






'''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()
UpperCamelCase     :   str	      =		logging.get_logger(__name__)
def     A__      (					__lowerCAmelCase		:      int ,     __lowerCAmelCase		:      Dict=False ,     __lowerCAmelCase		:      Tuple=False ,     __lowerCAmelCase		:      List[str]=False    ):
						lowerCamelCase__								=	[]
						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     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      Tuple    ):
						for i in range(config.num_hidden_layers    ):
												lowerCamelCase__								=	"""vilt."""
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCamelCase__								=	state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight'''    )
												lowerCamelCase__								=	state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias'''    )
												# next, add query, keys and values (in that order) to the state dict
												lowerCamelCase__								=	in_proj_weight[
												    : config.hidden_size, :
												]
												lowerCamelCase__								=	in_proj_bias[: config.hidden_size]
												lowerCamelCase__								=	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCamelCase__								=	in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCamelCase__								=	in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCamelCase__								=	in_proj_bias[-config.hidden_size :]
def     A__      (					__lowerCAmelCase		:      Optional[Any]    ):
						lowerCamelCase__								=	["""head.weight""", """head.bias"""]
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase ,     __lowerCAmelCase    )
def     A__      (					__lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      Union[str, Any]    ):
						lowerCamelCase__								=	dct.pop(__lowerCAmelCase    )
						lowerCamelCase__								=	val
@torch.no_grad()
def     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      Optional[Any]    ):
						lowerCamelCase__								=	ViltConfig(image_size=384 ,     patch_size=32 ,     tie_word_embeddings=__lowerCAmelCase    )
						lowerCamelCase__								=	False
						lowerCamelCase__								=	False
						lowerCamelCase__								=	False
						lowerCamelCase__								=	False
						if "vqa" in checkpoint_url:
												lowerCamelCase__								=	True
												lowerCamelCase__								=	3129
												lowerCamelCase__								=	"""huggingface/label-files"""
												lowerCamelCase__								=	"""vqa2-id2label.json"""
												lowerCamelCase__								=	json.load(open(hf_hub_download(__lowerCAmelCase ,     __lowerCAmelCase ,     repo_type="""dataset"""    ) ,     """r"""    )    )
												lowerCamelCase__								=	{int(__lowerCAmelCase    ): v for k, v in idalabel.items()}
												lowerCamelCase__								=	idalabel
												lowerCamelCase__								=	{v: k for k, v in idalabel.items()}
												lowerCamelCase__								=	ViltForQuestionAnswering(__lowerCAmelCase    )
						elif "nlvr" in checkpoint_url:
												lowerCamelCase__								=	True
												lowerCamelCase__								=	2
												lowerCamelCase__								=	{0: """False""", 1: """True"""}
												lowerCamelCase__								=	{v: k for k, v in config.idalabel.items()}
												lowerCamelCase__								=	3
												lowerCamelCase__								=	ViltForImagesAndTextClassification(__lowerCAmelCase    )
						elif "irtr" in checkpoint_url:
												lowerCamelCase__								=	True
												lowerCamelCase__								=	ViltForImageAndTextRetrieval(__lowerCAmelCase    )
						elif "mlm_itm" in checkpoint_url:
												lowerCamelCase__								=	True
												lowerCamelCase__								=	ViltForMaskedLM(__lowerCAmelCase    )
						else:
												raise ValueError("""Unknown model type"""    )

						# load state_dict of original model, remove and rename some keys
						lowerCamelCase__								=	torch.hub.load_state_dict_from_url(__lowerCAmelCase ,     map_location="""cpu"""    )["""state_dict"""]
						lowerCamelCase__								=	create_rename_keys(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )
						for src, dest in rename_keys:
												rename_key(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )
						read_in_q_k_v(__lowerCAmelCase ,     __lowerCAmelCase    )
						if mlm_model or irtr_model:
												lowerCamelCase__								=	["""itm_score.fc.weight""", """itm_score.fc.bias"""]
												for k in ignore_keys:
																		state_dict.pop(__lowerCAmelCase ,     __lowerCAmelCase    )

    # load state dict into HuggingFace model
						model.eval()
						if mlm_model:
												lowerCamelCase__							,      lowerCamelCase__								=	model.load_state_dict(__lowerCAmelCase ,     strict=__lowerCAmelCase    )
												assert missing_keys == ["mlm_score.decoder.bias"]
						else:
												model.load_state_dict(__lowerCAmelCase    )

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

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

						# Verify outputs
						if mlm_model:
												lowerCamelCase__								=	torch.Size([1, 11, 3_0522]    )
												lowerCamelCase__								=	torch.tensor([-12.5061, -12.5123, -12.5174]    )
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3] ,     __lowerCAmelCase ,     atol=1e-4    )

