



"""simple docstring"""


import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			VideoMAEConfig()

						set_architecture_configs(lowercase_		,     lowercase_						)

						if "finetuned" not in model_name:
												UpperCAmelCase					=			False

						if "finetuned" in model_name:
												UpperCAmelCase					=			'huggingface/label-files'
												if "kinetics" in model_name:
																		UpperCAmelCase					=			400
																		UpperCAmelCase					=			'kinetics400-id2label.json'
												elif "ssv2" in model_name:
																		UpperCAmelCase					=			174
																		UpperCAmelCase					=			'something-something-v2-id2label.json'
												else:
																		raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'						)
												UpperCAmelCase					=			json.load(open(hf_hub_download(lowercase_		,     lowercase_		,     repo_type='dataset'						)		,     'r'						)						)
												UpperCAmelCase					=			{int(lowercase_						): v for k, v in idalabel.items()}
												UpperCAmelCase					=			idalabel
												UpperCAmelCase					=			{v: k for k, v in idalabel.items()}

						return config

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						if "small" in model_name:
												UpperCAmelCase					=			384
												UpperCAmelCase					=			1536
												UpperCAmelCase					=			12
												UpperCAmelCase					=			16
												UpperCAmelCase					=			12
												UpperCAmelCase					=			3
												UpperCAmelCase					=			192
												UpperCAmelCase					=			768
						elif "large" in model_name:
												UpperCAmelCase					=			1024
												UpperCAmelCase					=			4096
												UpperCAmelCase					=			24
												UpperCAmelCase					=			16
												UpperCAmelCase					=			12
												UpperCAmelCase					=			8
												UpperCAmelCase					=			512
												UpperCAmelCase					=			2048
						elif "huge" in model_name:
												UpperCAmelCase					=			1280
												UpperCAmelCase					=			5120
												UpperCAmelCase					=			32
												UpperCAmelCase					=			16
												UpperCAmelCase					=			12
												UpperCAmelCase					=			8
												UpperCAmelCase					=			640
												UpperCAmelCase					=			2560
						elif "base" not in model_name:
												raise ValueError('Model name should include either "small", "base", "large", or "huge"'						)

def 			_lowerCAmelCase				(       lowercase_						):
						if "encoder." in name:
												UpperCAmelCase					=			name.replace('encoder.'		,     ''						)
						if "cls_token" in name:
												UpperCAmelCase					=			name.replace('cls_token'		,     'videomae.embeddings.cls_token'						)
						if "decoder_pos_embed" in name:
												UpperCAmelCase					=			name.replace('decoder_pos_embed'		,     'decoder.decoder_pos_embed'						)
						if "pos_embed" in name and "decoder" not in name:
												UpperCAmelCase					=			name.replace('pos_embed'		,     'videomae.embeddings.position_embeddings'						)
						if "patch_embed.proj" in name:
												UpperCAmelCase					=			name.replace('patch_embed.proj'		,     'videomae.embeddings.patch_embeddings.projection'						)
						if "patch_embed.norm" in name:
												UpperCAmelCase					=			name.replace('patch_embed.norm'		,     'videomae.embeddings.norm'						)
						if "decoder.blocks" in name:
												UpperCAmelCase					=			name.replace('decoder.blocks'		,     'decoder.decoder_layers'						)
						if "blocks" in name:
												UpperCAmelCase					=			name.replace('blocks'		,     'videomae.encoder.layer'						)
						if "attn.proj" in name:
												UpperCAmelCase					=			name.replace('attn.proj'		,     'attention.output.dense'						)
						if "attn" in name and "bias" not in name:
												UpperCAmelCase					=			name.replace('attn'		,     'attention.self'						)
						if "attn" in name:
												UpperCAmelCase					=			name.replace('attn'		,     'attention.attention'						)
						if "norm1" in name:
												UpperCAmelCase					=			name.replace('norm1'		,     'layernorm_before'						)
						if "norm2" in name:
												UpperCAmelCase					=			name.replace('norm2'		,     'layernorm_after'						)
						if "mlp.fc1" in name:
												UpperCAmelCase					=			name.replace('mlp.fc1'		,     'intermediate.dense'						)
						if "mlp.fc2" in name:
												UpperCAmelCase					=			name.replace('mlp.fc2'		,     'output.dense'						)
						if "decoder_embed" in name:
												UpperCAmelCase					=			name.replace('decoder_embed'		,     'decoder.decoder_embed'						)
						if "decoder_norm" in name:
												UpperCAmelCase					=			name.replace('decoder_norm'		,     'decoder.decoder_norm'						)
						if "decoder_pred" in name:
												UpperCAmelCase					=			name.replace('decoder_pred'		,     'decoder.decoder_pred'						)
						if "norm.weight" in name and "decoder" not in name and "fc" not in name:
												UpperCAmelCase					=			name.replace('norm.weight'		,     'videomae.layernorm.weight'						)
						if "norm.bias" in name and "decoder" not in name and "fc" not in name:
												UpperCAmelCase					=			name.replace('norm.bias'		,     'videomae.layernorm.bias'						)
						if "head" in name and "decoder" not in name:
												UpperCAmelCase					=			name.replace('head'		,     'classifier'						)

