


"""simple docstring"""







import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
						from comet_ml import OfflineExperiment

if is_tensorboard_available():
						import struct

						import tensorboard.compat.proto.event_pba as event_pba

__a			 =   logging.getLogger(__name__)
@require_tensorboard
class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






							def 					lowerCAmelCase     (						self :     List[str]				):
													UpperCAmelCase__		=    '''test_project_with_config'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(lowerCamelCase_		, lowerCamelCase_				)
																			accelerator.end_training()
																			for child in Path(f"""{dirpath}/{project_name}"""				).glob('''*/**'''				):
																									UpperCAmelCase__		=    list(filter(lambda lowerCamelCase_				: x.is_file()		, child.iterdir()				)				)[0]
																			self.assertNotEqual(str(lowerCamelCase_				)		, ''''''				)






							def 					lowerCAmelCase     (						self :     Any				):
													UpperCAmelCase__		=    '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_				)
																			accelerator.init_trackers(lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(lowerCamelCase_		, step=0				)
																			accelerator.end_training()
																			# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																			# Names are randomly generated each time
																			UpperCAmelCase__		=    list(filter(lambda lowerCamelCase_				: x.is_file()		, Path(f"""{dirpath}/{project_name}"""				).iterdir()				)				)[0]
																			self.assertNotEqual(str(lowerCamelCase_				)		, ''''''				)






							def 					lowerCAmelCase     (						self :     List[Any]				):
													UpperCAmelCase__		=    '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_				)
																			accelerator.init_trackers(lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''tensor''': torch.tensor(1				)}
																			accelerator.log(lowerCamelCase_		, step=0				)
																			accelerator.end_training()
																			# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																			# Names are randomly generated each time
																			UpperCAmelCase__		=    list(filter(lambda lowerCamelCase_				: x.is_file()		, Path(f"""{dirpath}/{project_name}"""				).iterdir()				)				)[0]
																			# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																			with open(lowerCamelCase_		, '''rb'''				) as f:
																									UpperCAmelCase__		=    f.read()
																			UpperCAmelCase__		=    False
																			while data:
																									UpperCAmelCase__		=    struct.unpack('''Q'''		, data[:8]				)

																									UpperCAmelCase__		=    data[12 : 12 + int(header[0]				)]  # 8+4
																									UpperCAmelCase__		=    data[12 + int(header[0]				) + 4 :]
																									UpperCAmelCase__		=    event_pba.Event()

																									event.ParseFromString(lowerCamelCase_				)
																									if event.HasField('''summary'''				):
																															for value in event.summary.value:
																																					if value.simple_value == 1.0 and value.tag == "tensor":
																																											UpperCAmelCase__		=    True
																			self.assertTrue(lowerCamelCase_		, '''Converted tensor was not found in the log file!'''				)






							def 					lowerCAmelCase     (						self :     int				):
													with self.assertRaisesRegex(lowerCamelCase_		, '''Logging with `tensorboard` requires a `logging_dir`'''				):
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''				)
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[int]				):
													UpperCAmelCase__		=    ProjectConfiguration(total_limit=30				)
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_		, project_config=lowerCamelCase_				)




@require_wandb
@mock.patch.dict(os.environ				,					{"""WANDB_MODE""": """offline"""} )
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""






							def 					lowerCAmelCase     (						self :     Dict				):
													super().setUp()
													# wandb let's us override where logs are stored to via the WANDB_DIR env var
													self.add_mocks(mock.patch.dict(os.environ		, {'''WANDB_DIR''': self.tmpdir}				)				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     str		, lowerCamelCase_ :     str		, lowerCamelCase_ :     bool = True				):
													UpperCAmelCase__		=    Rf"""{section} ([\S\s]*?)\n\n"""
													if record:
																			UpperCAmelCase__		=    Rf"""Record: {pattern}"""
													UpperCAmelCase__		=    re.findall(lowerCamelCase_		, lowerCamelCase_				)[0]
													# A config
													if section == "config" or section == "history":
																			UpperCAmelCase__		=    re.findall(R'''"([a-zA-Z0-9_.,]+)'''		, lowerCamelCase_				)
																			return {key: val for key, val in zip(cleaned_record[0::2]		, cleaned_record[1::2]				)}
													# Everything else
													else:
																			return dict(re.findall(R'''(\w+): "([^\s]+)"'''		, lowerCamelCase_				)				)






							@skip
							def 					lowerCAmelCase     (						self :     List[Any]				):
													UpperCAmelCase__		=    '''test_project_with_config'''
													UpperCAmelCase__		=    Accelerator(log_with='''wandb'''				)
													UpperCAmelCase__		=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
													UpperCAmelCase__		=    {'''wandb''': {'''tags''': ['''my_tag''']}}
													accelerator.init_trackers(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)
													UpperCAmelCase__		=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
													accelerator.log(lowerCamelCase_		, step=0				)
													accelerator.end_training()
													# The latest offline log is stored at wandb/latest-run/*.wandb
													for child in Path(f"""{self.tmpdir}/wandb/latest-run"""				).glob('''*'''				):
																			if child.is_file() and child.suffix == ".wandb":
																									UpperCAmelCase__		=    subprocess.check_output(
																									    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowerCamelCase_				)]		, env=os.environ.copy()				).decode('''utf8'''		, '''ignore'''				)
																									break

        # Check HPS through careful parsing and cleaning
													UpperCAmelCase__		=    self.parse_log(lowerCamelCase_		, '''config'''				)
													self.assertEqual(logged_items['''num_iterations''']		, '''12'''				)
													self.assertEqual(logged_items['''learning_rate''']		, '''0.01'''				)
													self.assertEqual(logged_items['''some_boolean''']		, '''false'''				)
													self.assertEqual(logged_items['''some_string''']		, '''some_value'''				)
													self.assertEqual(logged_items['''some_string''']		, '''some_value'''				)

													# Run tags
													UpperCAmelCase__		=    self.parse_log(lowerCamelCase_		, '''run'''		, lowerCamelCase_				)
													self.assertEqual(logged_items['''tags''']		, '''my_tag'''				)

													# Actual logging
													UpperCAmelCase__		=    self.parse_log(lowerCamelCase_		, '''history'''				)
													self.assertEqual(logged_items['''total_loss''']		, '''0.1'''				)
													self.assertEqual(logged_items['''iteration''']		, '''1'''				)
													self.assertEqual(logged_items['''my_text''']		, '''some_value'''				)
													self.assertEqual(logged_items['''_step''']		, '''0'''				)






def 					a__ (       self	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    run_name
						UpperCAmelCase__		=    OfflineExperiment(project_name=_UpperCamelCase	,			offline_directory=_UpperCamelCase )
						logger.info(F"""Initialized offline CometML project {self.run_name}""" )
						logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!''' )
@require_comet_ml
@mock.patch.object(__SCREAMING_SNAKE_CASE				,					"""__init__"""				,					__SCREAMING_SNAKE_CASE )
class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     Dict		, lowerCamelCase_ :     str		, lowerCamelCase_ :     bool = False				):
													for log in log_list:
																			UpperCAmelCase__		=    json.loads(lowerCamelCase_				)['''payload''']
																			if is_param and "param" in j.keys():
																									if j["param"]["paramName"] == key:
																															return j["param"]["paramValue"]
																			if "log_other" in j.keys():
																									if j["log_other"]["key"] == key:
																															return j["log_other"]["val"]
																			if "metric" in j.keys():
																									if j["metric"]["metricName"] == key:
																															return j["metric"]["metricValue"]






							def 					lowerCAmelCase     (						self :     List[Any]				):
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase__		=    CometMLTracker('''test_project_with_config'''		, lowerCamelCase_				)
																			UpperCAmelCase__		=    Accelerator(log_with=lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(lowerCamelCase_		, lowerCamelCase_				)
																			accelerator.end_training()
																			UpperCAmelCase__		=    os.listdir(lowerCamelCase_				)[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			UpperCAmelCase__		=    os.path.join(lowerCamelCase_		, lowerCamelCase_				)
																			UpperCAmelCase__		=    zipfile.ZipFile(lowerCamelCase_		, '''r'''				)
																			UpperCAmelCase__		=    archive.open('''messages.json'''				).read().decode('''utf-8'''				)
													UpperCAmelCase__		=    log.split('''\n'''				)[:-1]
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''num_iterations'''		, lowerCamelCase_				)		, 12				)
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''learning_rate'''		, lowerCamelCase_				)		, 0.01				)
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''some_boolean'''		, lowerCamelCase_				)		, lowerCamelCase_				)
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''some_string'''		, lowerCamelCase_				)		, '''some_value'''				)