												# verify masked token prediction equals "cats"
												lowerCamelCase__								=	outputs.logits[0, 4, :].argmax(-1    ).item()
												assert tokenizer.decode([predicted_id]    ) == "cats"
						elif vqa_model:
												lowerCamelCase__								=	torch.Size([1, 3129]    )
												lowerCamelCase__								=	torch.tensor([-15.9495, -18.1472, -10.3041]    )
												assert torch.allclose(outputs.logits[0, :3] ,     __lowerCAmelCase ,     atol=1e-4    )
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3] ,     __lowerCAmelCase ,     atol=1e-4    )

												# verify vqa prediction equals "2"
												lowerCamelCase__								=	outputs.logits.argmax(-1    ).item()
												assert model.config.idalabel[predicted_idx] == "2"
						elif nlvr_model:
												lowerCamelCase__								=	torch.Size([1, 2]    )
												lowerCamelCase__								=	torch.tensor([-2.8721, 2.1291]    )
												assert torch.allclose(outputs.logits[0, :3] ,     __lowerCAmelCase ,     atol=1e-4    )
												assert outputs.logits.shape == expected_shape

						Path(__lowerCAmelCase    ).mkdir(exist_ok=__lowerCAmelCase    )
						print(F'''Saving model and processor to {pytorch_dump_folder_path}'''    )
						model.save_pretrained(__lowerCAmelCase    )
						processor.save_pretrained(__lowerCAmelCase    )


if __name__ == "__main__":
							UpperCamelCase     :   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.'
							)

							UpperCamelCase     :   Union[str, Any]	      =		parser.parse_args()
							convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = list(snake_case__       )
	A_		:    List[Any]						     = list(snake_case__       )
	A_		:    List[str]						     = 0
	for i in range(len(snake_case__       )       ):
		if lista[i] != lista[i]:
			count += 1
			A_		:    Optional[int]						     = """_"""
	if count > 1:
		return False
	else:
		return "".join(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = []
	while True:
		A_		:    Any						     = ["""$"""] * len(snake_case__       )
		A_		:    int						     = []
		for i in range(len(snake_case__       )       ):
			for j in range(i + 1 , len(snake_case__       )       ):
				A_		:    int						     = compare_string(binary[i] , binary[j]       )
				if k is False:
					A_		:    Optional[int]						     = """*"""
					A_		:    str						     = """*"""
					temp.append("""X"""       )
		for i in range(len(snake_case__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(snake_case__       ) == 0:
			return pi
		A_		:    Tuple						     = list(set(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	for minterm in minterms:
		A_		:    Any						     = """"""
		for _ in range(snake_case__       ):
			A_		:    int						     = str(minterm % 2       ) + string
			minterm //= 2
		temp.append(snake_case__       )
	return temp






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    str						     = list(snake_case__       )
	A_		:    List[Any]						     = list(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for i in range(len(snake_case__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	A_		:    Tuple						     = [0] * len(snake_case__       )
	for i in range(len(chart[0]       )       ):
		A_		:    Optional[int]						     = 0
		A_		:    List[str]						     = -1
		for j in range(len(snake_case__       )       ):
			if chart[j][i] == 1:
				count += 1
				A_		:    Union[str, Any]						     = j
		if count == 1:
			A_		:    str						     = 1
	for i in range(len(snake_case__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(snake_case__       )       ):
						A_		:    Optional[int]						     = 0
			temp.append(prime_implicants[i]       )
	while True:
		A_		:    List[str]						     = 0
		A_		:    List[str]						     = -1
		A_		:    Union[str, Any]						     = 0
		for i in range(len(snake_case__       )       ):
			A_		:    List[str]						     = chart[i].count(1       )
			if count_n > max_n:
				A_		:    Union[str, Any]						     = count_n
				A_		:    Optional[int]						     = i

		if max_n == 0:
			return temp

		temp.append(prime_implicants[rem]       )

		for i in range(len(chart[0]       )       ):
			if chart[rem][i] == 1:
				for j in range(len(snake_case__       )       ):
					A_		:    Optional[Any]						     = 0






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = [[0 for x in range(len(snake_case__       )       )] for x in range(len(snake_case__       )       )]
	for i in range(len(snake_case__       )       ):
		A_		:    List[str]						     = prime_implicants[i].count("""_"""       )
		for j in range(len(snake_case__       )       ):
			if is_for_table(prime_implicants[i] , binary[j] , snake_case__       ):
				A_		:    Union[str, Any]						     = 1

	return chart






def       __UpperCamelCase						(		):
	A_		:    Union[str, Any]						     = int(input("""Enter the no. of variables\n"""       )       )
	A_		:    List[Any]						     = [
	    float(snake_case__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	A_		:    List[str]						     = decimal_to_binary(snake_case__ , snake_case__       )

	A_		:    str						     = check(snake_case__       )
	print("""Prime Implicants are:"""       )
	print(snake_case__       )
	A_		:    List[str]						     = prime_implicant_chart(snake_case__ , snake_case__       )

	A_		:    int						     = selection(snake_case__ , snake_case__       )
	print("""Essential Prime Implicants are:"""       )
	print(snake_case__       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

