

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

from ..utils import logging


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)





def      A__					(			__A  :						Optional[Any]=None     ,				__A  :						Optional[int]=None			)					->Optional[Any]:
      return field(default_factory=lambda: default     ,				metadata=__A			)





@dataclass
class        lowerCAmelCase__  :



       '''simple docstring'''







       lowercase_						=    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"""
               )
           }				,							)

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

       lowercase_						=    list_field(
           default=[8, 3_2, 1_2_8, 5_1_2]				,							metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}				,							)

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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.''' ,   lowercase__ ,   )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return json.dumps(dataclasses.asdict(self     ) ,   indent=2     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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     ):







             '''simple docstring'''



             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





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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


_lowerCamelCase      :					Dict					    =      logging.get_logger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""pixel_values"""]

       def __init__(							self ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = PILImageResampling.BILINEAR ,   lowercase__ = True ,   lowercase__ = 1 / 2_5_5 ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = True ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(**lowercase__     )
             __A    =size if size is not None else {'''shortest_edge''': 2_2_4}
             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             __A    =crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
             __A    =get_size_dict(lowercase__ ,   param_name='''crop_size'''     )

             __A    =do_resize
             __A    =size
             __A    =resample
             __A    =do_rescale
             __A    =rescale_factor
             __A    =do_center_crop
             __A    =crop_size
             __A    =do_flip_channel_order

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = PIL.Image.BILINEAR ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             if "shortest_edge" not in size:
                   raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''     )
             __A    =get_resize_output_image_size(lowercase__ ,   size=size['''shortest_edge'''] ,   default_to_square=lowercase__     )
             return resize(lowercase__ ,   size=lowercase__ ,   resample=lowercase__ ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =get_size_dict(lowercase__     )
             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 center_crop(lowercase__ ,   size=(size['''height'''], size['''width''']) ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             return rescale(lowercase__ ,   scale=lowercase__ ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return flip_channel_order(lowercase__ ,   data_format=lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = ChannelDimension.FIRST ,   **lowercase__ ,   ):







             '''simple docstring'''



             __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_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    =do_center_crop if do_center_crop is not None else self.do_center_crop
             __A    =(
                 do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
             )

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

             __A    =make_list_of_images(lowercase__     )

             if not valid_images(lowercase__     ):
                   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_rescale and rescale_factor is None:
                   raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

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

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

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

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

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

             # the pretrained checkpoints assume images are BGR, not RGB
             if do_flip_channel_order:
                   __A    =[self.flip_channel_order(image=lowercase__     ) for image in images]

             __A    =[to_channel_dimension_format(lowercase__ ,   lowercase__     ) for image in images]

             __A    ={'''pixel_values''': images}
             return BatchFeature(data=lowercase__ ,   tensor_type=lowercase__     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =outputs.logits

             # Resize logits and compute semantic segmentation maps
             if target_sizes is not None:
                   if len(lowercase__     ) != len(lowercase__     ):
                         raise ValueError(
                             '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''     )

                   if is_torch_tensor(lowercase__     ):
                         __A    =target_sizes.numpy()

                   __A    =[]

                   for idx in range(len(lowercase__     )     ):
                         __A    =torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0     ) ,   size=target_sizes[idx] ,   mode='''bilinear''' ,   align_corners=lowercase__     )
                         __A    =resized_logits[0].argmax(dim=0     )
                         semantic_segmentation.append(lowercase__     )
             else:
                   __A    =logits.argmax(dim=1     )
                   __A    =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

             return semantic_segmentation


