




"""simple docstring"""

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 (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


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






def 		_snake_case	(   _snake_case				:   List[Any]							,    _snake_case				:   Dict=False							,    _snake_case				:   Optional[int]=False							,    _snake_case				:   str=False				)       ->   Optional[Any]:






      '''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'''transformer.blocks.{i}.norm1.weight''', F'''vilt.encoder.layer.{i}.layernorm_before.weight''')				)
            rename_keys.append((F'''transformer.blocks.{i}.norm1.bias''', F'''vilt.encoder.layer.{i}.layernorm_before.bias''')				)
            rename_keys.append(
                (F'''transformer.blocks.{i}.attn.proj.weight''', F'''vilt.encoder.layer.{i}.attention.output.dense.weight''')				)
            rename_keys.append(
                (F'''transformer.blocks.{i}.attn.proj.bias''', F'''vilt.encoder.layer.{i}.attention.output.dense.bias''')				)
            rename_keys.append((F'''transformer.blocks.{i}.norm2.weight''', F'''vilt.encoder.layer.{i}.layernorm_after.weight''')				)
            rename_keys.append((F'''transformer.blocks.{i}.norm2.bias''', F'''vilt.encoder.layer.{i}.layernorm_after.bias''')				)
            rename_keys.append(
                (F'''transformer.blocks.{i}.mlp.fc1.weight''', F'''vilt.encoder.layer.{i}.intermediate.dense.weight''')				)
            rename_keys.append((F'''transformer.blocks.{i}.mlp.fc1.bias''', F'''vilt.encoder.layer.{i}.intermediate.dense.bias''')				)
            rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.weight''', F'''vilt.encoder.layer.{i}.output.dense.weight''')				)
            rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.bias''', F'''vilt.encoder.layer.{i}.output.dense.bias''')				)

      # embeddings
      rename_keys.extend(
          [
              # text embeddings
              ('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
              (
                  'text_embeddings.position_embeddings.weight',
                  'vilt.embeddings.text_embeddings.position_embeddings.weight',
              ),
              ('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
              (
                  'text_embeddings.token_type_embeddings.weight',
                  'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
              ),
              ('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
              ('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
              # patch embeddings
              ('transformer.cls_token', 'vilt.embeddings.cls_token'),
              ('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
              ('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
              ('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
              # token type embeddings
              ('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
          ]				)

      # final layernorm + pooler
      rename_keys.extend(
          [
              ('transformer.norm.weight', 'vilt.layernorm.weight'),
              ('transformer.norm.bias', 'vilt.layernorm.bias'),
              ('pooler.dense.weight', 'vilt.pooler.dense.weight'),
              ('pooler.dense.bias', 'vilt.pooler.dense.bias'),
          ]				)

      # classifier head(s)
      if vqa_model:
            # classification head
            rename_keys.extend(
                [
                    ('vqa_classifier.0.weight', 'classifier.0.weight'),
                    ('vqa_classifier.0.bias', 'classifier.0.bias'),
                    ('vqa_classifier.1.weight', 'classifier.1.weight'),
                    ('vqa_classifier.1.bias', 'classifier.1.bias'),
                    ('vqa_classifier.3.weight', 'classifier.3.weight'),
                    ('vqa_classifier.3.bias', 'classifier.3.bias'),
                ]				)
      elif nlvr_model:
            # classification head
            rename_keys.extend(
                [
                    ('nlvr2_classifier.0.weight', 'classifier.0.weight'),
                    ('nlvr2_classifier.0.bias', 'classifier.0.bias'),
                    ('nlvr2_classifier.1.weight', 'classifier.1.weight'),
                    ('nlvr2_classifier.1.bias', 'classifier.1.bias'),
                    ('nlvr2_classifier.3.weight', 'classifier.3.weight'),
                    ('nlvr2_classifier.3.bias', 'classifier.3.bias'),
                ]				)
      else:
            pass

      return rename_keys






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int				)       ->   Any:






      '''simple docstring'''


      for i in range(config.num_hidden_layers				):
            _A     =  'vilt.'
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _A     =  state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight'''				)
            _A     =  state_dict.pop(F'''transformer.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 		_snake_case	(   _snake_case				:   Tuple				)       ->   str:






      '''simple docstring'''


      _A     =  ['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(_snake_case							,    _snake_case				)






def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   str							,    _snake_case				:   Tuple				)       ->   str:






      '''simple docstring'''


      _A     =  dct.pop(_snake_case				)
      _A     =  val






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   Dict				)       ->   List[str]:






      '''simple docstring'''


