




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_		:					str          =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:					int          =  [
							    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Wav2Vec2ForAudioFrameClassification",
							    "Wav2Vec2ForCTC",
							    "Wav2Vec2ForMaskedLM",
							    "Wav2Vec2ForPreTraining",
							    "Wav2Vec2ForSequenceClassification",
							    "Wav2Vec2ForXVector",
							    "Wav2Vec2Model",
							    "Wav2Vec2PreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:					int          =  [
							    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFWav2Vec2ForCTC",
							    "TFWav2Vec2Model",
							    "TFWav2Vec2PreTrainedModel",
							    "TFWav2Vec2ForSequenceClassification",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:					Union[str, Any]          =  [
							    "FlaxWav2Vec2ForCTC",
							    "FlaxWav2Vec2ForPreTraining",
							    "FlaxWav2Vec2Model",
							    "FlaxWav2Vec2PreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
							from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
							from .processing_wavaveca import WavaVecaProcessor
							from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavaveca import (
														    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavaVecaForAudioFrameClassification,
														    WavaVecaForCTC,
														    WavaVecaForMaskedLM,
														    WavaVecaForPreTraining,
														    WavaVecaForSequenceClassification,
														    WavaVecaForXVector,
														    WavaVecaModel,
														    WavaVecaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_wavaveca import (
														    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFWavaVecaForCTC,
														    TFWavaVecaForSequenceClassification,
														    TFWavaVecaModel,
														    TFWavaVecaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_wavaveca import (
														    FlaxWavaVecaForCTC,
														    FlaxWavaVecaForPreTraining,
														    FlaxWavaVecaModel,
														    FlaxWavaVecaPreTrainedModel,
														)


else:
							import sys

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






'''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


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """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    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			"""mobilenet_v2"""


 def __init__(    self  : Optional[int]			,							a__  : Dict=3			,							a__  : str=224			,							a__  : int=1.0			,							a__  : Dict=8			,							a__  : Dict=8			,							a__  : Dict=6			,							a__  : Optional[Any]=32			,							a__  : List[str]=True			,							a__  : int=True			,							a__  : List[str]="relu6"			,							a__  : List[str]=True			,							a__  : List[Any]=0.8			,							a__  : Tuple=0.02			,							a__  : Tuple=0.001			,							a__  : Optional[Any]=255			,							**a__  : str			,							):
  '''simple docstring'''

  super().__init__(**a__    )

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

  lowerCAmelCase__     :   Any     		=	num_channels
  lowerCAmelCase__     :   Union[str, Any]     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	depth_multiplier
  lowerCAmelCase__     :   Optional[int]     		=	depth_divisible_by
  lowerCAmelCase__     :   Union[str, Any]     		=	min_depth
  lowerCAmelCase__     :   List[str]     		=	expand_ratio
  lowerCAmelCase__     :   Union[str, Any]     		=	output_stride
  lowerCAmelCase__     :   Optional[int]     		=	first_layer_is_expansion
  lowerCAmelCase__     :   Tuple     		=	finegrained_output
  lowerCAmelCase__     :   Optional[int]     		=	hidden_act
  lowerCAmelCase__     :   Optional[Any]     		=	tf_padding
  lowerCAmelCase__     :   Union[str, Any]     		=	classifier_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	initializer_range
  lowerCAmelCase__     :   str     		=	layer_norm_eps
  lowerCAmelCase__     :   int     		=	semantic_loss_ignore_index




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[int]	=			version.parse("""1.11"""  )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

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


 @property
 def    _A	(    self  : Optional[int]    ):
  '''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    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return 1e-4