						return name

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase					=			orig_state_dict.pop(lowercase_						)

												if key.startswith('encoder.'						):
																		UpperCAmelCase					=			key.replace('encoder.'		,     ''						)

												if "qkv" in key:
																		UpperCAmelCase					=			key.split('.'						)
																		if key.startswith('decoder.blocks'						):
																								UpperCAmelCase					=			config.decoder_hidden_size
																								UpperCAmelCase					=			int(key_split[2]						)
																								UpperCAmelCase					=			'decoder.decoder_layers.'
																								if "weight" in key:
																														UpperCAmelCase					=			val[:dim, :]
																														UpperCAmelCase					=			val[dim : dim * 2, :]
																														UpperCAmelCase					=			val[-dim:, :]
																		else:
																								UpperCAmelCase					=			config.hidden_size
																								UpperCAmelCase					=			int(key_split[1]						)
																								UpperCAmelCase					=			'videomae.encoder.layer.'
																								if "weight" in key:
																														UpperCAmelCase					=			val[:dim, :]
																														UpperCAmelCase					=			val[dim : dim * 2, :]
																														UpperCAmelCase					=			val[-dim:, :]
												else:
																		UpperCAmelCase					=			val

						return orig_state_dict

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'		,     filename='eating_spaghetti.npy'		,     repo_type='dataset'						)
						UpperCAmelCase					=			np.load(lowercase_						)
						return list(lowercase_						)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			get_videomae_config(lowercase_						)

						if "finetuned" in model_name:
												UpperCAmelCase					=			VideoMAEForVideoClassification(lowercase_						)
						else:
												UpperCAmelCase					=			VideoMAEForPreTraining(lowercase_						)

						# download original checkpoint, hosted on Google Drive
						UpperCAmelCase					=			'pytorch_model.bin'
						gdown.cached_download(lowercase_		,     lowercase_		,     quiet=lowercase_						)
						UpperCAmelCase					=			torch.load(lowercase_		,     map_location='cpu'						)
						if "model" in files:
												UpperCAmelCase					=			files['model']
						else:
												UpperCAmelCase					=			files['module']
						UpperCAmelCase					=			convert_state_dict(lowercase_		,     lowercase_						)

						model.load_state_dict(lowercase_						)
						model.eval()

						# verify model on basic input
						UpperCAmelCase					=			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,     image_std=[0.5, 0.5, 0.5]						)
						UpperCAmelCase					=			prepare_video()
						UpperCAmelCase					=			image_processor(lowercase_		,     return_tensors='pt'						)

						if "finetuned" not in model_name:
												UpperCAmelCase					=			hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'		,     filename='bool_masked_pos.pt'						)
												UpperCAmelCase					=			torch.load(lowercase_						)

						UpperCAmelCase					=			model(**lowercase_						)
						UpperCAmelCase					=			outputs.logits