      _A     =  ViltConfig(image_size=3_84							,    patch_size=32							,    tie_word_embeddings=_snake_case				)
      _A     =  False
      _A     =  False
      _A     =  False
      _A     =  False
      if "vqa" in checkpoint_url:
            _A     =  True
            _A     =  31_29
            _A     =  'huggingface/label-files'
            _A     =  'vqa2-id2label.json'
            _A     =  json.load(open(hf_hub_download(_snake_case							,    _snake_case							,    repo_type='dataset'				)							,    'r'				)				)
            _A     =  {int(_snake_case				): v for k, v in idalabel.items()}
            _A     =  idalabel
            _A     =  {v: k for k, v in idalabel.items()}
            _A     =  ViltForQuestionAnswering(_snake_case				)
      elif "nlvr" in checkpoint_url:
            _A     =  True
            _A     =  2
            _A     =  {0: 'False', 1: 'True'}
            _A     =  {v: k for k, v in config.idalabel.items()}
            _A     =  3
            _A     =  ViltForImagesAndTextClassification(_snake_case				)
      elif "irtr" in checkpoint_url:
            _A     =  True
            _A     =  ViltForImageAndTextRetrieval(_snake_case				)
      elif "mlm_itm" in checkpoint_url:
            _A     =  True
            _A     =  ViltForMaskedLM(_snake_case				)
      else:
            raise ValueError('Unknown model type'				)

      # load state_dict of original model, remove and rename some keys
      _A     =  torch.hub.load_state_dict_from_url(_snake_case							,    map_location='cpu'				)['state_dict']
      _A     =  create_rename_keys(_snake_case							,    _snake_case							,    _snake_case							,    _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				)
      if mlm_model or irtr_model:
            _A     =  ['itm_score.fc.weight', 'itm_score.fc.bias']
            for k in ignore_keys:
                  state_dict.pop(_snake_case							,    _snake_case				)

    # load state dict into HuggingFace model
      model.eval()
      if mlm_model:
            _A  ,       _A     =  model.load_state_dict(_snake_case							,    strict=_snake_case				)
            assert missing_keys == ["mlm_score.decoder.bias"]
      else:
            model.load_state_dict(_snake_case				)

      # Define processor
      _A     =  ViltImageProcessor(size=3_84				)
      _A     =  BertTokenizer.from_pretrained('bert-base-uncased'				)
      _A     =  ViltProcessor(_snake_case							,    _snake_case				)

      # Forward pass on example inputs (image + text)
      if nlvr_model:
            _A     =  Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'							,    stream=_snake_case				).raw				)
            _A     =  Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'							,    stream=_snake_case				).raw				)
            _A     =  (
                'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
                ' standing.'
            )
            _A     =  processor(_snake_case							,    _snake_case							,    return_tensors='pt'				)
            _A     =  processor(_snake_case							,    _snake_case							,    return_tensors='pt'				)
            _A     =  model(
                input_ids=encoding_a.input_ids							,    pixel_values=encoding_a.pixel_values							,    pixel_values_a=encoding_a.pixel_values							,    )
      else:
            _A     =  Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'							,    stream=_snake_case				).raw				)
            if mlm_model:
                  _A     =  'a bunch of [MASK] laying on a [MASK].'
            else:
                  _A     =  'How many cats are there?'
            _A     =  processor(_snake_case							,    _snake_case							,    return_tensors='pt'				)
            _A     =  model(**_snake_case				)

      # Verify outputs
      if mlm_model:
            _A     =  torch.Size([1, 11, 3_05_22]				)
            _A     =  torch.tensor([-12.5061, -12.5123, -12.5174]				)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]							,    _snake_case							,    atol=1E-4				)

            # verify masked token prediction equals "cats"
            _A     =  outputs.logits[0, 4, :].argmax(-1				).item()
            assert tokenizer.decode([predicted_id]				) == "cats"
      elif vqa_model:
            _A     =  torch.Size([1, 31_29]				)
            _A     =  torch.tensor([-15.9495, -18.1472, -10.3041]				)
            assert torch.allclose(outputs.logits[0, :3]							,    _snake_case							,    atol=1E-4				)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]							,    _snake_case							,    atol=1E-4				)

            # verify vqa prediction equals "2"
            _A     =  outputs.logits.argmax(-1				).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            _A     =  torch.Size([1, 2]				)
            _A     =  torch.tensor([-2.8721, 2.1291]				)
            assert torch.allclose(outputs.logits[0, :3]							,    _snake_case							,    atol=1E-4				)
            assert outputs.logits.shape == expected_shape

      Path(_snake_case				).mkdir(exist_ok=_snake_case				)
      print(F'''Saving model and processor to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(_snake_case				)
      processor.save_pretrained(_snake_case				)


