"""simple docstring"""




import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

							with open(a__							,     'wb'					) as f:
														with requests.get(a__							,     stream=a__					) as req:
																					for chunk in req.iter_content(chunk_size=2_048					):
																												f.write(a__					)






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				VivitConfig()

							A__						  =				400
							A__						  =				'huggingface/label-files'
							A__						  =				'kinetics400-id2label.json'

							A__						  =				json.load(open(hf_hub_download(a__							,     a__							,     repo_type='dataset'					)							,     'r'					)					)
							A__						  =				{int(a__					): v for k, v in idalabel.items()}
							A__						  =				idalabel
							A__						  =				{v: k for k, v in idalabel.items()}
							return config






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video'							,     filename='eating_spaghetti_32_frames.npy'							,     repo_type='dataset'					)
							A__						  =				np.load(a__					)
							return list(a__					)






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							if np.ndim(a__					) == 2:
														return transform_attention_bias(a__					)

							elif np.ndim(a__					) == 3:
														return transform_attention_kernel(a__					)

							else:
														raise Exception(F'''Invalid number of dimesions: {np.ndim(a__					)}'''					)






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							return current.flatten()






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							return np.reshape(a__							,     (current.shape[0], current.shape[1] * current.shape[2])					).T






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							return np.reshape(a__							,     (current.shape[0] * current.shape[1], current.shape[2])					).T






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				state_dict['optimizer']['target']['Transformer'][F'''encoderblock_{i}''']

							A__						  =				F'''encoder.layer.{i}.'''
							A__						  =				{
							    prefix + 'intermediate.dense.bias': state['MlpBlock_0']['Dense_0']['bias'],
							    prefix + 'intermediate.dense.weight': np.transpose(state['MlpBlock_0']['Dense_0']['kernel']					),
							    prefix + 'output.dense.bias': state['MlpBlock_0']['Dense_1']['bias'],
							    prefix + 'output.dense.weight': np.transpose(state['MlpBlock_0']['Dense_1']['kernel']					),
							    prefix + 'layernorm_before.bias': state['LayerNorm_0']['bias'],
							    prefix + 'layernorm_before.weight': state['LayerNorm_0']['scale'],
							    prefix + 'layernorm_after.bias': state['LayerNorm_1']['bias'],
							    prefix + 'layernorm_after.weight': state['LayerNorm_1']['scale'],
							    prefix
							    + 'attention.attention.query.bias': transform_attention(
							        state['MultiHeadDotProductAttention_0']['query']['bias']					),
							    prefix
							    + 'attention.attention.query.weight': transform_attention(
							        state['MultiHeadDotProductAttention_0']['query']['kernel']					),
							    prefix
							    + 'attention.attention.key.bias': transform_attention(state['MultiHeadDotProductAttention_0']['key']['bias']					),
							    prefix
							    + 'attention.attention.key.weight': transform_attention(
							        state['MultiHeadDotProductAttention_0']['key']['kernel']					),
							    prefix
							    + 'attention.attention.value.bias': transform_attention(
							        state['MultiHeadDotProductAttention_0']['value']['bias']					),
							    prefix
							    + 'attention.attention.value.weight': transform_attention(
							        state['MultiHeadDotProductAttention_0']['value']['kernel']					),
							    prefix + 'attention.output.dense.bias': state['MultiHeadDotProductAttention_0']['out']['bias'],
							    prefix
							    + 'attention.output.dense.weight': transform_attention_output_weight(
							        state['MultiHeadDotProductAttention_0']['out']['kernel']					),
							}

							return new_state






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=False					):






							"""simple docstring"""





							A__						  =				get_n_layers(a__					)

							A__						  =				OrderedDict()

							A__						  =				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
							A__						  =				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

							A__						  =				np.transpose(
							    state_dict['optimizer']['target']['embedding']['kernel']							,     (4, 3, 0, 1, 2)					)
							A__						  =				state_dict['optimizer']['target']['embedding']['bias']

							A__						  =				state_dict['optimizer']['target']['cls']
							A__						  =				state_dict['optimizer']['target']['Transformer']['posembed_input'][
							    'pos_embedding'
							]

							for i in range(a__					):
														new_state.update(transform_state_encoder_block(a__							,     a__					)					)

							if classification_head:
														A__						  =				{'vivit.' + k: v for k, v in new_state.items()}
														A__						  =				np.transpose(state_dict['optimizer']['target']['output_projection']['kernel']					)
														A__						  =				np.transpose(state_dict['optimizer']['target']['output_projection']['bias']					)

							return {k: torch.tensor(a__					) for k, v in new_state.items()}






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				VivitImageProcessor()

							assert extractor.do_resize is True
							assert extractor.size == {"shortest_edge": 256}
							assert extractor.do_center_crop is True
							assert extractor.crop_size == {"width": 224, "height": 224}
							assert extractor.resample == PILImageResampling.BILINEAR

							# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
							# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
							# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
							assert extractor.do_normalize is False
							assert extractor.do_rescale is True
							assert extractor.rescale_factor == 1 / 255

							# zero-centering = True in original implementation
							assert extractor.do_zero_centering is True

							return extractor






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				'checkpoint'

							if not os.path.exists(a__					):
														download_checkpoint(a__					)

							A__						  =				restore_checkpoint(a__							,     a__					)
							A__						  =				transform_state(a__							,     classification_head=a__					)

							A__						  =				get_vivit_config()

							assert config.image_size == 224
							assert config.num_frames == 32

							A__						  =				VivitForVideoClassification(a__					)
							model.load_state_dict(a__					)
							model.eval()

							A__						  =				get_processor()

							A__						  =				prepare_video()
							A__						  =				extractor(a__							,     return_tensors='pt'					)

							A__						  =				model(**a__					)

							A__						  =				torch.Size([1, 400]					)
							A__						  =				torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]					)

							assert outputs.logits.shape == expected_shape
							assert torch.allclose(outputs.logits[0, :5]							,     a__							,     atol=1E-4					), outputs.logits[0, :5]

							model.save_pretrained(a__					)
							extractor.save_pretrained(a__					)


if __name__ == "__main__":
				__lowerCamelCase									=	argparse.ArgumentParser()

				parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

				__lowerCamelCase									=	parser.parse_args()
				convert(args.output_model_name)











'''simple docstring'''
from math import gcd
def 							snake_case_    (  a__						:      int = 1_20_00     ):



    """simple docstring"""




    __lowercase     							=		0
    for d in range(max_d + 1     ):
        for n in range(d // 3 + 1       ,(d + 1) // 2     ):
            if gcd(a__       ,a__     ) == 1:
                fractions_number += 1
    return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")