							def 					lowerCAmelCase     (						self :     Union[str, Any]				):
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase__		=    CometMLTracker('''test_project_with_config'''		, lowerCamelCase_				)
																			UpperCAmelCase__		=    Accelerator(log_with=lowerCamelCase_				)
																			accelerator.init_trackers(lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(lowerCamelCase_		, step=0				)
																			accelerator.end_training()
																			UpperCAmelCase__		=    os.listdir(lowerCamelCase_				)[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			UpperCAmelCase__		=    os.path.join(lowerCamelCase_		, lowerCamelCase_				)
																			UpperCAmelCase__		=    zipfile.ZipFile(lowerCamelCase_		, '''r'''				)
																			UpperCAmelCase__		=    archive.open('''messages.json'''				).read().decode('''utf-8'''				)
													UpperCAmelCase__		=    log.split('''\n'''				)[:-1]
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''curr_step'''		, lowerCamelCase_				)		, 0				)
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''total_loss'''				)		, 0.1				)
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''iteration'''				)		, 1				)
													self.assertEqual(self.get_value_from_key(lowerCamelCase_		, '''my_text'''				)		, '''some_value'''				)




class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      [
							    """total_loss""",
							    """iteration""",
							    """my_text""",
							    """learning_rate""",
							    """num_iterations""",
							    """some_boolean""",
							    """some_string""",
							]

							SCREAMING_SNAKE_CASE										=      """my_custom_tracker"""
							SCREAMING_SNAKE_CASE										=      False






							def __init__(						self :     List[Any]		, lowerCamelCase_ :     str				):
													UpperCAmelCase__		=    open(f"""{dir}/log.csv"""		, '''w+'''				)
													UpperCAmelCase__		=    csv.DictWriter(self.f		, fieldnames=self._col_names				)
													self.writer.writeheader()






							@property
							def 					lowerCAmelCase     (						self :     Any				):
													return self.writer






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     dict				):
													logger.info('''Call init'''				)
													self.writer.writerow(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     dict		, lowerCamelCase_ :     Optional[int]				):
													logger.info('''Call log'''				)
													self.writer.writerow(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[Any]				):
													self.f.close()




class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






							def 					lowerCAmelCase     (						self :     str				):
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase__		=    MyCustomTracker(lowerCamelCase_				)
																			UpperCAmelCase__		=    Accelerator(log_with=lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers('''Some name'''		, lowerCamelCase_				)
																			accelerator.end_training()
																			with open(f"""{d}/log.csv"""		, '''r'''				) as f:
																									UpperCAmelCase__		=    csv.DictReader(lowerCamelCase_				)
																									UpperCAmelCase__		=    next(lowerCamelCase_				)
																									UpperCAmelCase__		=    {
																									    '''total_loss''': '''''',
																									    '''iteration''': '''''',
																									    '''my_text''': '''''',
																									    '''learning_rate''': '''0.01''',
																									    '''num_iterations''': '''12''',
																									    '''some_boolean''': '''False''',
																									    '''some_string''': '''some_value''',
																									}
																									self.assertDictEqual(lowerCamelCase_		, lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Union[str, Any]				):
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase__		=    MyCustomTracker(lowerCamelCase_				)
																			UpperCAmelCase__		=    Accelerator(log_with=lowerCamelCase_				)
																			accelerator.init_trackers('''Some name'''				)
																			UpperCAmelCase__		=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(lowerCamelCase_		, step=0				)
																			accelerator.end_training()
																			with open(f"""{d}/log.csv"""		, '''r'''				) as f:
																									UpperCAmelCase__		=    csv.DictReader(lowerCamelCase_				)
																									UpperCAmelCase__		=    next(lowerCamelCase_				)
																									UpperCAmelCase__		=    {
																									    '''total_loss''': '''0.1''',
																									    '''iteration''': '''1''',
																									    '''my_text''': '''some_value''',
																									    '''learning_rate''': '''''',
																									    '''num_iterations''': '''''',
																									    '''some_boolean''': '''''',
																									    '''some_string''': '''''',
																									}
																									self.assertDictEqual(lowerCamelCase_		, lowerCamelCase_				)



"""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)