"""simple docstring"""




import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__=None				,			UpperCamelCase__=None ):






							"""simple docstring"""





							return field(default_factory=lambda: default				,			metadata=UpperCamelCase__ )


@dataclass
class 			UpperCamelCase__:
						lowerCAmelCase__     :  List[str]  							=						list_field(
						    default=[]						,  metadata={
						        'help': (
						            'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
						            ' of all available models'
						        )
						    }						,  )

						lowerCAmelCase__     :  List[int]  							=						list_field(
						    default=[8]						,  metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}       )

						lowerCAmelCase__     :  List[int]  							=						list_field(
						    default=[8, 32, 128, 512]						,  metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}						,  )

						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}						,  )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}						,  )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}       )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Use FP16 to accelerate inference.'}       )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Benchmark training of model'}       )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Verbose memory tracing'}       )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}						,  )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={
						        'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
						    }						,  )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Trace memory line by line'}       )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Save result to a CSV file'}       )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Save all print statements in a log file'}       )
						lowerCAmelCase__     :  bool  							=						field(default=__A						,  metadata={'help': 'Whether to print environment information'}       )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
						            ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
						            ' for debugging / testing and on TPU.'
						        )
						    }						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default=f'inference_time_{round(time()       )}.csv'						,  metadata={'help': 'CSV filename used if saving time results to csv.'}						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default=f'inference_memory_{round(time()       )}.csv'						,  metadata={'help': 'CSV filename used if saving memory results to csv.'}						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default=f'train_time_{round(time()       )}.csv'						,  metadata={'help': 'CSV filename used if saving time results to csv for training.'}						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default=f'train_memory_{round(time()       )}.csv'						,  metadata={'help': 'CSV filename used if saving memory results to csv for training.'}						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default=f'env_info_{round(time()       )}.csv'						,  metadata={'help': 'CSV filename used if saving environment information.'}						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default=f'log_{round(time()       )}.csv'						,  metadata={'help': 'Log filename used if print statements are saved in log.'}						,  )
						lowerCAmelCase__     :  int  							=						field(default=3						,  metadata={'help': 'Times an experiment will be run.'}       )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
						            ' model weights.'
						        )
						    }						,  )





						def  snake_case__		(				self   )    -> Dict:
													warnings.warn(
													    f'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
													    ' are deprecated in general and it is advised to use external Benchmarking libraries '
													    ' to benchmark Transformer models.'						,__UpperCAmelCase						,)





						def  snake_case__		(				self   )    -> Dict:
													return json.dumps(dataclasses.asdict(self   )						,indent=2   )





						@property
						def  snake_case__		(				self   )    -> List[str]:
													if len(self.models   ) <= 0:
																				raise ValueError(
																				    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
																				    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'   )
													return self.models





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													if not self.multi_process:
																				return False
													elif self.is_tpu:
																				logger.info('Multiprocessing is currently not possible on TPU.'   )
																				return False
													else:
																				return True






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.ndarray:
													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

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

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





