






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 	__lowerCamelCase      (		snake_case__	,snake_case__=False	,snake_case__=False	,snake_case__=False		)							->  Any:






						"""simple docstring"""





						_SCREAMING_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 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  str:






						"""simple docstring"""





						for i in range(config.num_hidden_layers		):
												_SCREAMING_SNAKE_CASE     				=  """vilt."""
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight'		)
												_SCREAMING_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
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[
												    : config.hidden_size, :
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[: config.hidden_size]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[
												    -config.hidden_size :, :
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[-config.hidden_size :]

def 	__lowerCamelCase      (		snake_case__		)							->  List[str]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dct.pop(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  val

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ViltConfig(image_size=3_84	,patch_size=32	,tie_word_embeddings=snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  False
						_SCREAMING_SNAKE_CASE     				=  False
						_SCREAMING_SNAKE_CASE     				=  False
						_SCREAMING_SNAKE_CASE     				=  False
						if "vqa" in checkpoint_url:
												_SCREAMING_SNAKE_CASE     				=  True
												_SCREAMING_SNAKE_CASE     				=  31_29
												_SCREAMING_SNAKE_CASE     				=  """huggingface/label-files"""
												_SCREAMING_SNAKE_CASE     				=  """vqa2-id2label.json"""
												_SCREAMING_SNAKE_CASE     				=  json.load(open(hf_hub_download(snake_case__	,snake_case__	,repo_type="""dataset"""		)	,"""r"""		)		)
												_SCREAMING_SNAKE_CASE     				=  {int(snake_case__		): v for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE     				=  idalabel
												_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE     				=  ViltForQuestionAnswering(snake_case__		)
						elif "nlvr" in checkpoint_url:
												_SCREAMING_SNAKE_CASE     				=  True
												_SCREAMING_SNAKE_CASE     				=  2
												_SCREAMING_SNAKE_CASE     				=  {0: """False""", 1: """True"""}
												_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in config.idalabel.items()}
												_SCREAMING_SNAKE_CASE     				=  3
												_SCREAMING_SNAKE_CASE     				=  ViltForImagesAndTextClassification(snake_case__		)
						elif "irtr" in checkpoint_url:
												_SCREAMING_SNAKE_CASE     				=  True
												_SCREAMING_SNAKE_CASE     				=  ViltForImageAndTextRetrieval(snake_case__		)
						elif "mlm_itm" in checkpoint_url:
												_SCREAMING_SNAKE_CASE     				=  True
												_SCREAMING_SNAKE_CASE     				=  ViltForMaskedLM(snake_case__		)
						else:
												raise ValueError("""Unknown model type"""		)

						# load state_dict of original model, remove and rename some keys
						_SCREAMING_SNAKE_CASE     				=  torch.hub.load_state_dict_from_url(snake_case__	,map_location="""cpu"""		)["""state_dict"""]
						_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  ["""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:
												_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  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
						_SCREAMING_SNAKE_CASE     				=  ViltImageProcessor(size=3_84		)
						_SCREAMING_SNAKE_CASE     				=  BertTokenizer.from_pretrained("""bert-base-uncased"""		)
						_SCREAMING_SNAKE_CASE     				=  ViltProcessor(snake_case__	,snake_case__		)

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

						# Verify outputs
						if mlm_model:
												_SCREAMING_SNAKE_CASE     				=  torch.Size([1, 11, 3_05_22]		)
												_SCREAMING_SNAKE_CASE     				=  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"
												_SCREAMING_SNAKE_CASE     				=  outputs.logits[0, 4, :].argmax(-1		).item()
												assert tokenizer.decode([predicted_id]		) == "cats"
						elif vqa_model:
												_SCREAMING_SNAKE_CASE     				=  torch.Size([1, 31_29]		)
												_SCREAMING_SNAKE_CASE     				=  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"
												_SCREAMING_SNAKE_CASE     				=  outputs.logits.argmax(-1		).item()
												assert model.config.idalabel[predicted_idx] == "2"
						elif nlvr_model:
												_SCREAMING_SNAKE_CASE     				=  torch.Size([1, 2]		)
												_SCREAMING_SNAKE_CASE     				=  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)









def 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		)
						for i in range(length - 1		):
												_SCREAMING_SNAKE_CASE     				=  i
												for k in range(i + 1	,snake_case__		):
																		if collection[k] < collection[least]:
																								_SCREAMING_SNAKE_CASE     				=  k
												if least != i:
																		_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (collection[i], collection[least])
						return collection


if __name__ == "__main__":
		UpperCamelCase			    =     input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase			    =     [int(item) for item in user_input.split(''',''')]
		print(selection_sort(unsorted))

