




from typing import TYPE_CHECKING

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


__a :    List[Any]								=				{
    "auto_factory": ["get_values"],
    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
    "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
    "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a :    Union[str, Any]								=				[
      "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
      "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
      "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
      "MODEL_FOR_BACKBONE_MAPPING",
      "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
      "MODEL_FOR_CAUSAL_LM_MAPPING",
      "MODEL_FOR_CTC_MAPPING",
      "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
      "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
      "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
      "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
      "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
      "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
      "MODEL_FOR_MASKED_LM_MAPPING",
      "MODEL_FOR_MASK_GENERATION_MAPPING",
      "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
      "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
      "MODEL_FOR_OBJECT_DETECTION_MAPPING",
      "MODEL_FOR_PRETRAINING_MAPPING",
      "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
      "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
      "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
      "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
      "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
      "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
      "MODEL_FOR_TEXT_ENCODING_MAPPING",
      "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
      "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
      "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
      "MODEL_FOR_VISION_2_SEQ_MAPPING",
      "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
      "MODEL_MAPPING",
      "MODEL_WITH_LM_HEAD_MAPPING",
      "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
      "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
      "AutoModel",
      "AutoBackbone",
      "AutoModelForAudioClassification",
      "AutoModelForAudioFrameClassification",
      "AutoModelForAudioXVector",
      "AutoModelForCausalLM",
      "AutoModelForCTC",
      "AutoModelForDepthEstimation",
      "AutoModelForImageClassification",
      "AutoModelForImageSegmentation",
      "AutoModelForInstanceSegmentation",
      "AutoModelForMaskGeneration",
      "AutoModelForTextEncoding",
      "AutoModelForMaskedImageModeling",
      "AutoModelForMaskedLM",
      "AutoModelForMultipleChoice",
      "AutoModelForNextSentencePrediction",
      "AutoModelForObjectDetection",
      "AutoModelForPreTraining",
      "AutoModelForQuestionAnswering",
      "AutoModelForSemanticSegmentation",
      "AutoModelForSeq2SeqLM",
      "AutoModelForSequenceClassification",
      "AutoModelForSpeechSeq2Seq",
      "AutoModelForTableQuestionAnswering",
      "AutoModelForTokenClassification",
      "AutoModelForUniversalSegmentation",
      "AutoModelForVideoClassification",
      "AutoModelForVision2Seq",
      "AutoModelForVisualQuestionAnswering",
      "AutoModelForDocumentQuestionAnswering",
      "AutoModelWithLMHead",
      "AutoModelForZeroShotImageClassification",
      "AutoModelForZeroShotObjectDetection",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a :    Union[str, Any]								=				[
      "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
      "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
      "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
      "TF_MODEL_FOR_MASK_GENERATION_MAPPING",
      "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
      "TF_MODEL_FOR_MASKED_LM_MAPPING",
      "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
      "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
      "TF_MODEL_FOR_PRETRAINING_MAPPING",
      "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
      "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
      "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
      "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
      "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
      "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
      "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
      "TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
      "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
      "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
      "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
      "TF_MODEL_MAPPING",
      "TF_MODEL_WITH_LM_HEAD_MAPPING",
      "TFAutoModel",
      "TFAutoModelForAudioClassification",
      "TFAutoModelForCausalLM",
      "TFAutoModelForImageClassification",
      "TFAutoModelForMaskedImageModeling",
      "TFAutoModelForMaskedLM",
      "TFAutoModelForMaskGeneration",
      "TFAutoModelForMultipleChoice",
      "TFAutoModelForNextSentencePrediction",
      "TFAutoModelForPreTraining",
      "TFAutoModelForDocumentQuestionAnswering",
      "TFAutoModelForQuestionAnswering",
      "TFAutoModelForSemanticSegmentation",
      "TFAutoModelForSeq2SeqLM",
      "TFAutoModelForSequenceClassification",
      "TFAutoModelForSpeechSeq2Seq",
      "TFAutoModelForTableQuestionAnswering",
      "TFAutoModelForTextEncoding",
      "TFAutoModelForTokenClassification",
      "TFAutoModelForVision2Seq",
      "TFAutoModelForZeroShotImageClassification",
      "TFAutoModelWithLMHead",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a :    Any								=				[
      "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
      "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
      "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
      "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
      "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
      "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
      "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
      "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
      "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
      "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
      "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
      "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
      "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
      "FLAX_MODEL_MAPPING",
      "FlaxAutoModel",
      "FlaxAutoModelForCausalLM",
      "FlaxAutoModelForImageClassification",
      "FlaxAutoModelForMaskedLM",
      "FlaxAutoModelForMultipleChoice",
      "FlaxAutoModelForNextSentencePrediction",
      "FlaxAutoModelForPreTraining",
      "FlaxAutoModelForQuestionAnswering",
      "FlaxAutoModelForSeq2SeqLM",
      "FlaxAutoModelForSequenceClassification",
      "FlaxAutoModelForSpeechSeq2Seq",
      "FlaxAutoModelForTokenClassification",
      "FlaxAutoModelForVision2Seq",
  ]


if TYPE_CHECKING:
  from .auto_factory import get_values
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
  from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
  from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
  from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_auto import (
        MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_XVECTOR_MAPPING,
        MODEL_FOR_BACKBONE_MAPPING,
        MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
        MODEL_FOR_CAUSAL_LM_MAPPING,
        MODEL_FOR_CTC_MAPPING,
        MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
        MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_MASK_GENERATION_MAPPING,
        MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        MODEL_FOR_MASKED_LM_MAPPING,
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        MODEL_FOR_OBJECT_DETECTION_MAPPING,
        MODEL_FOR_PRETRAINING_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_TEXT_ENCODING_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        MODEL_FOR_VISION_2_SEQ_MAPPING,
        MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
        MODEL_MAPPING,
        MODEL_WITH_LM_HEAD_MAPPING,
        AutoBackbone,
        AutoModel,
        AutoModelForAudioClassification,
        AutoModelForAudioFrameClassification,
        AutoModelForAudioXVector,
        AutoModelForCausalLM,
        AutoModelForCTC,
        AutoModelForDepthEstimation,
        AutoModelForDocumentQuestionAnswering,
        AutoModelForImageClassification,
        AutoModelForImageSegmentation,
        AutoModelForInstanceSegmentation,
        AutoModelForMaskedImageModeling,
        AutoModelForMaskedLM,
        AutoModelForMaskGeneration,
        AutoModelForMultipleChoice,
        AutoModelForNextSentencePrediction,
        AutoModelForObjectDetection,
        AutoModelForPreTraining,
        AutoModelForQuestionAnswering,
        AutoModelForSemanticSegmentation,
        AutoModelForSeqaSeqLM,
        AutoModelForSequenceClassification,
        AutoModelForSpeechSeqaSeq,
        AutoModelForTableQuestionAnswering,
        AutoModelForTextEncoding,
        AutoModelForTokenClassification,
        AutoModelForUniversalSegmentation,
        AutoModelForVideoClassification,
        AutoModelForVisionaSeq,
        AutoModelForVisualQuestionAnswering,
        AutoModelForZeroShotImageClassification,
        AutoModelForZeroShotObjectDetection,
        AutoModelWithLMHead,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_auto import (
        TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASK_GENERATION_MAPPING,
        TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
        TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_MAPPING,
        TF_MODEL_WITH_LM_HEAD_MAPPING,
        TFAutoModel,
        TFAutoModelForAudioClassification,
        TFAutoModelForCausalLM,
        TFAutoModelForDocumentQuestionAnswering,
        TFAutoModelForImageClassification,
        TFAutoModelForMaskedImageModeling,
        TFAutoModelForMaskedLM,
        TFAutoModelForMaskGeneration,
        TFAutoModelForMultipleChoice,
        TFAutoModelForNextSentencePrediction,
        TFAutoModelForPreTraining,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForSemanticSegmentation,
        TFAutoModelForSeqaSeqLM,
        TFAutoModelForSequenceClassification,
        TFAutoModelForSpeechSeqaSeq,
        TFAutoModelForTableQuestionAnswering,
        TFAutoModelForTextEncoding,
        TFAutoModelForTokenClassification,
        TFAutoModelForVisionaSeq,
        TFAutoModelForZeroShotImageClassification,
        TFAutoModelWithLMHead,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_auto import (
        FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_MASKED_LM_MAPPING,
        FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        FLAX_MODEL_FOR_PRETRAINING_MAPPING,
        FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
        FLAX_MODEL_MAPPING,
        FlaxAutoModel,
        FlaxAutoModelForCausalLM,
        FlaxAutoModelForImageClassification,
        FlaxAutoModelForMaskedLM,
        FlaxAutoModelForMultipleChoice,
        FlaxAutoModelForNextSentencePrediction,
        FlaxAutoModelForPreTraining,
        FlaxAutoModelForQuestionAnswering,
        FlaxAutoModelForSeqaSeqLM,
        FlaxAutoModelForSequenceClassification,
        FlaxAutoModelForSpeechSeqaSeq,
        FlaxAutoModelForTokenClassification,
        FlaxAutoModelForVisionaSeq,
    )

