snake_case						)
   _SCREAMING_SNAKE_CASE      =				prepare_img()

   _SCREAMING_SNAKE_CASE      =				image_processor(images=__snake_case    ,      return_tensors="""pt"""						)
   _SCREAMING_SNAKE_CASE      =				encoding["""pixel_values"""]

   _SCREAMING_SNAKE_CASE      =				model(__snake_case						)
   _SCREAMING_SNAKE_CASE      =				outputs.logits

   # verify logits
   _SCREAMING_SNAKE_CASE      =				[1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(__snake_case						), "Shape of logits not as expected"

   Path(__snake_case						).mkdir(exist_ok=__snake_case						)
   print(F"Saving model to {pytorch_dump_folder_path}"						)
   model.save_pretrained(__snake_case						)
   print(F"Saving image processor to {pytorch_dump_folder_path}"						)
   image_processor.save_pretrained(__snake_case						)

   if push_to_hub:
      if has_lm_head:
         _SCREAMING_SNAKE_CASE      =				"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         _SCREAMING_SNAKE_CASE      =				"""dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(__snake_case    ,      __snake_case						)    ,      organization="""nielsr"""    ,      commit_message="""Add image processor"""    ,      use_temp_dir=__snake_case    ,      )
      model.push_to_hub(
          repo_path_or_name=Path(__snake_case    ,      __snake_case						)    ,      organization="""nielsr"""    ,      commit_message="""Add model"""    ,      use_temp_dir=__snake_case    ,      )


if __name__ == "__main__":
     UpperCamelCase__      :		str       							=  argparse.ArgumentParser()

     parser.add_argument(
         "--checkpoint_url",
         default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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."
     )
     parser.add_argument(
         "--push_to_hub",
         action="store_true",
     )
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()
     convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''



from __future__ import annotations
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   print(F"Vertex\tShortest Distance from vertex {src}"						)
   for i, d in enumerate(SCREAMING_SNAKE_CASE_						):
      print(F"{i}\t\t{d}"						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   for j in range(SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				(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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     list[float]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[float("""inf"""						)] * vertex_count
   _SCREAMING_SNAKE_CASE      =				0.0

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

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

   _SCREAMING_SNAKE_CASE      =				check_negative_cycle(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   if negative_cycle_exists:
      raise Exception("""Negative cycle found"""						)

   return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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

     UpperCamelCase__      :		list[dict[str, int]]       							=  [{} for _ in range(E)]

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

     UpperCamelCase__      :		Optional[Any]       							=  int(input("\nEnter shortest path source:").strip())
     UpperCamelCase__      :		Any       							=  bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)






