

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()
__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False		,							SCREAMING_SNAKE_CASE=False		,							SCREAMING_SNAKE_CASE=False				)       ->      List[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     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		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Any:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(snake_case__		,							snake_case__				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    dct.pop(snake_case__				)
     _lowerCAmelCase   :					List[Any]        =    val







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







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

     # load state_dict of original model, remove and rename some keys
     _lowerCAmelCase   :					Any        =    torch.hub.load_state_dict_from_url(snake_case__		,							map_location="cpu"				)["state_dict"]
     _lowerCAmelCase   :					Optional[Any]        =    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:
          _lowerCAmelCase   :					str        =    ["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:
          _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    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
     _lowerCAmelCase   :					List[str]        =    ViltImageProcessor(size=384				)
     _lowerCAmelCase   :					str        =    BertTokenizer.from_pretrained("bert-base-uncased"				)
     _lowerCAmelCase   :					List[str]        =    ViltProcessor(snake_case__		,							snake_case__				)

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

     # Verify outputs
     if mlm_model:
          _lowerCAmelCase   :					Tuple        =    torch.Size([1, 11, 30522]				)
          _lowerCAmelCase   :					str        =    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]		,							snake_case__		,							atol=1e-4				)

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

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









import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__UpperCAmelCase    				=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'





class A__    ( unittest.TestCase  ,		A ):



 """simple docstring"""







 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    load_tool("text-question-answering"       )
      self.tool.setup()
      _lowerCAmelCase   :					int        =    load_tool("text-question-answering"     ,						remote=A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.tool(A_     ,						"What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.remote_tool(A_     ,						"What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.tool(text=A_     ,						question="What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )


 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.remote_tool(text=A_     ,						question="What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )






