



"""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_		):
						UpperCAmelCase    =				'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 				_lowerCAmelCase   (     ):
						UpperCAmelCase    =				VivitConfig()

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

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

def 				_lowerCAmelCase   (     ):
						UpperCAmelCase    =				hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'	,			filename='eating_spaghetti_32_frames.npy'	,			repo_type='dataset'		)
						UpperCAmelCase    =				np.load(lowercase_		)
						return list(lowercase_		)

def 				_lowerCAmelCase   (     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 				_lowerCAmelCase   (     lowercase_		):
						return current.flatten()

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

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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				state_dict['optimizer']['target']['Transformer'][F"""encoderblock_{i}"""]

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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=False		):
						UpperCAmelCase    =				get_n_layers(lowercase_		)

						UpperCAmelCase    =				OrderedDict()

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

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

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

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

def 				_lowerCAmelCase   (     ):
						UpperCAmelCase    =				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 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				'checkpoint'

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

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

						UpperCAmelCase    =				get_vivit_config()

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

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

						UpperCAmelCase    =				get_processor()

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

						UpperCAmelCase    =				model(**lowercase_		)

						UpperCAmelCase    =				torch.Size([1, 400]		)
						UpperCAmelCase    =				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__":
				snake_case_         = argparse.ArgumentParser()

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

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










"""simple docstring"""


import warnings

from .generation import FlaxGenerationMixin







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						# warning at import time
						warnings.warn(
						    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
						    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""			,     SCREAMING_SNAKE_CASE_			,     )





