






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


_UpperCAmelCase				:	int    						=    logging.get_logger(__name__)

# General docstring
_UpperCAmelCase				:	int    						=    'ResNetConfig'

# Base docstring
_UpperCAmelCase				:	Optional[int]    						=    'microsoft/resnet-50'
_UpperCAmelCase				:	Union[str, Any]    						=    [1, 2048, 7, 7]

# Image classification docstring
_UpperCAmelCase				:	Optional[int]    						=    'microsoft/resnet-50'
_UpperCAmelCase				:	Optional[Any]    						=    'tiger cat'

_UpperCAmelCase				:	List[str]    						=    [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]


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





     def __init__(  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = 3       ,   _UpperCAmelCase = 1       ,   _UpperCAmelCase = "relu"       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=kernel_size // 2
        lowerCamelCase											=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
        lowerCamelCase											=tf.keras.layers.BatchNormalization(epsilon=1E-5       ,   momentum=0.9       ,   name="""normalization"""     )
        lowerCamelCase											=ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("""linear"""     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							tf.Tensor:
        # Pad to match that done in the PyTorch Conv2D model
        lowerCamelCase											=lowerCamelCase											=(self.pad_value, self.pad_value)
        lowerCamelCase											=tf.pad(_UpperCAmelCase       ,   [(0, 0), height_pad, width_pad, (0, 0)]     )
        lowerCamelCase											=self.conv(_UpperCAmelCase     )
        return hidden_state





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False     )		->							tf.Tensor:
        lowerCamelCase											=self.convolution(_UpperCAmelCase     )
        lowerCamelCase											=self.normalization(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        lowerCamelCase											=self.activation(_UpperCAmelCase     )
        return hidden_state




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





     def __init__(  self       ,   _UpperCAmelCase       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=TFResNetConvLayer(
            config.embedding_size       ,   kernel_size=7       ,   stride=2       ,   activation=config.hidden_act       ,   name="""embedder"""       ,   )
        lowerCamelCase											=tf.keras.layers.MaxPoolaD(pool_size=3       ,   strides=2       ,   padding="""valid"""       ,   name="""pooler"""     )
        lowerCamelCase											=config.num_channels





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False     )		->							tf.Tensor:
        lowerCamelCase							,					lowerCamelCase							,					lowerCamelCase							,					lowerCamelCase											=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."""     )
        lowerCamelCase											=pixel_values
        lowerCamelCase											=self.embedder(_UpperCAmelCase     )
        lowerCamelCase											=tf.pad(_UpperCAmelCase       ,   [[0, 0], [1, 1], [1, 1], [0, 0]]     )
        lowerCamelCase											=self.pooler(_UpperCAmelCase     )
        return hidden_state




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





     def __init__(  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = 2       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=tf.keras.layers.ConvaD(
            _UpperCAmelCase       ,   kernel_size=1       ,   strides=_UpperCAmelCase       ,   use_bias=_UpperCAmelCase       ,   name="""convolution"""     )
        # Use same default momentum and epsilon as PyTorch equivalent
        lowerCamelCase											=tf.keras.layers.BatchNormalization(epsilon=1E-5       ,   momentum=0.9       ,   name="""normalization"""     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False     )		->							tf.Tensor:
        lowerCamelCase											=x
        lowerCamelCase											=self.convolution(_UpperCAmelCase     )
        lowerCamelCase											=self.normalization(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        return hidden_state




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





     def __init__(  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = 1       ,   _UpperCAmelCase = "relu"       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=in_channels != out_channels or stride != 1
        lowerCamelCase											=TFResNetConvLayer(_UpperCAmelCase       ,   stride=_UpperCAmelCase       ,   name="""layer.0"""     )
        lowerCamelCase											=TFResNetConvLayer(_UpperCAmelCase       ,   activation=_UpperCAmelCase       ,   name="""layer.1"""     )
        lowerCamelCase											=(
            TFResNetShortCut(_UpperCAmelCase       ,   stride=_UpperCAmelCase       ,   name="""shortcut"""     )
            if should_apply_shortcut
            else tf.keras.layers.Activation("""linear"""       ,   name="""shortcut"""     )
        )
        lowerCamelCase											=ACTaFN[activation]





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False     )		->							tf.Tensor:
        lowerCamelCase											=hidden_state
        lowerCamelCase											=self.conva(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        lowerCamelCase											=self.conva(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        lowerCamelCase											=self.shortcut(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        hidden_state += residual
        lowerCamelCase											=self.activation(_UpperCAmelCase     )
        return hidden_state




