



"""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 				lowerCAmelCase_(      lowercase_   :							Tuple							)		->				Tuple:
				_lowerCamelCase					     = '''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=20_48							):
																f.write(lowercase_							)



def 				lowerCAmelCase_(      )		->				VivitConfig:
				_lowerCamelCase					     = VivitConfig()

				_lowerCamelCase					     = 4_00
				_lowerCamelCase					     = '''huggingface/label-files'''
				_lowerCamelCase					     = '''kinetics400-id2label.json'''

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



def 				lowerCAmelCase_(      )		->				List[Any]:
				_lowerCamelCase					     = hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video'''  ,      filename='''eating_spaghetti_32_frames.npy'''  ,      repo_type='''dataset'''							)
				_lowerCamelCase					     = np.load(lowercase_							)
				return list(lowercase_							)



def 				lowerCAmelCase_(      lowercase_   :							Any							)		->				Union[str, Any]:
				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 				lowerCAmelCase_(      lowercase_   :							Optional[Any]							)		->				Optional[int]:
				return current.flatten()



def 				lowerCAmelCase_(      lowercase_   :							Optional[Any]							)		->				Optional[Any]:
				return np.reshape(lowercase_  ,      (current.shape[0], current.shape[1] * current.shape[2])							).T



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				Union[str, Any]:
				return np.reshape(lowercase_  ,      (current.shape[0] * current.shape[1], current.shape[2])							).T



def 				lowerCAmelCase_(      lowercase_   :							Optional[Any]  ,      lowercase_   :							Optional[Any]							)		->				List[Any]:
				_lowerCamelCase					     = state_dict['''optimizer''']['''target''']['''Transformer'''][F"""encoderblock_{i}"""]

				_lowerCamelCase					     = F"""encoder.layer.{i}."""
				_lowerCamelCase					     = {
				    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 				lowerCAmelCase_(      lowercase_   :							Union[str, Any]							)		->				int:
				return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]							)



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							str=False							)		->				Optional[Any]:
				_lowerCamelCase					     = get_n_layers(lowercase_							)

				_lowerCamelCase					     = OrderedDict()

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

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

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

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



def 				lowerCAmelCase_(      )		->				VivitImageProcessor:
				_lowerCamelCase					     = VivitImageProcessor()

				assert extractor.do_resize is True
				assert extractor.size == {"shortest_edge": 2_56}
				assert extractor.do_center_crop is True
				assert extractor.crop_size == {"width": 2_24, "height": 2_24}
				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 / 2_55

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

				return extractor



def 				lowerCAmelCase_(      lowercase_   :							Union[str, Any]							)		->				str:
				_lowerCamelCase					     = '''checkpoint'''

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

				_lowerCamelCase					     = restore_checkpoint(lowercase_  ,      lowercase_							)
				_lowerCamelCase					     = transform_state(lowercase_  ,      classification_head=lowercase_							)

				_lowerCamelCase					     = get_vivit_config()

				assert config.image_size == 2_24
				assert config.num_frames == 32

				_lowerCamelCase					     = VivitForVideoClassification(lowercase_							)
				model.load_state_dict(lowercase_							)
				model.eval()

				_lowerCamelCase					     = get_processor()

				_lowerCamelCase					     = prepare_video()
				_lowerCamelCase					     = extractor(lowercase_  ,      return_tensors='''pt'''							)

				_lowerCamelCase					     = model(**lowercase_							)

				_lowerCamelCase					     = torch.Size([1, 4_00]							)
				_lowerCamelCase					     = 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]  ,      lowercase_  ,      atol=1e-4							), outputs.logits[0, :5]

				model.save_pretrained(lowercase_							)
				extractor.save_pretrained(lowercase_							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					argparse.ArgumentParser()

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

			__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					parser.parse_args()
			convert(args.output_model_name)







'''simple docstring'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class     _UpperCAmelCase							(						unittest.TestCase   ):
		@patch("""diffusers.utils.hub_utils.get_full_repo_name"""			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""full_repo_name"""
					with TemporaryDirectory() as tmpdir:
								# Dummy args values
								__lowerCAmelCase											=				Mock()
								__lowerCAmelCase											=				tmpdir
								__lowerCAmelCase											=				0
								__lowerCAmelCase											=				"""hub_token"""
								__lowerCAmelCase											=				"""dataset_name"""
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				10_00_00
								__lowerCAmelCase											=				1_00_00
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				0.02
								__lowerCAmelCase											=				0.03
								__lowerCAmelCase											=				0.0005
								__lowerCAmelCase											=				0.00_0001
								__lowerCAmelCase											=				1
								__lowerCAmelCase											=				10
								__lowerCAmelCase											=				0.001
								__lowerCAmelCase											=				0.1
								__lowerCAmelCase											=				0.2
								__lowerCAmelCase											=				True

								# Model card mush be rendered and saved
								diffusers.utils.hub_utils.create_model_card(__SCREAMING_SNAKE_CASE,model_name="""model_name"""			)
								self.assertTrue((Path(__SCREAMING_SNAKE_CASE			) / """README.md""").is_file()			)

