






'''simple docstring'''







import os
from math import logaa





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase = "base_exp.txt"      )      ->       int:
							lowerCamelCase__					:	float         =       0
							lowerCamelCase__					:	str         =       0
							for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase      )				,			UpperCamelCase      )      )      ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       list(map(UpperCamelCase				,			line.split(""","""      )      )      )
														if x * logaa(UpperCamelCase      ) > largest:
																					lowerCamelCase__					:	Tuple         =       x * logaa(UpperCamelCase      )
																					lowerCamelCase__					:	Union[str, Any]         =       i + 1
							return result


if __name__ == "__main__":
			print(solution())









'''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	:			List[str]         =logging.get_logger(__name__)





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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Any:
							lowerCamelCase__					:	Dict         =       ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	int         =       dct.pop(UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       val





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

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

							# Define processor
							lowerCamelCase__					:	Optional[int]         =       ViltImageProcessor(size=384      )
							lowerCamelCase__					:	List[str]         =       BertTokenizer.from_pretrained("""bert-base-uncased"""      )
							lowerCamelCase__					:	Union[str, Any]         =       ViltProcessor(UpperCamelCase				,			UpperCamelCase      )

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

							# Verify outputs
							if mlm_model:
														lowerCamelCase__					:	Tuple         =       torch.Size([1, 11, 30522]      )
														lowerCamelCase__					:	int         =       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"
														lowerCamelCase__					:	int         =       outputs.logits[0, 4, :].argmax(-1      ).item()
														assert tokenizer.decode([predicted_id]      ) == "cats"
							elif vqa_model:
														lowerCamelCase__					:	str         =       torch.Size([1, 3129]      )
														lowerCamelCase__					:	Any         =       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"
														lowerCamelCase__					:	Tuple         =       outputs.logits.argmax(-1      ).item()
														assert model.config.idalabel[predicted_idx] == "2"
							elif nlvr_model:
														lowerCamelCase__					:	str         =       torch.Size([1, 2]      )
														lowerCamelCase__					:	Optional[Any]         =       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	:			str         =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	:			Tuple         =parser.parse_args()
			convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

