






import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__snake_case			:			Union[str, Any]			   =   logging.get_logger(__name__)


class 			__SCREAMING_SNAKE_CASE    (      UpperCamelCase__):






						def __init__(		self   ,  *_UpperCamelCase   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												warnings.warn(
												    'The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use MobileNetV2ImageProcessor instead.'   ,  _UpperCamelCase   ,  )
												super().__init__(*_UpperCamelCase   ,  **_UpperCamelCase      )











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


lowercase__      :			int           =					logging.get_logger(__name__)

# General docstring
lowercase__      :			str           =					"ResNetConfig"

# Base docstring
lowercase__      :			Dict           =					"microsoft/resnet-50"
lowercase__      :			Dict           =					[1, 2_048, 7, 7]

# Image classification docstring
lowercase__      :			Optional[Any]           =					"microsoft/resnet-50"
lowercase__      :			str           =					"tiger cat"

lowercase__      :			Union[str, Any]           =					[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




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







     def __init__(	self  ,		A  ,		A = 3  ,		A = 1  ,		A = "relu"  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      kernel_size // 2
          a      =      tf.keras.layers.ConvaD(
              A  ,		kernel_size=A  ,		strides=A  ,		padding="valid"  ,		use_bias=A  ,		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  ,		A )  ->    tf.Tensor:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      self.convolution(A )
          a      =      self.normalization(A  ,		training=A )
          a      =      self.activation(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          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  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a  ,      a  ,      a  ,      a      =      shape_list(A )
          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(A )
          a      =      tf.pad(A  ,		[[0, 0], [1, 1], [1, 1], [0, 0]] )
          a      =      self.pooler(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A = 2  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      tf.keras.layers.ConvaD(
              A  ,		kernel_size=1  ,		strides=A  ,		use_bias=A  ,		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  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      x
          a      =      self.convolution(A )
          a      =      self.normalization(A  ,		training=A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A  ,		A = 1  ,		A = "relu"  ,		**A )  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      hidden_state
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.shortcut(A  ,		training=A )
          hidden_state += residual
          a      =      self.activation(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A  ,		A = 1  ,		A = "relu"  ,		A = 4  ,		**A  ,		)  ->    None:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      hidden_state
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.shortcut(A  ,		training=A )
          hidden_state += residual
          a      =      self.activation(A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		A  ,		A  ,		A = 2  ,		A = 2  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )

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

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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          for layer in self.stage_layers:
               a      =      layer(A  ,		training=A )
          return hidden_state







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          a      =      [
              TFResNetStage(
                  A  ,		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(A  ,		A  ,		A  ,		depth=A  ,		name=F'''stages.{i + 1}''' ) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False  ,		A = True  ,		A = False  ,		)  ->    TFBaseModelOutputWithNoAttention:



          '''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(A  ,		training=A )

          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=A  ,		hidden_states=A )







class 			a__       (					UpperCamelCase__      ):
     a		:			List[Any]							=	ResNetConfig
     a		:			str							=	"""resnet"""
     a		:			List[str]							=	"""pixel_values"""







     @property
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)  ,		dtype=tf.floataa )}







lowercase__      :			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"


lowercase__      :			Dict           =					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 			a__       (					tf.keras.layers.Layer      ):
     a		:			List[str]							=	ResNetConfig







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



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







     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:



          '''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(A  ,		perm=[0, 2, 3, 1] )
          a      =      self.embedder(A  ,		training=A )

          a      =      self.encoder(
              A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A )

          a      =      encoder_outputs[0]

          a      =      self.pooler(A )

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







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(A  ,		**A )
          a      =      TFResNetMainLayer(config=A  ,		name="resnet" )







     @add_start_docstrings_to_model_forward(A )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC  ,		output_type=A  ,		config_class=_CONFIG_FOR_DOC  ,		modality="vision"  ,		expected_output=_EXPECTED_OUTPUT_SHAPE  ,		)
     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:



          '''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=A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A  ,		)
          return resnet_outputs







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(A  ,		**A )
          a      =      config.num_labels
          a      =      TFResNetMainLayer(A  ,		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  ,		A )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      tf.keras.layers.Flatten()(A )
          a      =      self.classifier_layer(A )
          return logits







     @add_start_docstrings_to_model_forward(A )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT  ,		output_type=A  ,		config_class=_CONFIG_FOR_DOC  ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,		)
     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:



          '''simple docstring'''



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

          a      =      self.resnet(
              A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A )

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

          a      =      self.classifier(A )

          a      =      None if labels is None else self.hf_compute_loss(A  ,		A )

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

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