else:
  import sys

  __a :    Dict								=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


__a :    List[Any]								=				logging.get_logger(__name__)

# General docstring
__a :    Any								=				"ResNetConfig"

# Base docstring
__a :    List[str]								=				"microsoft/resnet-50"
__a :    Dict								=				[1, 2048, 7, 7]

# Image classification docstring
__a :    Optional[int]								=				"microsoft/resnet-50"
__a :    Dict								=				"tiger cat"

__a :    Dict								=				[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 3	,		UpperCamelCase_    :     int = 1	,		UpperCamelCase_    :     str = "relu"	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		kernel_size // 2
         __A             =		tf.keras.layers.ConvaD(
             UpperCamelCase_	,		kernel_size=UpperCamelCase_	,		strides=UpperCamelCase_	,		padding="""valid"""	,		use_bias=UpperCamelCase_	,		name="""convolution"""			)
         # Use same default momentum and epsilon as PyTorch equivalent
         __A             =		tf.keras.layers.BatchNormalization(epsilon=1e-5	,		momentum=0.9	,		name="""normalization"""			)
         __A             =		ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("""linear"""			)




     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     tf.Tensor			):


         """simple docstring"""



         __A             =		__A             =		(self.pad_value, self.pad_value)
         __A             =		tf.pad(UpperCamelCase_	,		[(0, 0), height_pad, width_pad, (0, 0)]			)
         __A             =		self.conv(UpperCamelCase_			)
         return hidden_state





     def 						lowerCAmelCase_       (			self    :     Optional[int]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		self.convolution(UpperCamelCase_			)
         __A             =		self.normalization(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.activation(UpperCamelCase_			)
         return hidden_state


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		TFResNetConvLayer(
             config.embedding_size	,		kernel_size=7	,		stride=2	,		activation=config.hidden_act	,		name="""embedder"""	,		)
         __A             =		tf.keras.layers.MaxPoolaD(pool_size=3	,		strides=2	,		padding="""valid"""	,		name="""pooler"""			)
         __A             =		config.num_channels





     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A      ,		__A      ,		__A      ,		__A             =		shape_list(UpperCamelCase_			)
         if tf.executing_eagerly() and num_channels != self.num_channels:
             raise ValueError(
                 """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""			)
         __A             =		pixel_values
         __A             =		self.embedder(UpperCamelCase_			)
         __A             =		tf.pad(UpperCamelCase_	,		[[0, 0], [1, 1], [1, 1], [0, 0]]			)
         __A             =		self.pooler(UpperCamelCase_			)
         return hidden_state


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     str	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 2	,		**UpperCamelCase_    :     Optional[Any]			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		tf.keras.layers.ConvaD(
             UpperCamelCase_	,		kernel_size=1	,		strides=UpperCamelCase_	,		use_bias=UpperCamelCase_	,		name="""convolution"""			)
         # Use same default momentum and epsilon as PyTorch equivalent
         __A             =		tf.keras.layers.BatchNormalization(epsilon=1e-5	,		momentum=0.9	,		name="""normalization"""			)





     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		x
         __A             =		self.convolution(UpperCamelCase_			)
         __A             =		self.normalization(UpperCamelCase_	,		training=UpperCamelCase_			)
         return hidden_state


