


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__					(     a__:						Any			)    ->						Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     VideoMAEConfig()

							set_architecture_configs(a__     ,    a__			)

							if "finetuned" not in model_name:
														_UpperCAmelCase											=     False

							if "finetuned" in model_name:
														_UpperCAmelCase											=     'huggingface/label-files'
														if "kinetics" in model_name:
																					_UpperCAmelCase											=     4_0_0
																					_UpperCAmelCase											=     'kinetics400-id2label.json'
														elif "ssv2" in model_name:
																					_UpperCAmelCase											=     1_7_4
																					_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(a__     ,    a__     ,    repo_type='dataset'			)     ,    'r'			)			)
														_UpperCAmelCase											=     {int(a__			): v for k, v in idalabel.items()}
														_UpperCAmelCase											=     idalabel
														_UpperCAmelCase											=     {v: k for k, v in idalabel.items()}

							return config

def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						List[Any]			)    ->						Union[str, Any]:


							'''simple docstring'''





							if "small" in model_name:
														_UpperCAmelCase											=     3_8_4
														_UpperCAmelCase											=     1_5_3_6
														_UpperCAmelCase											=     1_2
														_UpperCAmelCase											=     1_6
														_UpperCAmelCase											=     1_2
														_UpperCAmelCase											=     3
														_UpperCAmelCase											=     1_9_2
														_UpperCAmelCase											=     7_6_8
							elif "large" in model_name:
														_UpperCAmelCase											=     1_0_2_4
														_UpperCAmelCase											=     4_0_9_6
														_UpperCAmelCase											=     2_4
														_UpperCAmelCase											=     1_6
														_UpperCAmelCase											=     1_2
														_UpperCAmelCase											=     8
														_UpperCAmelCase											=     5_1_2
														_UpperCAmelCase											=     2_0_4_8
							elif "huge" in model_name:
														_UpperCAmelCase											=     1_2_8_0
														_UpperCAmelCase											=     5_1_2_0
														_UpperCAmelCase											=     3_2
														_UpperCAmelCase											=     1_6
														_UpperCAmelCase											=     1_2
														_UpperCAmelCase											=     8
														_UpperCAmelCase											=     6_4_0
														_UpperCAmelCase											=     2_5_6_0
							elif "base" not in model_name:
														raise ValueError('Model name should include either "small", "base", "large", or "huge"'			)

def 			lowerCAmelCase__					(     a__:						Union[str, Any]			)    ->						Tuple:


							'''simple docstring'''





							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__					(     a__:						Optional[int]     ,    a__:						Optional[Any]			)    ->						str:


							'''simple docstring'''





							for key in orig_state_dict.copy().keys():
														_UpperCAmelCase											=     orig_state_dict.pop(a__			)

														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__					(     )    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase											=     hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video'     ,    filename='eating_spaghetti.npy'     ,    repo_type='dataset'			)
							_UpperCAmelCase											=     np.load(a__			)
							return list(a__			)

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Optional[Any]     ,    a__:						Optional[int]     ,    a__:						List[str]			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     get_videomae_config(a__			)

							if "finetuned" in model_name:
														_UpperCAmelCase											=     VideoMAEForVideoClassification(a__			)
							else:
														_UpperCAmelCase											=     VideoMAEForPreTraining(a__			)

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

							model.load_state_dict(a__			)
							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(a__     ,    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(a__			)

