




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







      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  '''test_project_with_log'''
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase     						=  Accelerator(log_with='''tensorboard'''       ,				project_dir=snake_case )
              accelerator.init_trackers(snake_case )
              __UpperCamelCase     						=  {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
              accelerator.log(snake_case       ,				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 snake_case : x.is_file()       ,				Path(F"{dirpath}/{project_name}" ).iterdir() ) )[0]
              self.assertNotEqual(str(snake_case )       ,				'''''' )







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  '''test_project_with_log'''
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase     						=  Accelerator(log_with='''tensorboard'''       ,				project_dir=snake_case )
              accelerator.init_trackers(snake_case )
              __UpperCamelCase     						=  {'''tensor''': torch.tensor(1 )}
              accelerator.log(snake_case       ,				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 snake_case : 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(snake_case       ,				'''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(snake_case )
                  if event.HasField('''summary''' ):
                      for value in event.summary.value:
                          if value.simple_value == 1.0 and value.tag == "tensor":
                              __UpperCamelCase     						=  True
              self.assertTrue(snake_case       ,				'''Converted tensor was not found in the log file!''' )







      def 					snake_case		(   self    :       str ):
          with self.assertRaisesRegex(snake_case       ,				'''Logging with `tensorboard` requires a `logging_dir`''' ):
              __UpperCamelCase     						=  Accelerator(log_with='''tensorboard''' )
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase     						=  Accelerator(log_with='''tensorboard'''       ,				project_dir=snake_case )







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  ProjectConfiguration(total_limit=30 )
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase     						=  Accelerator(log_with='''tensorboard'''       ,				project_dir=snake_case       ,				project_config=snake_case )







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

      """simple docstring"""

      def 					snake_case		(   self    :       Any ):
          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 					snake_case		(   snake_case    :       str       ,				snake_case    :       str       ,				snake_case    :       bool = True ):
          __UpperCamelCase     						=  RF"{section} ([\S\s]*?)\n\n"
          if record:
              __UpperCamelCase     						=  RF"Record: {pattern}"
          __UpperCamelCase     						=  re.findall(snake_case       ,				snake_case )[0]
          # A config
          if section == "config" or section == "history":
              __UpperCamelCase     						=  re.findall(R'''"([a-zA-Z0-9_.,]+)'''       ,				snake_case )
              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]+)"'''       ,				snake_case ) )







      @skip
      def 					snake_case		(   self    :       List[str] ):
          __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(snake_case       ,				snake_case       ,				snake_case )
          __UpperCamelCase     						=  {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
          accelerator.log(snake_case       ,				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(snake_case )]       ,				env=os.environ.copy() ).decode('''utf8'''       ,				'''ignore''' )
                  break

        # Check HPS through careful parsing and cleaning
          __UpperCamelCase     						=  self.parse_log(snake_case       ,				'''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(snake_case       ,				'''run'''       ,				snake_case )
          self.assertEqual(logged_items['''tags''']       ,				'''my_tag''' )

          # Actual logging
          __UpperCamelCase     						=  self.parse_log(snake_case       ,				'''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     __SCREAMING_SNAKE_CASE      (     self    ,   lowercase_    ,   lowercase_					)				->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  run_name
    __UpperCamelCase     						=  OfflineExperiment(project_name=lowercase_    ,   offline_directory=lowercase_					)
    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 					snake_case		(   snake_case    :       Any       ,				snake_case    :       str       ,				snake_case    :       bool = False ):
          for log in log_list:
              __UpperCamelCase     						=  json.loads(snake_case )['''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 					snake_case		(   self    :       Union[str, Any] ):
          with tempfile.TemporaryDirectory() as d:
              __UpperCamelCase     						=  CometMLTracker('''test_project_with_config'''       ,				snake_case )
              __UpperCamelCase     						=  Accelerator(log_with=snake_case )
              __UpperCamelCase     						=  {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
              accelerator.init_trackers(snake_case       ,				snake_case )
              accelerator.end_training()
              __UpperCamelCase     						=  os.listdir(snake_case )[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              __UpperCamelCase     						=  os.path.join(snake_case       ,				snake_case )
              __UpperCamelCase     						=  zipfile.ZipFile(snake_case       ,				'''r''' )
              __UpperCamelCase     						=  archive.open('''messages.json''' ).read().decode('''utf-8''' )
          __UpperCamelCase     						=  log.split('''\n''' )[:-1]
          self.assertEqual(self.get_value_from_key(snake_case       ,				'''num_iterations'''       ,				snake_case )       ,				12 )
          self.assertEqual(self.get_value_from_key(snake_case       ,				'''learning_rate'''       ,				snake_case )       ,				0.01 )
          self.assertEqual(self.get_value_from_key(snake_case       ,				'''some_boolean'''       ,				snake_case )       ,				snake_case )
          self.assertEqual(self.get_value_from_key(snake_case       ,				'''some_string'''       ,				snake_case )       ,				'''some_value''' )







