





'''simple docstring'''






from math import ceil


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=list(range(0  ,      __SCREAMING_SNAKE_CASE   )   )

  _UpperCamelCase       			=[item for sublist in list(device_map.values()   ) for item in sublist]

  # Duplicate check
  _UpperCamelCase       			=[]
  for i in device_map_blocks:
    if device_map_blocks.count(__SCREAMING_SNAKE_CASE   ) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(__SCREAMING_SNAKE_CASE   )
    # Missing blocks
  _UpperCamelCase       			=[i for i in blocks if i not in device_map_blocks]
  _UpperCamelCase       			=[i for i in device_map_blocks if i not in blocks]

  if len(__SCREAMING_SNAKE_CASE   ) != 0:
    raise ValueError(
        '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
        ''' These attention blocks were specified more than once: ''' + str(__SCREAMING_SNAKE_CASE   )   )
  if len(__SCREAMING_SNAKE_CASE   ) != 0:
    raise ValueError(
        '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
        '''blocks to a device on the device_map: ''' + str(__SCREAMING_SNAKE_CASE   )   )
  if len(__SCREAMING_SNAKE_CASE   ) != 0:
    raise ValueError(
        '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
        + str(__SCREAMING_SNAKE_CASE   )   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=list(range(__SCREAMING_SNAKE_CASE   )   )
  _UpperCamelCase       			=int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE   )   )   )
  _UpperCamelCase       			=[layers[i : i + n_blocks] for i in range(0  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )]

  return dict(zip(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )   )













'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)

__lowerCamelCase						: int		=   {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""mobilenet_v2"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Union[str, Any]=3							,	UpperCamelCase__    :					Dict=224							,	UpperCamelCase__    :					str=1.0							,	UpperCamelCase__    :					List[Any]=8							,	UpperCamelCase__    :					Union[str, Any]=8							,	UpperCamelCase__    :					str=6							,	UpperCamelCase__    :					str=32							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					str="relu6"							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					List[str]=0.8							,	UpperCamelCase__    :					List[str]=0.02							,	UpperCamelCase__    :					str=0.001							,	UpperCamelCase__    :					Dict=255							,	**UpperCamelCase__    :					Tuple							,	)     ->						List[Any]:
    super().__init__(**UpperCamelCase__  )

    if depth_multiplier <= 0:
      raise ValueError('''depth_multiplier must be greater than zero.'''  )

    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=depth_multiplier
    _UpperCamelCase       			=depth_divisible_by
    _UpperCamelCase       			=min_depth
    _UpperCamelCase       			=expand_ratio
    _UpperCamelCase       			=output_stride
    _UpperCamelCase       			=first_layer_is_expansion
    _UpperCamelCase       			=finegrained_output
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=tf_padding
    _UpperCamelCase       			=classifier_dropout_prob
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=layer_norm_eps
    _UpperCamelCase       			=semantic_loss_ignore_index







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				version.parse("""1.11""")


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						Mapping[str, Mapping[int, str]]:
    return OrderedDict([('''pixel_values''', {0: '''batch'''})]  )


  @property
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Mapping[str, Mapping[int, str]]:
    if self.task == "image-classification":
      return OrderedDict([('''logits''', {0: '''batch'''})]  )
    else:
      return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]  )




  @property
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						float:
    return 1E-4






