"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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






def 			__UpperCAmelCase    (	lowercase      ,lowercase=False						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  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'''vit.encoder.layer.{i}.layernorm_before.weight''')						)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')						)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')						)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')						)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')						)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')						)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')						)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')						)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')						)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')						)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""cls_token""", """vit.embeddings.cls_token"""),
          ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""pos_embed""", """vit.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 "vit" from all keys that start with "vit"
    _UpperCAmelCase         =					[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""						) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""norm.weight""", """vit.layernorm.weight"""),
            ("""norm.bias""", """vit.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ]						)

  return rename_keys






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase=False						):







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






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(lowercase      ,lowercase						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					dct.pop(lowercase						)
  _UpperCAmelCase         =					val






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase         =					Image.open(requests.get(lowercase      ,stream=lowercase						).raw						)
  return im






@torch.no_grad()
def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					ViTConfig()
  _UpperCAmelCase         =					False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					int(vit_name[-12:-10]						)
    _UpperCAmelCase         =					int(vit_name[-9:-6]						)
  else:
    _UpperCAmelCase         =					10_00
    _UpperCAmelCase         =					"""huggingface/label-files"""
    _UpperCAmelCase         =					"""imagenet-1k-id2label.json"""
    _UpperCAmelCase         =					json.load(open(hf_hub_download(lowercase      ,lowercase      ,repo_type="""dataset"""						)      ,"""r"""						)						)
    _UpperCAmelCase         =					{int(lowercase						): v for k, v in idalabel.items()}
    _UpperCAmelCase         =					idalabel
    _UpperCAmelCase         =					{v: k for k, v in idalabel.items()}
    _UpperCAmelCase         =					int(vit_name[-6:-4]						)
    _UpperCAmelCase         =					int(vit_name[-3:]						)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("""tiny"""						):
      _UpperCAmelCase         =					1_92
      _UpperCAmelCase         =					7_68
      _UpperCAmelCase         =					12
      _UpperCAmelCase         =					3
    elif vit_name[9:].startswith("""small"""						):
      _UpperCAmelCase         =					3_84
      _UpperCAmelCase         =					15_36
      _UpperCAmelCase         =					12
      _UpperCAmelCase         =					6
    else:
      pass
  else:
    if vit_name[4:].startswith("""small"""						):
      _UpperCAmelCase         =					7_68
      _UpperCAmelCase         =					23_04
      _UpperCAmelCase         =					8
      _UpperCAmelCase         =					8
    elif vit_name[4:].startswith("""base"""						):
      pass
    elif vit_name[4:].startswith("""large"""						):
      _UpperCAmelCase         =					10_24
      _UpperCAmelCase         =					40_96
      _UpperCAmelCase         =					24
      _UpperCAmelCase         =					16
    elif vit_name[4:].startswith("""huge"""						):
      _UpperCAmelCase         =					12_80
      _UpperCAmelCase         =					51_20
      _UpperCAmelCase         =					32
      _UpperCAmelCase         =					16

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

  # load state_dict of original model, remove and rename some keys
  _UpperCAmelCase         =					timm_model.state_dict()
  if base_model:
    remove_classification_head_(lowercase						)
  _UpperCAmelCase         =					create_rename_keys(lowercase      ,lowercase						)
  for src, dest in rename_keys:
    rename_key(lowercase      ,lowercase      ,lowercase						)
  read_in_q_k_v(lowercase      ,lowercase      ,lowercase						)

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    _UpperCAmelCase         =					ViTModel(lowercase						).eval()
  else:
    _UpperCAmelCase         =					ViTForImageClassification(lowercase						).eval()
  model.load_state_dict(lowercase						)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    _UpperCAmelCase         =					DeiTImageProcessor(size=config.image_size						)
  else:
    _UpperCAmelCase         =					ViTImageProcessor(size=config.image_size						)
  _UpperCAmelCase         =					image_processor(images=prepare_img()      ,return_tensors="""pt"""						)
  _UpperCAmelCase         =					encoding["""pixel_values"""]
  _UpperCAmelCase         =					model(lowercase						)

  if base_model:
    _UpperCAmelCase         =					timm_model.forward_features(lowercase						)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(lowercase      ,outputs.pooler_output      ,atol=1E-3						)
  else:
    _UpperCAmelCase         =					timm_model(lowercase						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(lowercase      ,outputs.logits      ,atol=1E-3						)

  Path(lowercase						).mkdir(exist_ok=lowercase						)
  print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''						)
  model.save_pretrained(lowercase						)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
  image_processor.save_pretrained(lowercase						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--vit_name""",
        default="""vit_base_patch16_224""",
        type=str,
        help="""Name of the ViT 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_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

"""simple docstring"""


UpperCAmelCase__     						=  [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Return True if there is node that has not iterated.
  _UpperCAmelCase         =					[False] * len(lowercase						)
  _UpperCAmelCase         =					[s]
  _UpperCAmelCase         =					True

  while queue:
    _UpperCAmelCase         =					queue.pop(0						)
    for ind in range(len(graph[u]						)						):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase						)
        _UpperCAmelCase         =					True
        _UpperCAmelCase         =					u

  return visited[t]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[-1] * (len(lowercase						))
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase      ,lowercase      ,lowercase      ,lowercase						):
    _UpperCAmelCase         =					float("""Inf"""						)
    _UpperCAmelCase         =					sink

    while s != source:
      # Find the minimum value in select path
      _UpperCAmelCase         =					min(lowercase      ,graph[parent[s]][s]						)
      _UpperCAmelCase         =					parent[s]

    max_flow += path_flow
    _UpperCAmelCase         =					sink

    while v != source:
      _UpperCAmelCase         =					parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _UpperCAmelCase         =					parent[v]

  for i in range(len(lowercase						)						):
    for j in range(len(graph[0]						)						):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)						)

  return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))
