


"""simple docstring"""







import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__a			 =   False
__a			 =   Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__a			 =   datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class 							lowerCamelCase       :


							"""simple docstring"""






							def __init__(						self :     Optional[Any]		, lowerCamelCase_ :     List[str]=sys.argv[1:]				):
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    1
													UpperCAmelCase__	,				UpperCAmelCase__		=    getopt.getopt(lowerCamelCase_		, '''i:o:b:s'''		, ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']				)
													for opt, arg in opts:
																			if opt in ("-i", "--inputdir"):
																									UpperCAmelCase__		=    arg
																			elif opt in ("-o", "--outfile"):
																									UpperCAmelCase__		=    arg
																			elif opt in ("-b", "--batch_size"):
																									UpperCAmelCase__		=    int(lowerCamelCase_				)
																			elif opt in ("-s", "--subset_list"):
																									UpperCAmelCase__		=    arg

													assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
													assert outputfile is not None and not os.path.isfile(lowerCamelCase_				), f"""{outputfile}"""
													if subset_list is not None:
																			with open(os.path.realpath(lowerCamelCase_				)				) as f:
																									UpperCAmelCase__		=    {self._vqa_file_split()[0] for x in tryload(lowerCamelCase_				)}
													else:
																			UpperCAmelCase__		=    None

													UpperCAmelCase__		=    CONFIG
													if torch.cuda.is_available():
																			UpperCAmelCase__		=    '''cuda'''
													UpperCAmelCase__		=    os.path.realpath(lowerCamelCase_				)
													UpperCAmelCase__		=    os.path.realpath(lowerCamelCase_				)
													UpperCAmelCase__		=    Preprocess(self.config				)
													UpperCAmelCase__		=    GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''		, config=self.config				)
													UpperCAmelCase__		=    batch_size if batch_size != 0 else 1
													UpperCAmelCase__		=    DEFAULT_SCHEMA






							def 					lowerCAmelCase     (						self :     str		, lowerCamelCase_ :     Any				):
													UpperCAmelCase__		=    int(file.split('''.'''				)[0].split('''_'''				)[-1]				)
													UpperCAmelCase__		=    os.path.join(self.inputdir		, lowerCamelCase_				)
													return (img_id, filepath)






							@property
							def 					lowerCAmelCase     (						self :     int				):
													UpperCAmelCase__		=    []
													for i, file in enumerate(os.listdir(self.inputdir				)				):
																			if self.subset_list is not None and i not in self.subset_list:
																									continue
																			batch.append(self._vqa_file_split(lowerCamelCase_				)				)
																			if len(lowerCamelCase_				) == self.batch:
																									UpperCAmelCase__		=    batch
																									UpperCAmelCase__		=    []
																									yield list(map(lowerCamelCase_		, zip(*lowerCamelCase_				)				)				)

													for i in range(1				):
																			yield list(map(lowerCamelCase_		, zip(*lowerCamelCase_				)				)				)






							def __call__(						self :     int				):
													# make writer
													if not TEST:
																			UpperCAmelCase__		=    datasets.ArrowWriter(features=self.schema		, path=self.outputfile				)
													# do file generator
													for i, (img_ids, filepaths) in enumerate(self.file_generator				):
																			UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    self.preprocess(lowerCamelCase_				)
																			UpperCAmelCase__		=    self.model(
																			    lowerCamelCase_		, lowerCamelCase_		, scales_yx=lowerCamelCase_		, padding='''max_detections'''		, max_detections=self.config.MAX_DETECTIONS		, pad_value=0		, return_tensors='''np'''		, location='''cpu'''		, )
																			UpperCAmelCase__		=    output_dict.pop('''normalized_boxes'''				)
																			if not TEST:
																									UpperCAmelCase__		=    np.array(lowerCamelCase_				)
																									UpperCAmelCase__		=    self.schema.encode_batch(lowerCamelCase_				)
																									writer.write_batch(lowerCamelCase_				)
																			if TEST:
																									break
																			# finalizer the writer
													if not TEST:
																			UpperCAmelCase__	,				UpperCAmelCase__		=    writer.finalize()
																			print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""				)






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						try:
												UpperCAmelCase__		=    json.load(_UpperCamelCase )
												try:
																		UpperCAmelCase__		=    list(data.keys() )
												except Exception:
																		UpperCAmelCase__		=    [d['''img_id'''] for d in data]
						except Exception:
												try:
																		UpperCAmelCase__		=    eval(stream.read() )
												except Exception:
																		UpperCAmelCase__		=    stream.read().split('''\n''' )
						return data


if __name__ == "__main__":
						__a			 =   Extract(sys.argv[1:])
						extract()
						if not TEST:
												__a			 =   datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)



"""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__ (       _UpperCamelCase ):





						"""simple docstring"""

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

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






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitConfig()

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

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






def 					a__ (       ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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

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

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return current.flatten()






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=False ):





						"""simple docstring"""

						UpperCAmelCase__		=    get_n_layers(_UpperCamelCase )

						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(_UpperCamelCase ):
												new_state.update(transform_state_encoder_block(_UpperCamelCase	,			_UpperCamelCase ) )

						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(_UpperCamelCase ) for k, v in new_state.items()}






def 					a__ (       ):





						"""simple docstring"""

						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 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''checkpoint'''

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

						UpperCAmelCase__		=    restore_checkpoint(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    transform_state(_UpperCamelCase	,			classification_head=_UpperCamelCase )

						UpperCAmelCase__		=    get_vivit_config()

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

						UpperCAmelCase__		=    VivitForVideoClassification(_UpperCamelCase )
						model.load_state_dict(_UpperCamelCase )
						model.eval()

						UpperCAmelCase__		=    get_processor()

						UpperCAmelCase__		=    prepare_video()
						UpperCAmelCase__		=    extractor(_UpperCamelCase	,			return_tensors='''pt''' )

						UpperCAmelCase__		=    model(**_UpperCamelCase )

						UpperCAmelCase__		=    torch.Size([1, 400] )
						UpperCAmelCase__		=    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]	,			_UpperCamelCase	,			atol=1E-4 ), outputs.logits[0, :5]

						model.save_pretrained(_UpperCamelCase )
						extractor.save_pretrained(_UpperCamelCase )


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

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

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