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
           type=str,
           help='''URL of the checkpoint 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							      =     parser.parse_args()
       convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""

class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       _A     =  None
       _A     =  graph

       self._normalize_graph(_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  len(_UpperCAmelCase						)
       _A     =  None

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Dict						):
       if sources is int:
             _A     =  [sources]
       if sinks is int:
             _A     =  [sinks]

       if len(_UpperCAmelCase						) == 0 or len(_UpperCAmelCase						) == 0:
             return

       _A     =  sources[0]
       _A     =  sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(_UpperCAmelCase						) > 1 or len(_UpperCAmelCase						) > 1:
             _A     =  0
             for i in sources:
                   max_input_flow += sum(self.graph[i]						)

             _A     =  len(self.graph						) + 1
             for room in self.graph:
                   room.insert(0       ,				0						)
             self.graph.insert(0       ,				[0] * size						)
             for i in sources:
                   _A     =  max_input_flow
             _A     =  0

             _A     =  len(self.graph						) + 1
             for room in self.graph:
                   room.append(0						)
             self.graph.append([0] * size						)
             for i in sinks:
                   _A     =  max_input_flow
             _A     =  size - 1

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if self.maximum_flow_algorithm is None:
             raise Exception('You need to set maximum flow algorithm before.'						)
       if self.source_index is None or self.sink_index is None:
             return 0

       self.maximum_flow_algorithm.execute()
       return self.maximum_flow_algorithm.getMaximumFlow()



 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  algorithm(self						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  flow_network
       _A     =  flow_network.verticesCount
       _A     =  flow_network.sourceIndex
       _A     =  flow_network.sinkIndex
       # it's just a reference, so you shouldn't change
       # it in your algorithms, use deep copy before doing that
       _A     =  flow_network.graph
       _A     =  False

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             self._algorithm()
             _A     =  True



 def      lowerCAmelCase_				(					self			:			int						):
       pass





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       super().__init__(_UpperCAmelCase						)
       # use this to save your result
       _A     =  -1



 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             raise Exception('You should execute algorithm before using its result!'						)

       return self.maximum_flow





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]						):
       super().__init__(_UpperCAmelCase						)

       _A     =  [[0] * self.verticies_count for i in range(self.verticies_count						)]

       _A     =  [0] * self.verticies_count
       _A     =  [0] * self.verticies_count

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.verticies_count

       # push some substance to graph
       for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]						):
             self.preflow[self.source_index][nextvertex_index] += bandwidth
             self.preflow[nextvertex_index][self.source_index] -= bandwidth
             self.excesses[nextvertex_index] += bandwidth

       # Relabel-to-front selection rule
       _A     =  [
           i
           for i in range(self.verticies_count						)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       _A     =  0
       while i < len(_UpperCAmelCase						):
             _A     =  vertices_list[i]
             _A     =  self.heights[vertex_index]
             self.process_vertex(_UpperCAmelCase						)
             if self.heights[vertex_index] > previous_height:
                   # if it was relabeled, swap elements
                   # and start from 0 index
                   vertices_list.insert(0       ,				vertices_list.pop(_UpperCAmelCase						)						)
                   _A     =  0
             else:
                   i += 1

       _A     =  sum(self.preflow[self.source_index]						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       while self.excesses[vertex_index] > 0:
             for neighbour_index in range(self.verticies_count						):
                   # if it's neighbour and current vertex is higher
                   if (
                       self.graph[vertex_index][neighbour_index]
                       - self.preflow[vertex_index][neighbour_index]
                       > 0
                       and self.heights[vertex_index] > self.heights[neighbour_index]
                   ):
                         self.push(_UpperCAmelCase       ,				_UpperCAmelCase						)

             self.relabel(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple						):
       _A     =  min(
           self.excesses[from_index]       ,				self.graph[from_index][to_index] - self.preflow[from_index][to_index]       ,				)
       self.preflow[from_index][to_index] += preflow_delta
       self.preflow[to_index][from_index] -= preflow_delta
       self.excesses[from_index] -= preflow_delta
       self.excesses[to_index] += preflow_delta



 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       for to_index in range(self.verticies_count						):
             if (
                 self.graph[vertex_index][to_index]
                 - self.preflow[vertex_index][to_index]
                 > 0
             ) and (min_height is None or self.heights[to_index] < min_height):
                   _A     =  self.heights[to_index]

       if min_height is not None:
             _A     =  min_height + 1


if __name__ == "__main__":
       a							      =     [0]
       a							      =     [3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       a							      =     [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       a							      =     FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       a							      =     flow_network.find_maximum_flow()

       print(F'''maximum flow is {maximum_flow}''')