							_UpperCAmelCase											=     model(**a__			)
							_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, 4_0_0]			)
														_UpperCAmelCase											=     torch.tensor([-0.9_291, -0.4_061, -0.9_307]			)
							elif model_name == "videomae-small-finetuned-ssv2":
														_UpperCAmelCase											=     torch.Size([1, 1_7_4]			)
														_UpperCAmelCase											=     torch.tensor([0.2_671, -0.4_689, -0.8_235]			)
							elif model_name == "videomae-base":
														_UpperCAmelCase											=     torch.Size([1, 1_4_0_8, 1_5_3_6]			)
														_UpperCAmelCase											=     torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]			)
							elif model_name == "videomae-base-short":
														_UpperCAmelCase											=     torch.Size([1, 1_4_0_8, 1_5_3_6]			)
														_UpperCAmelCase											=     torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]			)
														# we verified the loss both for normalized and unnormalized targets for this one
														_UpperCAmelCase											=     torch.tensor([0.5_142]			) if config.norm_pix_loss else torch.tensor([0.6_469]			)
							elif model_name == "videomae-large":
														_UpperCAmelCase											=     torch.Size([1, 1_4_0_8, 1_5_3_6]			)
														_UpperCAmelCase											=     torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]			)
							elif model_name == "videomae-large-finetuned-kinetics":
														_UpperCAmelCase											=     torch.Size([1, 4_0_0]			)
														_UpperCAmelCase											=     torch.tensor([0.0_771, 0.0_011, -0.3_625]			)
							elif model_name == "videomae-huge-finetuned-kinetics":
														_UpperCAmelCase											=     torch.Size([1, 4_0_0]			)
														_UpperCAmelCase											=     torch.tensor([0.2_433, 0.1_632, -0.4_894]			)
							elif model_name == "videomae-base-short-finetuned-kinetics":
														_UpperCAmelCase											=     torch.Size([1, 4_0_0]			)
														_UpperCAmelCase											=     torch.tensor([0.6_588, 0.0_990, -0.2_493]			)
							elif model_name == "videomae-base-finetuned-kinetics":
														_UpperCAmelCase											=     torch.Size([1, 4_0_0]			)
														_UpperCAmelCase											=     torch.tensor([0.3_669, -0.0_688, -0.2_421]			)
							elif model_name == "videomae-base-short-ssv2":
														_UpperCAmelCase											=     torch.Size([1, 1_4_0_8, 1_5_3_6]			)
														_UpperCAmelCase											=     torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]			)
							elif model_name == "videomae-base-short-finetuned-ssv2":
														_UpperCAmelCase											=     torch.Size([1, 1_7_4]			)
														_UpperCAmelCase											=     torch.tensor([-0.0_537, -0.1_539, -0.3_266]			)
							elif model_name == "videomae-base-ssv2":
														_UpperCAmelCase											=     torch.Size([1, 1_4_0_8, 1_5_3_6]			)
														_UpperCAmelCase											=     torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]			)
							elif model_name == "videomae-base-finetuned-ssv2":
														_UpperCAmelCase											=     torch.Size([1, 1_7_4]			)
														_UpperCAmelCase											=     torch.tensor([0.1_961, -0.8_337, -0.6_389]			)
							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]     ,    a__     ,    atol=1e-4			)
							else:
														print('Logits:'     ,    logits[0, :3, :3]			)
														assert torch.allclose(logits[0, :3, :3]     ,    a__     ,    atol=1e-4			)
							print('Logits ok!'			)

							# verify loss, if applicable
							if model_name == "videomae-base-short":
														_UpperCAmelCase											=     outputs.loss
														assert torch.allclose(a__     ,    a__     ,    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(a__			)
														model.save_pretrained(a__			)

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


if __name__ == "__main__":
		lowerCAmelCase__    :List[str]       =				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.'''
		)

		lowerCAmelCase__    :int       =				parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




import math

import qiskit

def 			lowerCAmelCase__					(     a__:						int = 1     ,    a__:						int = 1     ,    a__:						int = 1			)    ->						qiskit.result.counts.Counts:


							'''simple docstring'''





							if (
							    isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							):
														raise TypeError('inputs must be integers.'			)

							if (input_a < 0) or (input_a < 0) or (carry_in < 0):
														raise ValueError('inputs must be positive.'			)

							if (
							    (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != carry_in)
							):
														raise ValueError('inputs must be exact integers.'			)

							if (input_a > 2) or (input_a > 2) or (carry_in > 2):
														raise ValueError('inputs must be less or equal to 2.'			)

							# build registers
							_UpperCAmelCase											=     qiskit.QuantumRegister(4     ,    'qr'			)
							_UpperCAmelCase											=     qiskit.ClassicalRegister(2     ,    'cr'			)
							# list the entries
							_UpperCAmelCase											=     [input_a, input_a, carry_in]

							_UpperCAmelCase											=     qiskit.QuantumCircuit(a__     ,    a__			)

							for i in range(0     ,    3			):
														if entry[i] == 2:
																					quantum_circuit.h(a__			)  # for hadamard entries
														elif entry[i] == 1:
																					quantum_circuit.x(a__			)  # for 1 entries
														elif entry[i] == 0:
																					quantum_circuit.i(a__			)  # for 0 entries

    # build the circuit
							quantum_circuit.ccx(0     ,    1     ,    3			)  # ccx = toffoli gate
							quantum_circuit.cx(0     ,    1			)
							quantum_circuit.ccx(1     ,    2     ,    3			)
							quantum_circuit.cx(1     ,    2			)
							quantum_circuit.cx(0     ,    1			)

							quantum_circuit.measure([2, 3]     ,    a__			)  # measure the last two qbits

							_UpperCAmelCase											=     qiskit.Aer.get_backend('aer_simulator'			)
							_UpperCAmelCase											=     qiskit.execute(a__     ,    a__     ,    shots=1_0_0_0			)

							return job.result().get_counts(a__			)


if __name__ == "__main__":
		print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