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     List[str]	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 1	,		UpperCamelCase_    :     str = "relu"	,		**UpperCamelCase_    :     Any			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		in_channels != out_channels or stride != 1
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""layer.0"""			)
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		activation=UpperCamelCase_	,		name="""layer.1"""			)
         __A             =		(
             TFResNetShortCut(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""shortcut"""			)
             if should_apply_shortcut
             else tf.keras.layers.Activation("""linear"""	,		name="""shortcut"""			)
         )
         __A             =		ACTaFN[activation]





     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		hidden_state
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.shortcut(UpperCamelCase_	,		training=UpperCamelCase_			)
         hidden_state += residual
         __A             =		self.activation(UpperCamelCase_			)
         return hidden_state


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 1	,		UpperCamelCase_    :     str = "relu"	,		UpperCamelCase_    :     int = 4	,		**UpperCamelCase_    :     str	,		):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		in_channels != out_channels or stride != 1
         __A             =		out_channels // reduction
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		kernel_size=1	,		name="""layer.0"""			)
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""layer.1"""			)
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		kernel_size=1	,		activation=UpperCamelCase_	,		name="""layer.2"""			)
         __A             =		(
             TFResNetShortCut(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""shortcut"""			)
             if should_apply_shortcut
             else tf.keras.layers.Activation("""linear"""	,		name="""shortcut"""			)
         )
         __A             =		ACTaFN[activation]





     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		hidden_state
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.shortcut(UpperCamelCase_	,		training=UpperCamelCase_			)
         hidden_state += residual
         __A             =		self.activation(UpperCamelCase_			)
         return hidden_state


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     str	,		UpperCamelCase_    :     ResNetConfig	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 2	,		UpperCamelCase_    :     int = 2	,		**UpperCamelCase_    :     Optional[int]			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)

         __A             =		TFResNetBottleNeckLayer if config.layer_type == """bottleneck""" else TFResNetBasicLayer

         __A             =		[layer(UpperCamelCase_	,		UpperCamelCase_	,		stride=UpperCamelCase_	,		activation=config.hidden_act	,		name="""layers.0"""			)]
         layers += [
             layer(UpperCamelCase_	,		UpperCamelCase_	,		activation=config.hidden_act	,		name=F"layers.{i + 1}"			)
             for i in range(depth - 1			)
         ]
         __A             =		layers





     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         for layer in self.stage_layers:
             __A             =		layer(UpperCamelCase_	,		training=UpperCamelCase_			)
         return hidden_state


class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
         __A             =		[
             TFResNetStage(
                 UpperCamelCase_	,		config.embedding_size	,		config.hidden_sizes[0]	,		stride=2 if config.downsample_in_first_stage else 1	,		depth=config.depths[0]	,		name="""stages.0"""	,		)
         ]
         for i, (in_channels, out_channels, depth) in enumerate(
             zip(config.hidden_sizes	,		config.hidden_sizes[1:]	,		config.depths[1:]			)			):
             self.stages.append(TFResNetStage(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		depth=UpperCamelCase_	,		name=F"stages.{i + 1}"			)			)





     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		() if output_hidden_states else None

         for stage_module in self.stages:
             if output_hidden_states:
                 __A             =		hidden_states + (hidden_state,)

             __A             =		stage_module(UpperCamelCase_	,		training=UpperCamelCase_			)

         if output_hidden_states:
             __A             =		hidden_states + (hidden_state,)

         if not return_dict:
             return tuple(v for v in [hidden_state, hidden_states] if v is not None			)

         return TFBaseModelOutputWithNoAttention(last_hidden_state=UpperCamelCase_	,		hidden_states=UpperCamelCase_			)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			ResNetConfig
     SCREAMING_SNAKE_CASE				  =			"resnet"
     SCREAMING_SNAKE_CASE				  =			"pixel_values"




     @property
     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)	,		dtype=tf.floataa			)}
__a :    Union[str, Any]								=				R"\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"


__a :    Tuple								=				R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@keras_serializable
class  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			ResNetConfig




     def __init__(			self    :     str	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     str			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		config
         __A             =		TFResNetEmbeddings(UpperCamelCase_	,		name="""embedder"""			)
         __A             =		TFResNetEncoder(UpperCamelCase_	,		name="""encoder"""			)
         __A             =		tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase_			)





