
'''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


lowercase    :							Any      =					logging.get_logger(__name__)

lowercase    :							str      =					{
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """mobilenet_v1"""


							def __init__(       self    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=2_24    ,   lowerCAmelCase_=1.0    ,   lowerCAmelCase_=8    ,   lowerCAmelCase_="relu6"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=0.999    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=0.001    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_  )

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

								_snake_case   						=		num_channels
								_snake_case   						=		image_size
								_snake_case   						=		depth_multiplier
								_snake_case   						=		min_depth
								_snake_case   						=		hidden_act
								_snake_case   						=		tf_padding
								_snake_case   						=		classifier_dropout_prob
								_snake_case   						=		initializer_range
								_snake_case   						=		layer_norm_eps


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       version.parse("""1.11"""						)


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return OrderedDict([('pixel_values', {0: 'batch'})]  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								if self.task == "image-classification":
									return OrderedDict([('logits', {0: 'batch'})]  )
								else:
									return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]  )




							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return 1E-4


'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Tuple     =  ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Dict     =  [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE       :     Any     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

