


'''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()
SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=False	,		__UpperCamelCase=False	,		__UpperCamelCase=False					)->				Any:
      UpperCamelCase       						=      []
      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      for i in range(config.num_hidden_layers					):
            UpperCamelCase       						=      """vilt."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            UpperCamelCase       						=      state_dict.pop(F"transformer.blocks.{i}.attn.qkv.weight"					)
            UpperCamelCase       						=      state_dict.pop(F"transformer.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 					lowercase__				(   __UpperCamelCase					)->				List[str]:
      UpperCamelCase       						=      ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				List[str]:
      UpperCamelCase       						=      dct.pop(__UpperCamelCase					)
      UpperCamelCase       						=      val







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

      # load state_dict of original model, remove and rename some keys
      UpperCamelCase       						=      torch.hub.load_state_dict_from_url(__UpperCamelCase	,		map_location="""cpu"""					)["""state_dict"""]
      UpperCamelCase       						=      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:
            UpperCamelCase       						=      ["""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:
            UpperCamelCase						,UpperCamelCase       						=      model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
            assert missing_keys == ["mlm_score.decoder.bias"]
      else:
            model.load_state_dict(__UpperCamelCase					)

      # Define processor
      UpperCamelCase       						=      ViltImageProcessor(size=384					)
      UpperCamelCase       						=      BertTokenizer.from_pretrained("""bert-base-uncased"""					)
      UpperCamelCase       						=      ViltProcessor(__UpperCamelCase	,		__UpperCamelCase					)

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

      # Verify outputs
      if mlm_model:
            UpperCamelCase       						=      torch.Size([1, 11, 30522]					)
            UpperCamelCase       						=      torch.tensor([-12.5_061, -12.5_123, -12.5_174]					)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]	,		__UpperCamelCase	,		atol=1E-4					)

            # verify masked token prediction equals "cats"
            UpperCamelCase       						=      outputs.logits[0, 4, :].argmax(-1					).item()
            assert tokenizer.decode([predicted_id]					) == "cats"
      elif vqa_model:
            UpperCamelCase       						=      torch.Size([1, 3129]					)
            UpperCamelCase       						=      torch.tensor([-15.9_495, -18.1_472, -10.3_041]					)
            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"
            UpperCamelCase       						=      outputs.logits.argmax(-1					).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            UpperCamelCase       						=      torch.Size([1, 2]					)
            UpperCamelCase       						=      torch.tensor([-2.8_721, 2.1_291]					)
            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__":
     SCREAMING_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.'
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