     @unpack_inputs
     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __A             =		return_dict if return_dict is not None else self.config.use_return_dict

         # TF 2.0 image layers can't use NCHW format when running on CPU.
         # We transpose to NHWC format and then transpose back after the full forward pass.
         # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
         __A             =		tf.transpose(UpperCamelCase_	,		perm=[0, 2, 3, 1]			)
         __A             =		self.embedder(UpperCamelCase_	,		training=UpperCamelCase_			)

         __A             =		self.encoder(
             UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		return_dict=UpperCamelCase_	,		training=UpperCamelCase_			)

         __A             =		encoder_outputs[0]

         __A             =		self.pooler(UpperCamelCase_			)

         # Transpose all the outputs to the NCHW format
         # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
         __A             =		tf.transpose(UpperCamelCase_	,		(0, 3, 1, 2)			)
         __A             =		tf.transpose(UpperCamelCase_	,		(0, 3, 1, 2)			)
         __A             =		()
         for hidden_state in encoder_outputs[1:]:
             __A             =		hidden_states + tuple(tf.transpose(UpperCamelCase_	,		(0, 3, 1, 2)			) for h in hidden_state			)

         if not return_dict:
             return (last_hidden_state, pooled_output) + hidden_states

         __A             =		hidden_states if output_hidden_states else None

         return TFBaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=UpperCamelCase_	,		pooler_output=UpperCamelCase_	,		hidden_states=UpperCamelCase_	,		)


@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."     , lowercase_     , )
class  __lowercase (		lowercase_					):







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         super().__init__(UpperCamelCase_	,		**UpperCamelCase_			)
         __A             =		TFResNetMainLayer(config=UpperCamelCase_	,		name="""resnet"""			)





     @add_start_docstrings_to_model_forward(UpperCamelCase_			)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC	,		output_type=UpperCamelCase_	,		config_class=_CONFIG_FOR_DOC	,		modality="""vision"""	,		expected_output=_EXPECTED_OUTPUT_SHAPE	,		)
     @unpack_inputs
     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __A             =		return_dict if return_dict is not None else self.config.use_return_dict

         __A             =		self.resnet(
             pixel_values=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		return_dict=UpperCamelCase_	,		training=UpperCamelCase_	,		)
         return resnet_outputs


@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "     , lowercase_     , )
class  __lowercase (		lowercase_     , lowercase_					):







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Any			):


         """simple docstring"""



         super().__init__(UpperCamelCase_	,		**UpperCamelCase_			)
         __A             =		config.num_labels
         __A             =		TFResNetMainLayer(UpperCamelCase_	,		name="""resnet"""			)
         # classification head
         __A             =		(
             tf.keras.layers.Dense(config.num_labels	,		name="""classifier.1"""			)
             if config.num_labels > 0
             else tf.keras.layers.Activation("""linear"""	,		name="""classifier.1"""			)
         )




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     tf.Tensor			):


         """simple docstring"""



         __A             =		tf.keras.layers.Flatten()(UpperCamelCase_			)
         __A             =		self.classifier_layer(UpperCamelCase_			)
         return logits





     @add_start_docstrings_to_model_forward(UpperCamelCase_			)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT	,		output_type=UpperCamelCase_	,		config_class=_CONFIG_FOR_DOC	,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,		)
     @unpack_inputs
     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     tf.Tensor = None	,		UpperCamelCase_    :     tf.Tensor = None	,		UpperCamelCase_    :     bool = None	,		UpperCamelCase_    :     bool = None	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		return_dict if return_dict is not None else self.config.use_return_dict

         __A             =		self.resnet(
             UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		return_dict=UpperCamelCase_	,		training=UpperCamelCase_			)

         __A             =		outputs.pooler_output if return_dict else outputs[1]

         __A             =		self.classifier(UpperCamelCase_			)

         __A             =		None if labels is None else self.hf_compute_loss(UpperCamelCase_	,		UpperCamelCase_			)

         if not return_dict:
             __A             =		(logits,) + outputs[2:]
             return (loss,) + output if loss is not None else output

         return TFImageClassifierOutputWithNoAttention(loss=UpperCamelCase_	,		logits=UpperCamelCase_	,		hidden_states=outputs.hidden_states			)




