'''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()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False ,						__UpperCAmelCase=False ,						__UpperCAmelCase=False		)						->    Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      for i in range(config.num_hidden_layers		):
            _lowerCamelCase			    =				'''vilt.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowerCamelCase			    =				state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'transformer.blocks.{i}.attn.qkv.bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[
                : config.hidden_size, :
            ]
            _lowerCamelCase			    =				in_proj_bias[: config.hidden_size]
            _lowerCamelCase			    =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase			    =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase			    =				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase			    =				in_proj_bias[-config.hidden_size :]
def       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				ViltConfig(image_size=384 ,						patch_size=32 ,						tie_word_embeddings=__UpperCAmelCase		)
      _lowerCamelCase			    =				False
      _lowerCamelCase			    =				False
      _lowerCamelCase			    =				False
      _lowerCamelCase			    =				False
      if "vqa" in checkpoint_url:
            _lowerCamelCase			    =				True
            _lowerCamelCase			    =				3129
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''vqa2-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}
            _lowerCamelCase			    =				ViltForQuestionAnswering(__UpperCAmelCase		)
      elif "nlvr" in checkpoint_url:
            _lowerCamelCase			    =				True
            _lowerCamelCase			    =				2
            _lowerCamelCase			    =				{0: '''False''', 1: '''True'''}
            _lowerCamelCase			    =				{v: k for k, v in config.idalabel.items()}
            _lowerCamelCase			    =				3
            _lowerCamelCase			    =				ViltForImagesAndTextClassification(__UpperCAmelCase		)
      elif "irtr" in checkpoint_url:
            _lowerCamelCase			    =				True
            _lowerCamelCase			    =				ViltForImageAndTextRetrieval(__UpperCAmelCase		)
      elif "mlm_itm" in checkpoint_url:
            _lowerCamelCase			    =				True
            _lowerCamelCase			    =				ViltForMaskedLM(__UpperCAmelCase		)
      else:
            raise ValueError('''Unknown model type'''		)

      # load state_dict of original model, remove and rename some keys
      _lowerCamelCase			    =				torch.hub.load_state_dict_from_url(__UpperCAmelCase ,						map_location='''cpu'''		)['''state_dict''']
      _lowerCamelCase			    =				create_rename_keys(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      read_in_q_k_v(__UpperCAmelCase ,						__UpperCAmelCase		)
      if mlm_model or irtr_model:
            _lowerCamelCase			    =				['''itm_score.fc.weight''', '''itm_score.fc.bias''']
            for k in ignore_keys:
                  state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)

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

      # Define processor
      _lowerCamelCase			    =				ViltImageProcessor(size=384		)
      _lowerCamelCase			    =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)
      _lowerCamelCase			    =				ViltProcessor(__UpperCAmelCase ,						__UpperCAmelCase		)

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

      # Verify outputs
      if mlm_model:
            _lowerCamelCase			    =				torch.Size([1, 11, 3_0522]		)
            _lowerCamelCase			    =				torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]		)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3] ,						__UpperCAmelCase ,						atol=1E-4		)

            # verify masked token prediction equals "cats"
            _lowerCamelCase			    =				outputs.logits[0, 4, :].argmax(-1		).item()
            assert tokenizer.decode([predicted_id]		) == "cats"
      elif vqa_model:
            _lowerCamelCase			    =				torch.Size([1, 3129]		)
            _lowerCamelCase			    =				torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]		)
            assert torch.allclose(outputs.logits[0, :3] ,						__UpperCAmelCase ,						atol=1E-4		)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3] ,						__UpperCAmelCase ,						atol=1E-4		)

            # verify vqa prediction equals "2"
            _lowerCamelCase			    =				outputs.logits.argmax(-1		).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            _lowerCamelCase			    =				torch.Size([1, 2]		)
            _lowerCamelCase			    =				torch.tensor([-2.8_7_2_1, 2.1_2_9_1]		)
            assert torch.allclose(outputs.logits[0, :3] ,						__UpperCAmelCase ,						atol=1E-4		)
            assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
     snake_case__				= 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.'
     )

     snake_case__				= parser.parse_args()
     convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()