


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__       (							A__							):
			SCREAMING_SNAKE_CASE_						:			int       						= '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_0_4_8							):
												f.write(A__							)






def 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			int       						= VivitConfig()

			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 4_0_0
			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 'huggingface/label-files'
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 'kinetics400-id2label.json'

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






def 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= hf_hub_download(
			    repo_id='hf-internal-testing/spaghetti-video',	filename='eating_spaghetti_32_frames.npy',	repo_type='dataset'							)
			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= np.load(A__							)
			return list(A__							)






def 		a__       (							A__							):
			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 		a__       (							A__							):
			return current.flatten()






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






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






def 		a__       (							A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= state_dict['optimizer']['target']['Transformer'][F'''encoderblock_{i}''']

			SCREAMING_SNAKE_CASE_						:			Tuple       						= F'''encoder.layer.{i}.'''
			SCREAMING_SNAKE_CASE_						:			Dict       						= {
			    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__       (							A__							):
			return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]							)






def 		a__       (							A__,	A__=False							):
			SCREAMING_SNAKE_CASE_						:			Dict       						= get_n_layers(A__							)

			SCREAMING_SNAKE_CASE_						:			str       						= OrderedDict()

			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= state_dict['optimizer']['target']['cls']
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 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:
						SCREAMING_SNAKE_CASE_						:			Any       						= {'vivit.' + k: v for k, v in new_state.items()}
						SCREAMING_SNAKE_CASE_						:			Dict       						= np.transpose(state_dict['optimizer']['target']['output_projection']['kernel']							)
						SCREAMING_SNAKE_CASE_						:			str       						= np.transpose(state_dict['optimizer']['target']['output_projection']['bias']							)

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






def 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			Tuple       						= VivitImageProcessor()

			assert extractor.do_resize is True
			assert extractor.size == {"shortest_edge": 2_5_6}
			assert extractor.do_center_crop is True
			assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
			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_5_5

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

			return extractor






def 		a__       (							A__							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 'checkpoint'

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

			SCREAMING_SNAKE_CASE_						:			str       						= restore_checkpoint(A__,	A__							)
			SCREAMING_SNAKE_CASE_						:			str       						= transform_state(A__,	classification_head=A__							)

			SCREAMING_SNAKE_CASE_						:			str       						= get_vivit_config()

			assert config.image_size == 2_2_4
			assert config.num_frames == 3_2

			SCREAMING_SNAKE_CASE_						:			List[Any]       						= VivitForVideoClassification(A__							)
			model.load_state_dict(A__							)
			model.eval()

			SCREAMING_SNAKE_CASE_						:			Tuple       						= get_processor()

			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= prepare_video()
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= extractor(A__,	return_tensors='pt'							)

			SCREAMING_SNAKE_CASE_						:			Any       						= model(**A__							)

			SCREAMING_SNAKE_CASE_						:			List[str]       						= torch.Size([1, 4_0_0]							)
			SCREAMING_SNAKE_CASE_						:			Any       						= torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]							)

			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__       :	Any     				=argparse.ArgumentParser()

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

				lowerCAmelCase__       :	Optional[Any]     				=parser.parse_args()
				convert(args.output_model_name)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__       :	List[Any]     				={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Optional[Any]     				=['MBart50Tokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Any     				=['MBart50TokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__       :	Optional[int]     				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





