"""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

lowerCAmelCase__    :    List[Any]			=      logging.getLogger(__name__)
@require_tensorboard
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									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  :      Dict     ):
									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  :      Tuple     ):
									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  :      Tuple     ):
									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  :      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 							snake_case			(					__UpperCAmelCase ,       __UpperCAmelCase							):




					"""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[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(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   ,     lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				run_name
				UpperCAmelCase__			 =				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(__UpperCAmelCase ,       "__init__" ,       __UpperCAmelCase							)
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					@staticmethod
					def 						__lowerCAmelCase		(							lowerCamelCase__  :      int       ,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  :      Optional[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__     )       ,0.0_1     )
									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  :      int     ):
									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 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					[
					    "total_loss",
					    "iteration",
					    "my_text",
					    "learning_rate",
					    "num_iterations",
					    "some_boolean",
					    "some_string",
					]

					snake_case__							       =					"my_custom_tracker"
					snake_case__							       =					False







					def __init__(							self  :      List[str]       ,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  :      List[Any]     ):
									return self.writer







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







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      dict       ,lowerCamelCase__  :      Optional[int]     ):
									logger.info('Call log'     )
									self.writer.writerow(lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									self.f.close()






class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      Any     ):
									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  :      List[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"""






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


lowerCAmelCase__    :    Tuple			=      logging.get_logger(__name__)
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					["pixel_values"]







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

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

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







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      PILImageResampling = PIL.Image.BICUBIC       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      Any       ,):
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									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(
									    lowerCamelCase__       ,size=(size['height'], size['width'])       ,resample=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







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







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







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      ImageInput       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      PILImageResampling = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[float] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[str, TensorType]] = None       ,lowerCamelCase__  :      ChannelDimension = ChannelDimension.FIRST       ,**lowerCamelCase__  :      Optional[int]       ,):
									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(lowerCamelCase__       ,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(lowerCamelCase__     )
									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(lowerCamelCase__     )

									if not valid_images(lowerCamelCase__     ):
													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(lowerCamelCase__     ) for image in images]

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

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

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

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

									UpperCAmelCase__			 =				[to_channel_dimension_format(lowerCamelCase__       ,lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__       ,tensor_type=lowerCamelCase__     )



