



'''simple docstring'''






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
     import tensorflow as tf


lowerCAmelCase: str							=						logging.get_logger(__name__)

@dataclass
class 							a__( lowerCamelCase__     ):
    lowercase__			=   [
        """no_inference""",
        """no_cuda""",
        """no_tpu""",
        """no_speed""",
        """no_memory""",
        """no_env_print""",
        """no_multi_process""",
    ]







    def __init__(				self			:  int						,      **__snake_case			:  Dict     ):
      for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
          a						: int					       =					deprecated_arg[3:]
          a						: Union[str, Any]					       =					not kwargs.pop(__snake_case     )
          logger.warning(
              F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
              F""" {positive_arg}={kwargs[positive_arg]}"""     )
      a						: Optional[Any]					       =					kwargs.pop('tpu_name'						,      self.tpu_name     )
      a						: Tuple					       =					kwargs.pop('device_idx'						,      self.device_idx     )
      a						: Tuple					       =					kwargs.pop('eager_mode'						,      self.eager_mode     )
      a						: str					       =					kwargs.pop('use_xla'						,      self.use_xla     )
      super().__init__(**__snake_case     )

    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Name of TPU"""}		,			)
    lowercase__			=   field(
        default=0		,			metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}		,			)
    lowercase__			=   field(default=lowerCamelCase__		,			metadata={"""help""": """Benchmark models in eager model."""}     )
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={
            """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
        }		,			)







    @cached_property
    def       lowercase_	(				self			:  Dict     ):
      requires_backends(self						,      ['tf']     )
      a						: Optional[Any]					       =					None
      if self.tpu:
        try:
          if self.tpu_name:
            a						: Optional[int]					       =					tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
          else:
            a						: Tuple					       =					tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
          a						: Any					       =					None
      return tpu







    @cached_property
    def       lowercase_	(				self			:  Optional[int]     ):
      requires_backends(self						,      ['tf']     )
      if self.is_tpu:
        tf.config.experimental_connect_to_cluster(self._setup_tpu     )
        tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

        a						: int					       =					tf.distribute.TPUStrategy(self._setup_tpu     )
      else:
        # currently no multi gpu is allowed
        if self.is_gpu:
          # TODO: Currently only single GPU is supported
          tf.config.set_visible_devices(self.gpu_list[self.device_idx]						,      'GPU'     )
          a						: List[str]					       =					tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""     )
        else:
          tf.config.set_visible_devices([]						,      'GPU'     )  # disable GPU
          a						: str					       =					tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""     )

      return strategy







    @property
    def       lowercase_	(				self			:  Dict     ):
      requires_backends(self						,      ['tf']     )
      return self._setup_tpu is not None







    @property
    def       lowercase_	(				self			:  Tuple     ):
      requires_backends(self						,      ['tf']     )
      return self._setup_strategy







    @property
    def       lowercase_	(				self			:  Union[str, Any]     ):
      requires_backends(self						,      ['tf']     )
      return tf.config.list_physical_devices('GPU'     )







    @property
    def       lowercase_	(				self			:  int     ):
      requires_backends(self						,      ['tf']     )
      if self.cuda:
        return len(self.gpu_list     )
      return 0







    @property
    def       lowercase_	(				self			:  Any     ):
      return self.n_gpu > 0




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )