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



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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   lowercase__:   Optional[int]					=      dct.pop(__UpperCAmelCase )
   lowercase__:   int					=      val



@torch.no_grad()
def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Dict:
   lowercase__:   List[Any]					=      ViltConfig(image_size=3_8_4       ,    patch_size=3_2       ,    tie_word_embeddings=__UpperCAmelCase )
   lowercase__:   Any					=      False
   lowercase__:   Optional[Any]					=      False
   lowercase__:   Any					=      False
   lowercase__:   Any					=      False
   if "vqa" in checkpoint_url:
      lowercase__:   str					=      True
      lowercase__:   int					=      3_1_2_9
      lowercase__:   Any					=      '''huggingface/label-files'''
      lowercase__:   Optional[Any]					=      '''vqa2-id2label.json'''
      lowercase__:   List[Any]					=      json.load(open(hf_hub_download(__UpperCAmelCase       ,    __UpperCAmelCase       ,    repo_type='''dataset''' )       ,    '''r''' ) )
      lowercase__:   Optional[Any]					=      {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
      lowercase__:   str					=      idalabel
      lowercase__:   Optional[Any]					=      {v: k for k, v in idalabel.items()}
      lowercase__:   str					=      ViltForQuestionAnswering(__UpperCAmelCase )
   elif "nlvr" in checkpoint_url:
      lowercase__:   List[Any]					=      True
      lowercase__:   Union[str, Any]					=      2
      lowercase__:   Tuple					=      {0: '''False''', 1: '''True'''}
      lowercase__:   Optional[int]					=      {v: k for k, v in config.idalabel.items()}
      lowercase__:   Dict					=      3
      lowercase__:   Dict					=      ViltForImagesAndTextClassification(__UpperCAmelCase )
   elif "irtr" in checkpoint_url:
      lowercase__:   Any					=      True
      lowercase__:   List[str]					=      ViltForImageAndTextRetrieval(__UpperCAmelCase )
   elif "mlm_itm" in checkpoint_url:
      lowercase__:   Any					=      True
      lowercase__:   List[Any]					=      ViltForMaskedLM(__UpperCAmelCase )
   else:
      raise ValueError('''Unknown model type''' )

   # load state_dict of original model, remove and rename some keys
   lowercase__:   Tuple					=      torch.hub.load_state_dict_from_url(__UpperCAmelCase       ,    map_location='''cpu''' )['''state_dict''']
   lowercase__:   Optional[int]					=      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:
      lowercase__:   Any					=      ['''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:
      lowercase__, lowercase__:   Tuple					=      model.load_state_dict(__UpperCAmelCase       ,    strict=__UpperCAmelCase )
      assert missing_keys == ["mlm_score.decoder.bias"]
   else:
      model.load_state_dict(__UpperCAmelCase )

   # Define processor
   lowercase__:   Optional[int]					=      ViltImageProcessor(size=3_8_4 )
   lowercase__:   Union[str, Any]					=      BertTokenizer.from_pretrained('''bert-base-uncased''' )
   lowercase__:   Union[str, Any]					=      ViltProcessor(__UpperCAmelCase       ,    __UpperCAmelCase )

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

   # Verify outputs
   if mlm_model:
      lowercase__:   List[Any]					=      torch.Size([1, 1_1, 3_0_5_2_2] )
      lowercase__:   Any					=      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"
      lowercase__:   Optional[int]					=      outputs.logits[0, 4, :].argmax(-1 ).item()
      assert tokenizer.decode([predicted_id] ) == "cats"
   elif vqa_model:
      lowercase__:   Optional[int]					=      torch.Size([1, 3_1_2_9] )
      lowercase__:   int					=      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"
      lowercase__:   Union[str, Any]					=      outputs.logits.argmax(-1 ).item()
      assert model.config.idalabel[predicted_idx] == "2"
   elif nlvr_model:
      lowercase__:   Any					=      torch.Size([1, 2] )
      lowercase__:   Union[str, Any]					=      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__":
       __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"""






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			   = logging.get_logger(__name__)

__A			   = {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[Any]						    = "efficientformer"






    def __init__(   self    ,			_UpperCAmelCase = [3, 2, 6, 4]    ,			_UpperCAmelCase = [48, 96, 224, 448]    ,			_UpperCAmelCase = [True, True, True, True]    ,			_UpperCAmelCase = 448    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 7    ,			_UpperCAmelCase = 5    ,			_UpperCAmelCase = 8    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = 16    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 2    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 1e-5    ,			_UpperCAmelCase = "gelu"    ,			_UpperCAmelCase = 0.02    ,			_UpperCAmelCase = 1e-1_2    ,			_UpperCAmelCase = 224    ,			_UpperCAmelCase = 1e-0_5    ,			**_UpperCAmelCase    ,			):
       super().__init__(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Union[str, Any]					=      hidden_dropout_prob
       lowercase__:   Union[str, Any]					=      hidden_sizes
       lowercase__:   Any					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Dict					=      initializer_range
       lowercase__:   Optional[int]					=      layer_norm_eps
       lowercase__:   int					=      patch_size
       lowercase__:   int					=      num_channels
       lowercase__:   str					=      depths
       lowercase__:   List[str]					=      mlp_expansion_ratio
       lowercase__:   List[str]					=      downsamples
       lowercase__:   List[str]					=      dim
       lowercase__:   Optional[Any]					=      key_dim
       lowercase__:   Union[str, Any]					=      attention_ratio
       lowercase__:   Any					=      resolution
       lowercase__:   Any					=      pool_size
       lowercase__:   List[Any]					=      downsample_patch_size
       lowercase__:   Optional[int]					=      downsample_stride
       lowercase__:   Union[str, Any]					=      downsample_pad
       lowercase__:   List[Any]					=      drop_path_rate
       lowercase__:   Optional[Any]					=      num_metaad_blocks
       lowercase__:   Any					=      distillation
       lowercase__:   Optional[int]					=      use_layer_scale
       lowercase__:   List[str]					=      layer_scale_init_value
       lowercase__:   Dict					=      image_size
       lowercase__:   List[str]					=      batch_norm_eps