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







class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    [
          "total_loss",
          "iteration",
          "my_text",
          "learning_rate",
          "num_iterations",
          "some_boolean",
          "some_string",
      ]

      lowerCAmelCase__	:       Optional[Any]			=    "my_custom_tracker"
      lowerCAmelCase__	:       int			=    False







      def __init__(   self    :       Any       ,				snake_case    :       str ):
          __UpperCamelCase     						=  open(F"{dir}/log.csv"       ,				'''w+''' )
          __UpperCamelCase     						=  csv.DictWriter(self.f       ,				fieldnames=self._col_names )
          self.writer.writeheader()







      @property
      def 					snake_case		(   self    :       Union[str, Any] ):
          return self.writer







      def 					snake_case		(   self    :       List[str]       ,				snake_case    :       dict ):
          logger.info('''Call init''' )
          self.writer.writerow(snake_case )







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







      def 					snake_case		(   self    :       str ):
          self.f.close()







class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

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







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












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    ["pixel_values"]







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       PILImageResampling = PILImageResampling.BICUBIC       ,				snake_case    :       bool = True       ,				snake_case    :       Union[int, float] = 1 / 255       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				**snake_case    :       Union[str, Any]       ,				):
          super().__init__(**snake_case )
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCamelCase     						=  get_size_dict(snake_case )

          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  resample
          __UpperCamelCase     						=  do_rescale
          __UpperCamelCase     						=  rescale_factor
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCamelCase     						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  self.size if size is None else size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )

          __UpperCamelCase						,					__UpperCamelCase     						=  get_image_size(snake_case )
          __UpperCamelCase     						=  min(snake_case       ,				snake_case )
          __UpperCamelCase     						=  (size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCamelCase     						=  (size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(snake_case       ,				size=(cropped_height, cropped_width)       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       PILImageResampling = PIL.Image.BICUBIC       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[str]       ,				):
          __UpperCamelCase     						=  get_size_dict(snake_case )
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
          return resize(
              snake_case       ,				size=(size['''height'''], size['''width'''])       ,				resample=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Union[int, float]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Optional[int]       ,				):
          return rescale(snake_case       ,				scale=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Union[float, List[float]]       ,				snake_case    :       Union[float, List[float]]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Any       ,				):
          return normalize(snake_case       ,				mean=snake_case       ,				std=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       ImageInput       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       PILImageResampling = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[float] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[str, TensorType]] = None       ,				snake_case    :       ChannelDimension = ChannelDimension.FIRST       ,				**snake_case    :       Tuple       ,				):
          __UpperCamelCase     						=  do_center_crop if do_center_crop is not None else self.do_center_crop
          __UpperCamelCase     						=  crop_size if crop_size is not None else self.crop_size
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  do_resize if do_resize is not None else self.do_resize
          __UpperCamelCase     						=  size if size is not None else self.size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  resample if resample is not None else self.resample
          __UpperCamelCase     						=  do_rescale if do_rescale is not None else self.do_rescale
          __UpperCamelCase     						=  rescale_factor if rescale_factor is not None else self.rescale_factor
          __UpperCamelCase     						=  do_normalize if do_normalize is not None else self.do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else self.image_mean
          __UpperCamelCase     						=  image_std if image_std is not None else self.image_std

          __UpperCamelCase     						=  make_list_of_images(snake_case )

          if not valid_images(snake_case ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

          if do_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.''' )

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.''' )

          # All transformations expect numpy arrays.
          __UpperCamelCase     						=  [to_numpy_array(snake_case ) for image in images]

          if do_center_crop:
              __UpperCamelCase     						=  [self.center_crop(snake_case       ,				snake_case       ,				size=snake_case ) for image in images]

          if do_resize:
              __UpperCamelCase     						=  [self.resize(image=snake_case       ,				size=snake_case       ,				resample=snake_case ) for image in images]

          if do_rescale:
              __UpperCamelCase     						=  [self.rescale(image=snake_case       ,				scale=snake_case ) for image in images]

          if do_normalize:
              __UpperCamelCase     						=  [self.normalize(image=snake_case       ,				mean=snake_case       ,				std=snake_case ) for image in images]

          __UpperCamelCase     						=  [to_channel_dimension_format(snake_case       ,				snake_case ) for image in images]

          __UpperCamelCase     						=  {'''pixel_values''': images}
          return BatchFeature(data=snake_case       ,				tensor_type=snake_case )






