"""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  =				logging.get_logger(__name__)


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	List[Any]=False						,   _lowerCamelCase:	Optional[int]=False						,   _lowerCamelCase:	int=False      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     []
							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    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Optional[Any]      ) ->      Union[str, 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					:		int		   =     state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight"""      )
														__lowerCamelCase					:		List[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					:		Optional[int]		   =     in_proj_weight[
														    : config.hidden_size, :
														]
														__lowerCamelCase					:		List[str]		   =     in_proj_bias[: config.hidden_size]
														__lowerCamelCase					:		Tuple		   =     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowerCamelCase					:		Dict		   =     in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__lowerCamelCase					:		Optional[Any]		   =     in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowerCamelCase					:		List[str]		   =     in_proj_bias[-config.hidden_size :]


def    lowercase_   (   _lowerCamelCase:	Dict      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     ["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE_						,   SCREAMING_SNAKE_CASE_      )


def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	List[Any]      ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     dct.pop(SCREAMING_SNAKE_CASE_      )
							__lowerCamelCase					:		Union[str, Any]		   =     val


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	int      ) ->      str:



							'''simple docstring'''

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

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

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

							# Define processor
							__lowerCamelCase					:		List[Any]		   =     ViltImageProcessor(size=384      )
							__lowerCamelCase					:		Any		   =     BertTokenizer.from_pretrained("bert-base-uncased"      )
							__lowerCamelCase					:		List[str]		   =     ViltProcessor(SCREAMING_SNAKE_CASE_						,   SCREAMING_SNAKE_CASE_      )

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

							# Verify outputs
							if mlm_model:
														__lowerCamelCase					:		Tuple		   =     torch.Size([1, 11, 30522]      )
														__lowerCamelCase					:		List[str]		   =     torch.tensor([-12.5061, -12.5123, -12.5174]      )
														assert outputs.logits.shape == expected_shape
														assert torch.allclose(outputs.logits[0, 0, :3]						,   SCREAMING_SNAKE_CASE_						,   atol=1E-4      )

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

														# verify vqa prediction equals "2"
														__lowerCamelCase					:		List[str]		   =     outputs.logits.argmax(-1      ).item()
														assert model.config.idalabel[predicted_idx] == "2"
							elif nlvr_model:
														__lowerCamelCase					:		Any		   =     torch.Size([1, 2]      )
														__lowerCamelCase					:		Optional[int]		   =     torch.tensor([-2.8721, 2.1291]      )
														assert torch.allclose(outputs.logits[0, :3]						,   SCREAMING_SNAKE_CASE_						,   atol=1E-4      )
														assert outputs.logits.shape == expected_shape

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


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)
"""simple docstring"""





import math
from datetime import datetime, timedelta


def    lowercase_   (   _lowerCamelCase:	int      ) ->      datetime:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     year % 19
							__lowerCamelCase					:		List[str]		   =     year % 4
							__lowerCamelCase					:		Dict		   =     year % 7
							__lowerCamelCase					:		Optional[Any]		   =     math.floor(year / 100      )
							__lowerCamelCase					:		Tuple		   =     math.floor((13 + 8 * leap_day_inhibits) / 25      )
							__lowerCamelCase					:		List[str]		   =     leap_day_inhibits / 4
							__lowerCamelCase					:		Optional[Any]		   =     (
							    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
							) % 30
							__lowerCamelCase					:		Union[str, Any]		   =     (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

							# days to be added to March 21
							__lowerCamelCase					:		Union[str, Any]		   =     (19 * metonic_cycle + secular_moon_shift) % 30

							# PHM -> Paschal Full Moon
							__lowerCamelCase					:		List[Any]		   =     (
							    2 * julian_leap_year
							    + 4 * non_leap_year
							    + 6 * days_to_add
							    + century_starting_point
							) % 7

							if days_to_add == 29 and days_from_phm_to_sunday == 6:
														return datetime(_lowerCamelCase						,   4						,   19      )
							elif days_to_add == 28 and days_from_phm_to_sunday == 6:
														return datetime(_lowerCamelCase						,   4						,   18      )
							else:
														return datetime(_lowerCamelCase						,   3						,   22      ) + timedelta(
														    days=int(days_to_add + days_from_phm_to_sunday      )      )


if __name__ == "__main__":
				for year in (1994, 2000, 2010, 2021, 2023):
								__A  =				'''will be''' if year > datetime.now().year else '''was'''
								print(F"""Easter in {year} {tense} {gauss_easter(year)}""")