
"""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 A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       "test_project_with_config"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   int     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers(lowercase_							,  lowercase_    )
        accelerator.end_training()
        for child in Path(F"""{dirpath}/{project_name}"""    ).glob("*/**"    ):
           UpperCAmelCase_   :   Union[str, Any]     	=       list(filter(lambda lowercase_    : x.is_file()							,  child.iterdir()    )    )[0]
        self.assertNotEqual(str(lowercase_    )							,  ""    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       "test_project_with_log"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Union[str, Any]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   int     	=       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(lowercase_							,  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_   :   int     	=       list(filter(lambda lowercase_    : x.is_file()							,  Path(F"""{dirpath}/{project_name}"""    ).iterdir()    )    )[0]
        self.assertNotEqual(str(lowercase_    )							,  ""    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       "test_project_with_log"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   str     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   Any     	=       {"tensor": torch.tensor(1    )}
        accelerator.log(lowercase_							,  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_   :   Optional[Any]     	=       list(filter(lambda lowercase_    : 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(lowercase_							,  "rb"    ) as f:
           UpperCAmelCase_   :   Any     	=       f.read()
        UpperCAmelCase_   :   List[Any]     	=       False
        while data:
           UpperCAmelCase_   :   Dict     	=       struct.unpack("Q"							,  data[:8]    )

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

           event.ParseFromString(lowercase_    )
           if event.HasField("summary"    ):
              for value in event.summary.value:
                 if value.simple_value == 1.0 and value.tag == "tensor":
                    UpperCAmelCase_   :   Optional[Any]     	=       True
        self.assertTrue(lowercase_							,  "Converted tensor was not found in the log file!"    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with self.assertRaisesRegex(lowercase_							,  "Logging with `tensorboard` requires a `logging_dir`"    ):
        UpperCAmelCase_   :   Optional[Any]     	=       Accelerator(log_with="tensorboard"    )
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Union[str, Any]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       ProjectConfiguration(total_limit=30    )
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Optional[int]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_							,  project_config=lowercase_    )




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




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     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 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_ = True    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       RF"""{section} ([\S\s]*?)\n\n"""
     if record:
        UpperCAmelCase_   :   Dict     	=       RF"""Record: {pattern}"""
     UpperCAmelCase_   :   Optional[int]     	=       re.findall(lowercase_							,  lowercase_    )[0]
     # A config
     if section == "config" or section == "history":
        UpperCAmelCase_   :   Any     	=       re.findall(R"\"([a-zA-Z0-9_.,]+)"							,  lowercase_    )
        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]+)\""							,  lowercase_    )    )







  @skip
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       "test_project_with_config"
     UpperCAmelCase_   :   List[Any]     	=       Accelerator(log_with="wandb"    )
     UpperCAmelCase_   :   Optional[Any]     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
     UpperCAmelCase_   :   List[str]     	=       {"wandb": {"tags": ["my_tag"]}}
     accelerator.init_trackers(lowercase_							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   Optional[Any]     	=       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
     accelerator.log(lowercase_							,  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_   :   Optional[int]     	=       subprocess.check_output(
               ["wandb", "sync", "--view", "--verbose", str(lowercase_    )]							,  env=os.environ.copy()    ).decode("utf8"							,  "ignore"    )
           break

        # Check HPS through careful parsing and cleaning
     UpperCAmelCase_   :   List[str]     	=       self.parse_log(lowercase_							,  "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_   :   Optional[int]     	=       self.parse_log(lowercase_							,  "run"							,  lowercase_    )
     self.assertEqual(logged_items["tags"]							,  "my_tag"    )

     # Actual logging
     UpperCAmelCase_   :   Union[str, Any]     	=       self.parse_log(lowercase_							,  "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,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       run_name
   UpperCAmelCase_   :   str     	=       OfflineExperiment(project_name=__lowerCamelCase,						offline_directory=__lowerCamelCase    )
   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(lowercase__							,"""__init__"""							,lowercase__ )
class A_      (unittest.TestCase ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_ = False    ):

     """simple docstring"""
     for log in log_list:
        UpperCAmelCase_   :   str     	=       json.loads(lowercase_    )["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 			UpperCamelCase__   (						self    ):

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







  def 			UpperCamelCase__   (						self    ):

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




class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						[
      """total_loss""",
      """iteration""",
      """my_text""",
      """learning_rate""",
      """num_iterations""",
      """some_boolean""",
      """some_string""",
  ]

  SCREAMING_SNAKE_CASE__     :		Dict    	=						"""my_custom_tracker"""
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						False







