





"""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  a_       (							_lowercase     ):
						_UpperCamelCase    :      List[str]						     =					'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

						with open(_lowercase ,		'''wb'''     ) as f:
												with requests.get(_lowercase ,		stream=_lowercase     ) as req:
																		for chunk in req.iter_content(chunk_size=2048     ):
																								f.write(_lowercase     )





def  a_       (							):
						_UpperCamelCase    :      Any						     =					VivitConfig()

						_UpperCamelCase    :      Optional[Any]						     =					400
						_UpperCamelCase    :      Dict						     =					'''huggingface/label-files'''
						_UpperCamelCase    :      int						     =					'''kinetics400-id2label.json'''

						_UpperCamelCase    :      str						     =					json.load(open(hf_hub_download(_lowercase ,		_lowercase ,		repo_type='''dataset'''     ) ,		'''r'''     )     )
						_UpperCamelCase    :      List[Any]						     =					{int(_lowercase     ): v for k, v in idalabel.items()}
						_UpperCamelCase    :      Optional[Any]						     =					idalabel
						_UpperCamelCase    :      Any						     =					{v: k for k, v in idalabel.items()}
						return config





def  a_       (							):
						_UpperCamelCase    :      List[str]						     =					hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''' ,		filename='''eating_spaghetti_32_frames.npy''' ,		repo_type='''dataset'''     )
						_UpperCamelCase    :      Tuple						     =					np.load(_lowercase     )
						return list(_lowercase     )





def  a_       (							_lowercase     ):
						if np.ndim(_lowercase     ) == 2:
												return transform_attention_bias(_lowercase     )

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

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





def  a_       (							_lowercase     ):
						return current.flatten()





def  a_       (							_lowercase     ):
						return np.reshape(_lowercase ,		(current.shape[0], current.shape[1] * current.shape[2])     ).T





def  a_       (							_lowercase     ):
						return np.reshape(_lowercase ,		(current.shape[0] * current.shape[1], current.shape[2])     ).T





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Any						     =					state_dict['''optimizer''']['''target''']['''Transformer'''][F"""encoderblock_{i}"""]

						_UpperCamelCase    :      Dict						     =					F"""encoder.layer.{i}."""
						_UpperCamelCase    :      int						     =					{
						    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  a_       (							_lowercase     ):
						return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )





def  a_       (							_lowercase ,		_lowercase=False     ):
						_UpperCamelCase    :      int						     =					get_n_layers(_lowercase     )

						_UpperCamelCase    :      Optional[Any]						     =					OrderedDict()

						_UpperCamelCase    :      Union[str, Any]						     =					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
						_UpperCamelCase    :      Optional[Any]						     =					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

						_UpperCamelCase    :      int						     =					np.transpose(
						    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel'''] ,		(4, 3, 0, 1, 2)     )
						_UpperCamelCase    :      List[str]						     =					state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

						_UpperCamelCase    :      int						     =					state_dict['''optimizer''']['''target''']['''cls''']
						_UpperCamelCase    :      List[Any]						     =					state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
						    '''pos_embedding'''
						]

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

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

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





def  a_       (							):
						_UpperCamelCase    :      List[str]						     =					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  a_       (							_lowercase     ):
						_UpperCamelCase    :      Optional[Any]						     =					'''checkpoint'''

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

						_UpperCamelCase    :      Union[str, Any]						     =					restore_checkpoint(_lowercase ,		_lowercase     )
						_UpperCamelCase    :      Tuple						     =					transform_state(_lowercase ,		classification_head=_lowercase     )

						_UpperCamelCase    :      Union[str, Any]						     =					get_vivit_config()

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

						_UpperCamelCase    :      str						     =					VivitForVideoClassification(_lowercase     )
						model.load_state_dict(_lowercase     )
						model.eval()

						_UpperCamelCase    :      List[str]						     =					get_processor()

						_UpperCamelCase    :      int						     =					prepare_video()
						_UpperCamelCase    :      Any						     =					extractor(_lowercase ,		return_tensors='''pt'''     )

						_UpperCamelCase    :      int						     =					model(**_lowercase     )

						_UpperCamelCase    :      Dict						     =					torch.Size([1, 400]     )
						_UpperCamelCase    :      Any						     =					torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]     )

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

						model.save_pretrained(_lowercase     )
						extractor.save_pretrained(_lowercase     )


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

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

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










"""simple docstring"""




UpperCamelCase_     =1_000





def  a_       (							_lowercase     ):
						return x * x





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase = 4     ):
						assert callable(
						    _lowercase     ), F"""the function(object) passed should be callable your input : {function}"""
						assert isinstance(_lowercase ,		(float, int)     ), F"""a should be float or integer your input : {a}"""
						assert isinstance(function(_lowercase     ) ,		(float, int)     ), (
						    "the function should return integer or float return type of your function, "
						    F"""{type(_lowercase     )}"""
						)
						assert isinstance(_lowercase ,		(float, int)     ), F"""b should be float or integer your input : {b}"""
						assert (
						    isinstance(_lowercase ,		_lowercase     ) and precision > 0
						), F"""precision should be positive integer your input : {precision}"""

						# just applying the formula of simpson for approximate integration written in
						# mentioned article in first comment of this file and above this function

						_UpperCamelCase    :      str						     =					(b - a) / N_STEPS
						_UpperCamelCase    :      Dict						     =					function(_lowercase     ) + function(_lowercase     )

						for i in range(1 ,		_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					a + h * i
												result += function(_lowercase     ) * (4 if i % 2 else 2)

						result *= h / 3
						return round(_lowercase ,		_lowercase     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



