'''simple docstring'''

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_     :						Tuple    							=logging.get_logger(__name__)


class     __UpperCAmelCase       ( __lowercase		):
  __A :				Optional[int]											= ['pixel_values']





  def __init__(	self    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = PILImageResampling.BICUBIC    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1 / 255    ,     _lowerCamelCase = True    ,     _lowerCamelCase = IMAGENET_DEFAULT_MEAN    ,     _lowerCamelCase = IMAGENET_DEFAULT_STD    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_A )
     lowerCAmelCase_			=      size if size is not None else {'''shortest_edge''': 224}
     lowerCAmelCase_			=      get_size_dict(_A    ,     default_to_square=_A )
     lowerCAmelCase_			=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     lowerCAmelCase_			=      get_size_dict(_A    ,     param_name='''crop_size''' )

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





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = PILImageResampling.BICUBIC    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_A    ,     default_to_square=_A )
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
        lowerCAmelCase_			=      int((256 / 224) * size['''shortest_edge'''] )
        lowerCAmelCase_			=      get_resize_output_image_size(_A    ,     size=_A    ,     default_to_square=_A )
        lowerCAmelCase_			=      {'''height''': output_size[0], '''width''': output_size[1]}
     if "height" not in size_dict or "width" not in size_dict:
        raise ValueError(
            F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
     return resize(
         _A    ,     size=(size_dict['''height'''], size_dict['''width'''])    ,     resample=_A    ,     data_format=_A    ,     **_A )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_A )
     if "height" not in size or "width" not in size:
        raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
     return center_crop(_A    ,     size=(size['''height'''], size['''width'''])    ,     data_format=_A    ,     **_A )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     return rescale(_A    ,     scale=_A    ,     data_format=_A    ,     **_A )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     return normalize(_A    ,     mean=_A    ,     std=_A    ,     data_format=_A    ,     **_A )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = ChannelDimension.FIRST    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      do_resize if do_resize is not None else self.do_resize
     lowerCAmelCase_			=      resample if resample is not None else self.resample
     lowerCAmelCase_			=      do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCAmelCase_			=      do_rescale if do_rescale is not None else self.do_rescale
     lowerCAmelCase_			=      rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCAmelCase_			=      do_normalize if do_normalize is not None else self.do_normalize
     lowerCAmelCase_			=      image_mean if image_mean is not None else self.image_mean
     lowerCAmelCase_			=      image_std if image_std is not None else self.image_std

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

     lowerCAmelCase_			=      make_list_of_images(_A )

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

     if do_resize and size is None:
        raise ValueError('''Size 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.''' )

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

     if do_resize:
        lowerCAmelCase_			=      [self.resize(_A    ,     _A    ,     _A ) for image in images]

     if do_center_crop:
        lowerCAmelCase_			=      [self.center_crop(_A    ,     _A ) for image in images]

     if do_rescale:
        lowerCAmelCase_			=      [self.rescale(_A    ,     _A ) for image in images]

     if do_normalize:
        lowerCAmelCase_			=      [self.normalize(_A    ,     _A    ,     _A ) for image in images]

     lowerCAmelCase_			=      [to_channel_dimension_format(_A    ,     _A ) for image in images]

     lowerCAmelCase_			=      {'''pixel_values''': images}
     return BatchFeature(data=_A    ,     tensor_type=_A )






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 					_snake_case	(  __snake_case=None  ,     __snake_case=None						):
					return field(default_factory=lambda: default  ,     metadata=__snake_case						)







@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     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"
							        )
							    },  )

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

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

							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Use FP16 to accelerate inference."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Benchmark training of model"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Verbose memory tracing"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    },  )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Trace memory line by line"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save result to a CSV file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save all print statements in a log file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Whether to print environment information"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  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."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""env_info_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving environment information."},  )
							UpperCAmelCase				   =     field(
							    default=F"""log_{round(time() )}.csv""",  metadata={"help": "Log filename used if print statements are saved in log."},  )
							UpperCAmelCase				   =     field(default=3,  metadata={"help": "Times an experiment will be run."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    },  )







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												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.''' ,			_A ,			)







							def  UpperCamelCase_						(   self      :     str		):
												return json.dumps(dataclasses.asdict(self		) ,			indent=2		)







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												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  UpperCamelCase_						(   self      :     Optional[int]		):
												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
