

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_(_UpperCamelCase			,	_UpperCamelCase=False			,	_UpperCamelCase=False			,	_UpperCamelCase=False					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							[]
  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_(_UpperCamelCase			,	_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



  """simple docstring"""






  _snake_case						=							dct.pop(_UpperCamelCase					)
  _snake_case						=							val



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Dict:



  """simple docstring"""






  _snake_case						=							ViltConfig(image_size=384			,	patch_size=32			,	tie_word_embeddings=_UpperCamelCase					)
  _snake_case						=							False
  _snake_case						=							False
  _snake_case						=							False
  _snake_case						=							False
  if "vqa" in checkpoint_url:
    _snake_case						=							True
    _snake_case						=							3_129
    _snake_case						=							'''huggingface/label-files'''
    _snake_case						=							'''vqa2-id2label.json'''
    _snake_case						=							json.load(open(hf_hub_download(_UpperCamelCase			,	_UpperCamelCase			,	repo_type='''dataset'''					)			,	'''r'''					)					)
    _snake_case						=							{int(_UpperCamelCase					): v for k, v in idalabel.items()}
    _snake_case						=							idalabel
    _snake_case						=							{v: k for k, v in idalabel.items()}
    _snake_case						=							ViltForQuestionAnswering(_UpperCamelCase					)
  elif "nlvr" in checkpoint_url:
    _snake_case						=							True
    _snake_case						=							2
    _snake_case						=							{0: '''False''', 1: '''True'''}
    _snake_case						=							{v: k for k, v in config.idalabel.items()}
    _snake_case						=							3
    _snake_case						=							ViltForImagesAndTextClassification(_UpperCamelCase					)
  elif "irtr" in checkpoint_url:
    _snake_case						=							True
    _snake_case						=							ViltForImageAndTextRetrieval(_UpperCamelCase					)
  elif "mlm_itm" in checkpoint_url:
    _snake_case						=							True
    _snake_case						=							ViltForMaskedLM(_UpperCamelCase					)
  else:
    raise ValueError('''Unknown model type'''					)

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

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

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

  # Verify outputs
  if mlm_model:
    _snake_case						=							torch.Size([1, 11, 30_522]					)
    _snake_case						=							torch.tensor([-12.5061, -12.5123, -12.5174]					)
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,	_UpperCamelCase			,	atol=1E-4					)

    # verify masked token prediction equals "cats"
    _snake_case						=							outputs.logits[0, 4, :].argmax(-1					).item()
    assert tokenizer.decode([predicted_id]					) == "cats"
  elif vqa_model:
    _snake_case						=							torch.Size([1, 3_129]					)
    _snake_case						=							torch.tensor([-15.9495, -18.1472, -10.3041]					)
    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"
    _snake_case						=							outputs.logits.argmax(-1					).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    _snake_case						=							torch.Size([1, 2]					)
    _snake_case						=							torch.tensor([-2.8721, 2.1291]					)
    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)





from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							(1 - _cos) / 2
  _snake_case						=							1 - _cos

  _snake_case						=							1 + alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							(1 + _cos) / 2
  _snake_case						=							-1 - _cos

  _snake_case						=							1 + alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							_sin / 2
  _snake_case						=							0
  _snake_case						=							-ba

  _snake_case						=							1 + alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							1 - alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 + alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([ba, ba, ba]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)			,	)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)
  _snake_case						=							10 ** (gain_db / 40)

  _snake_case						=							1 + alpha * big_a
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha * big_a
  _snake_case						=							1 + alpha / big_a
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha / big_a

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)			,	)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)
  _snake_case						=							10 ** (gain_db / 40)
  _snake_case						=							(big_a + 1) - (big_a - 1) * _cos
  _snake_case						=							(big_a + 1) + (big_a - 1) * _cos
  _snake_case						=							(big_a - 1) - (big_a + 1) * _cos
  _snake_case						=							(big_a - 1) + (big_a + 1) * _cos
  _snake_case						=							2 * sqrt(_UpperCamelCase					) * alpha

  _snake_case						=							big_a * (pmc + aaa)
  _snake_case						=							2 * big_a * mpc
  _snake_case						=							big_a * (pmc - aaa)
  _snake_case						=							ppmc + aaa
  _snake_case						=							-2 * pmpc
  _snake_case						=							ppmc - aaa

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)			,	)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)
  _snake_case						=							10 ** (gain_db / 40)
  _snake_case						=							(big_a + 1) - (big_a - 1) * _cos
  _snake_case						=							(big_a + 1) + (big_a - 1) * _cos
  _snake_case						=							(big_a - 1) - (big_a + 1) * _cos
  _snake_case						=							(big_a - 1) + (big_a + 1) * _cos
  _snake_case						=							2 * sqrt(_UpperCamelCase					) * alpha

  _snake_case						=							big_a * (ppmc + aaa)
  _snake_case						=							-2 * big_a * pmpc
  _snake_case						=							big_a * (ppmc - aaa)
  _snake_case						=							pmc + aaa
  _snake_case						=							2 * mpc
  _snake_case						=							pmc - aaa

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt

