





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase						: Dict		=   logging.get_logger(__name__)

__lowerCamelCase						: int		=   {}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""llama"""
  lowerCAmelCase_					=				["""past_key_values"""]


  def __init__(				self    :					str							,	UpperCamelCase__    :					Optional[Any]=3_2000							,	UpperCamelCase__    :					Any=4096							,	UpperCamelCase__    :					List[Any]=1_1008							,	UpperCamelCase__    :					int=32							,	UpperCamelCase__    :					List[Any]=32							,	UpperCamelCase__    :					Dict=None							,	UpperCamelCase__    :					Any="silu"							,	UpperCamelCase__    :					str=2048							,	UpperCamelCase__    :					Tuple=0.02							,	UpperCamelCase__    :					Optional[Any]=1E-6							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Optional[Any]=0							,	UpperCamelCase__    :					List[Any]=1							,	UpperCamelCase__    :					Dict=2							,	UpperCamelCase__    :					Dict=1							,	UpperCamelCase__    :					Union[str, Any]=False							,	UpperCamelCase__    :					List[Any]=None							,	**UpperCamelCase__    :					Tuple							,	)     ->						int:
    _UpperCamelCase       			=vocab_size
    _UpperCamelCase       			=max_position_embeddings
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=intermediate_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads

    # for backward compatibility
    if num_key_value_heads is None:
      _UpperCamelCase       			=num_attention_heads

    _UpperCamelCase       			=num_key_value_heads
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=rms_norm_eps
    _UpperCamelCase       			=pretraining_tp
    _UpperCamelCase       			=use_cache
    _UpperCamelCase       			=rope_scaling
    self._rope_scaling_validation()

    super().__init__(
        pad_token_id=UpperCamelCase__							,	bos_token_id=UpperCamelCase__							,	eos_token_id=UpperCamelCase__							,	tie_word_embeddings=UpperCamelCase__							,	**UpperCamelCase__							,	)




  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Tuple:
    if self.rope_scaling is None:
      return

    if not isinstance(self.rope_scaling							,	UpperCamelCase__  ) or len(self.rope_scaling  ) != 2:
      raise ValueError(
          '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
          F'''got {self.rope_scaling}'''  )
    _UpperCamelCase       			=self.rope_scaling.get('''type'''							,	UpperCamelCase__  )
    _UpperCamelCase       			=self.rope_scaling.get('''factor'''							,	UpperCamelCase__  )
    if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
      raise ValueError(
          F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''  )
    if rope_scaling_factor is None or not isinstance(UpperCamelCase__							,	UpperCamelCase__  ) or rope_scaling_factor <= 1.0:
      raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''  )













'''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