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       open(F"""{dir}/log.csv"""							,  "w+"    )
     UpperCAmelCase_   :   Optional[int]     	=       csv.DictWriter(self.f							,  fieldnames=self._col_names    )
     self.writer.writeheader()







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.writer







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     logger.info("Call init"    )
     self.writer.writerow(lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     logger.info("Call log"    )
     self.writer.writerow(lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.f.close()




class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   Any     	=       MyCustomTracker(lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       Accelerator(log_with=lowercase_    )
        UpperCAmelCase_   :   Any     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers("Some name"							,  lowercase_    )
        accelerator.end_training()
        with open(F"""{d}/log.csv"""							,  "r"    ) as f:
           UpperCAmelCase_   :   Dict     	=       csv.DictReader(lowercase_    )
           UpperCAmelCase_   :   List[Any]     	=       next(lowercase_    )
           UpperCAmelCase_   :   int     	=       {
               "total_loss": "",
               "iteration": "",
               "my_text": "",
               "learning_rate": "0.01",
               "num_iterations": "12",
               "some_boolean": "False",
               "some_string": "some_value",
           }
           self.assertDictEqual(lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   Any     	=       MyCustomTracker(lowercase_    )
        UpperCAmelCase_   :   Dict     	=       Accelerator(log_with=lowercase_    )
        accelerator.init_trackers("Some name"    )
        UpperCAmelCase_   :   Any     	=       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(lowercase_							,  step=0    )
        accelerator.end_training()
        with open(F"""{d}/log.csv"""							,  "r"    ) as f:
           UpperCAmelCase_   :   Optional[int]     	=       csv.DictReader(lowercase_    )
           UpperCAmelCase_   :   Any     	=       next(lowercase_    )
           UpperCAmelCase_   :   Tuple     	=       {
               "total_loss": "0.1",
               "iteration": "1",
               "my_text": "some_value",
               "learning_rate": "",
               "num_iterations": "",
               "some_boolean": "",
               "some_string": "",
           }
           self.assertDictEqual(lowercase_							,  lowercase_    )





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				:     Dict     )    ->		Any:
  UpperCAmelCase			:				List[str]										=					'''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=2_048     ):
        f.write(UpperCAmelCase     )

def      a__	(						)    ->		VivitConfig:
  UpperCAmelCase			:				Any										=					VivitConfig()

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

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

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

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  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				:     np.ndarray     )    ->		List[Any]:
  return current.flatten()

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

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  return np.reshape(UpperCAmelCase							,					(current.shape[0] * current.shape[1], current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Union[str, Any]     )    ->		Dict:
  UpperCAmelCase			:				Any										=					state_dict['''optimizer''']['''target''']['''Transformer'''][f'''encoderblock_{i}''']

  UpperCAmelCase			:				Dict										=					f'''encoder.layer.{i}.'''
  UpperCAmelCase			:				Optional[int]										=					{
      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				:     Any     )    ->		List[str]:
  return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict=False     )    ->		Dict:
  UpperCAmelCase			:				int										=					get_n_layers(UpperCAmelCase     )

  UpperCAmelCase			:				List[str]										=					OrderedDict()

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

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

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

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

def      a__	(						)    ->		VivitImageProcessor:
  UpperCAmelCase			:				Optional[int]										=					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				:     str     )    ->		Tuple:
  UpperCAmelCase			:				Union[str, Any]										=					'''checkpoint'''

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

  UpperCAmelCase			:				Tuple										=					restore_checkpoint(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					transform_state(UpperCAmelCase							,					classification_head=UpperCAmelCase     )

  UpperCAmelCase			:				Union[str, Any]										=					get_vivit_config()

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

  UpperCAmelCase			:				Optional[int]										=					VivitForVideoClassification(UpperCAmelCase     )
  model.load_state_dict(UpperCAmelCase     )
  model.eval()

  UpperCAmelCase			:				List[Any]										=					get_processor()

  UpperCAmelCase			:				str										=					prepare_video()
  UpperCAmelCase			:				Union[str, Any]										=					extractor(UpperCAmelCase							,					return_tensors='''pt'''     )

  UpperCAmelCase			:				Optional[Any]										=					model(**UpperCAmelCase     )

  UpperCAmelCase			:				str										=					torch.Size([1, 400]     )
  UpperCAmelCase			:				int										=					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__":
     _lowerCamelCase							:  Optional[Any]     =				argparse.ArgumentParser()

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

     _lowerCamelCase							:  str     =				parser.parse_args()
     convert(args.output_model_name)





