





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}






class     lowercase__       (	lowercase_			,				lowercase_				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		"resnet"
   a						:							List[str]   		=		["basic", "bottleneck"]




   def __init__(    self,		__magic_name__=3,		__magic_name__=64,		__magic_name__=[256, 512, 1024, 2048],		__magic_name__=[3, 4, 6, 3],		__magic_name__="bottleneck",		__magic_name__="relu",		__magic_name__=False,		__magic_name__=None,		__magic_name__=None,		**__magic_name__,		) ->      int:






       """simple docstring"""
       super().__init__(**SCREAMING_SNAKE_CASE_							)
       if layer_type not in self.layer_types:
           raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types							)}"							)
       UpperCamelCase__							:				List[Any]          =      num_channels
       UpperCamelCase__							:				Union[str, Any]          =      embedding_size
       UpperCamelCase__							:				Dict          =      hidden_sizes
       UpperCamelCase__							:				int          =      depths
       UpperCamelCase__							:				str          =      layer_type
       UpperCamelCase__							:				Dict          =      hidden_act
       UpperCamelCase__							:				List[str]          =      downsample_in_first_stage
       UpperCamelCase__							:				List[str]          =      ['stem'] + [f"stage{idx}" for idx in range(1,		len(SCREAMING_SNAKE_CASE_							) + 1							)]
       UpperCamelCase__							:				Tuple          =      get_aligned_output_features_output_indices(
           out_features=SCREAMING_SNAKE_CASE_,		out_indices=SCREAMING_SNAKE_CASE_,		stage_names=self.stage_names							)






class     lowercase__       (	lowercase_				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		version.parse("1.11"				)




   @property
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]							)




   @property
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       return 1E-3






"""simple docstring"""







from typing import TYPE_CHECKING

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


lowercase__		:				int          =	{"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Optional[int]          =	["""MBartTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Dict          =	["""MBartTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Any          =	[
         """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MBartForCausalLM""",
         """MBartForConditionalGeneration""",
         """MBartForQuestionAnswering""",
         """MBartForSequenceClassification""",
         """MBartModel""",
         """MBartPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """TFMBartForConditionalGeneration""",
         """TFMBartModel""",
         """TFMBartPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """FlaxMBartForConditionalGeneration""",
         """FlaxMBartForQuestionAnswering""",
         """FlaxMBartForSequenceClassification""",
         """FlaxMBartModel""",
         """FlaxMBartPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowercase__		:				int          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