						UpperCAmelCase					=			[
						    'videomae-small-finetuned-kinetics',
						    'videomae-small-finetuned-ssv2',
						    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
						    'videomae-base-short',
						    'videomae-base-short-finetuned-kinetics',
						    'videomae-base',
						    'videomae-base-finetuned-kinetics',
						    'videomae-large',
						    'videomae-large-finetuned-kinetics',
						    'videomae-huge-finetuned-kinetics',
						    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
						    'videomae-base-short-ssv2',
						    'videomae-base-short-finetuned-ssv2',
						    'videomae-base-ssv2',
						    'videomae-base-finetuned-ssv2',
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "videomae-small-finetuned-kinetics":
												UpperCAmelCase					=			torch.Size([1, 400]						)
												UpperCAmelCase					=			torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]						)
						elif model_name == "videomae-small-finetuned-ssv2":
												UpperCAmelCase					=			torch.Size([1, 174]						)
												UpperCAmelCase					=			torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]						)
						elif model_name == "videomae-base":
												UpperCAmelCase					=			torch.Size([1, 1408, 1536]						)
												UpperCAmelCase					=			torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]						)
						elif model_name == "videomae-base-short":
												UpperCAmelCase					=			torch.Size([1, 1408, 1536]						)
												UpperCAmelCase					=			torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]						)
												# we verified the loss both for normalized and unnormalized targets for this one
												UpperCAmelCase					=			torch.tensor([0.5_1_4_2]						) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]						)
						elif model_name == "videomae-large":
												UpperCAmelCase					=			torch.Size([1, 1408, 1536]						)
												UpperCAmelCase					=			torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]						)
						elif model_name == "videomae-large-finetuned-kinetics":
												UpperCAmelCase					=			torch.Size([1, 400]						)
												UpperCAmelCase					=			torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]						)
						elif model_name == "videomae-huge-finetuned-kinetics":
												UpperCAmelCase					=			torch.Size([1, 400]						)
												UpperCAmelCase					=			torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]						)
						elif model_name == "videomae-base-short-finetuned-kinetics":
												UpperCAmelCase					=			torch.Size([1, 400]						)
												UpperCAmelCase					=			torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]						)
						elif model_name == "videomae-base-finetuned-kinetics":
												UpperCAmelCase					=			torch.Size([1, 400]						)
												UpperCAmelCase					=			torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]						)
						elif model_name == "videomae-base-short-ssv2":
												UpperCAmelCase					=			torch.Size([1, 1408, 1536]						)
												UpperCAmelCase					=			torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]						)
						elif model_name == "videomae-base-short-finetuned-ssv2":
												UpperCAmelCase					=			torch.Size([1, 174]						)
												UpperCAmelCase					=			torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]						)
						elif model_name == "videomae-base-ssv2":
												UpperCAmelCase					=			torch.Size([1, 1408, 1536]						)
												UpperCAmelCase					=			torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]						)
						elif model_name == "videomae-base-finetuned-ssv2":
												UpperCAmelCase					=			torch.Size([1, 174]						)
												UpperCAmelCase					=			torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]						)
						else:
												raise ValueError(F"""Model name not supported. Should be one of {model_names}"""						)

						# verify logits
						assert logits.shape == expected_shape
						if "finetuned" in model_name:
												assert torch.allclose(logits[0, :3]		,     lowercase_		,     atol=1e-4						)
						else:
												print('Logits:'		,     logits[0, :3, :3]						)
												assert torch.allclose(logits[0, :3, :3]		,     lowercase_		,     atol=1e-4						)
						print('Logits ok!'						)

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												UpperCAmelCase					=			outputs.loss
												assert torch.allclose(lowercase_		,     lowercase_		,     atol=1e-4						)
												print('Loss ok!'						)

						if pytorch_dump_folder_path is not None:
												print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""						)
												image_processor.save_pretrained(lowercase_						)
												model.save_pretrained(lowercase_						)

						if push_to_hub:
												print('Pushing to the hub...'						)
												model.push_to_hub(lowercase_		,     organization='nielsr'						)


if __name__ == "__main__":
				snake_case_    					=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--checkpoint_url""",
				    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
				    type=str,
				    help=(
				        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
				        """ download link."""
				    ),
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
				    type=str,
				    help="""Path to the output PyTorch model directory.""",
				)
				parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
				parser.add_argument(
				    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
				)

				snake_case_    					=	parser.parse_args()
				convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










"""simple docstring"""


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_    					=	3

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating primitive root of p'						)
						while True:
												UpperCAmelCase					=			random.randrange(3		,     lowercase_						)
												if pow(lowercase_		,     2		,     lowercase_						) == 1:
																		continue
												if pow(lowercase_		,     lowercase_		,     lowercase_						) == 1:
																		continue
												return g

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating prime p...'						)
						UpperCAmelCase					=			rabin_miller.generate_large_prime(lowercase_						)  # select large prime number.
						UpperCAmelCase					=			primitive_root(lowercase_						)  # one primitive root on modulo p.
						UpperCAmelCase					=			random.randrange(3		,     lowercase_						)  # private_key -> have to be greater than 2 for safety.
						UpperCAmelCase					=			cryptomath.find_mod_inverse(pow(lowercase_		,     lowercase_		,     lowercase_						)		,     lowercase_						)

						UpperCAmelCase					=			(key_size, e_a, e_a, p)
						UpperCAmelCase					=			(key_size, d)

						return public_key, private_key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						if os.path.exists(F"""{name}_pubkey.txt"""						) or os.path.exists(F"""{name}_privkey.txt"""						):
												print('\nWARNING:'						)
												print(
												    F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    'Use a different name or delete these files and re-run this program.'						)
												sys.exit()

						UpperCAmelCase				,   UpperCAmelCase					=			generate_key(lowercase_						)
						print(F"""\nWriting public key to file {name}_pubkey.txt..."""						)
						with open(F"""{name}_pubkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""						)

						print(F"""Writing private key to file {name}_privkey.txt..."""						)
						with open(F"""{name}_privkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{private_key[0]},{private_key[1]}"""						)

def 			_lowerCAmelCase				(       ):
						print('Making key files...'						)
						make_key_files('elgamal'		,     2048						)
						print('Key files generation successful'						)


if __name__ == "__main__":
				main()