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





     def __init__(  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = 1       ,   _UpperCAmelCase = "relu"       ,   _UpperCAmelCase = 4       ,   **_UpperCAmelCase       ,   )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=in_channels != out_channels or stride != 1
        lowerCamelCase											=out_channels // reduction
        lowerCamelCase											=TFResNetConvLayer(_UpperCAmelCase       ,   kernel_size=1       ,   name="""layer.0"""     )
        lowerCamelCase											=TFResNetConvLayer(_UpperCAmelCase       ,   stride=_UpperCAmelCase       ,   name="""layer.1"""     )
        lowerCamelCase											=TFResNetConvLayer(_UpperCAmelCase       ,   kernel_size=1       ,   activation=_UpperCAmelCase       ,   name="""layer.2"""     )
        lowerCamelCase											=(
            TFResNetShortCut(_UpperCAmelCase       ,   stride=_UpperCAmelCase       ,   name="""shortcut"""     )
            if should_apply_shortcut
            else tf.keras.layers.Activation("""linear"""       ,   name="""shortcut"""     )
        )
        lowerCamelCase											=ACTaFN[activation]





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False     )		->							tf.Tensor:
        lowerCamelCase											=hidden_state
        lowerCamelCase											=self.conva(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        lowerCamelCase											=self.conva(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        lowerCamelCase											=self.conva(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        lowerCamelCase											=self.shortcut(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        hidden_state += residual
        lowerCamelCase											=self.activation(_UpperCAmelCase     )
        return hidden_state




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





     def __init__(  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = 2       ,   _UpperCAmelCase = 2       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )

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

        lowerCamelCase											=[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     )
        ]
        lowerCamelCase											=layers





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False     )		->							tf.Tensor:
        for layer in self.stage_layers:
           lowerCamelCase											=layer(_UpperCAmelCase       ,   training=_UpperCAmelCase     )
        return hidden_state




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





     def __init__(  self       ,   _UpperCAmelCase       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )
        # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
        lowerCamelCase											=[
            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       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = True       ,   _UpperCAmelCase = False       ,   )		->							TFBaseModelOutputWithNoAttention:
        lowerCamelCase											=() if output_hidden_states else None

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

           lowerCamelCase											=stage_module(_UpperCAmelCase       ,   training=_UpperCAmelCase     )

        if output_hidden_states:
           lowerCamelCase											=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 		lowerCAmelCase_							(     UpperCamelCase   ):
     __lowerCamelCase					:						Optional[Any]     =    ResNetConfig
     __lowerCamelCase					:						Tuple     =    '''resnet'''
     __lowerCamelCase					:						Tuple     =    '''pixel_values'''





     @property
     def 	lowerCamelCase       (  self     )		->							str:
        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)       ,   dtype=tf.floataa     )}




_UpperCAmelCase				:	Dict    						=    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'


_UpperCAmelCase				:	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 		lowerCAmelCase_							(     tf.keras.layers.Layer   ):
     __lowerCamelCase					:						List[Any]     =    ResNetConfig





     def __init__(  self       ,   _UpperCAmelCase       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=config
        lowerCamelCase											=TFResNetEmbeddings(_UpperCAmelCase       ,   name="""embedder"""     )
        lowerCamelCase											=TFResNetEncoder(_UpperCAmelCase       ,   name="""encoder"""     )
        lowerCamelCase											=tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCAmelCase     )





     @unpack_inputs
     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = False       ,   )		->							Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
        lowerCamelCase											=(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCamelCase											=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)
        lowerCamelCase											=tf.transpose(_UpperCAmelCase       ,   perm=[0, 2, 3, 1]     )
        lowerCamelCase											=self.embedder(_UpperCAmelCase       ,   training=_UpperCAmelCase     )

        lowerCamelCase											=self.encoder(
            _UpperCAmelCase       ,   output_hidden_states=_UpperCAmelCase       ,   return_dict=_UpperCAmelCase       ,   training=_UpperCAmelCase     )

        lowerCamelCase											=encoder_outputs[0]

        lowerCamelCase											=self.pooler(_UpperCAmelCase     )

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

        lowerCamelCase											=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.'''							,    UpperCamelCase							,    )
class 		lowerCAmelCase_							(     UpperCamelCase   ):





     def __init__(  self       ,   _UpperCAmelCase       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(_UpperCAmelCase       ,   **_UpperCAmelCase     )
        lowerCamelCase											=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       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = False       ,   )		->							Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
        lowerCamelCase											=(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCamelCase											=return_dict if return_dict is not None else self.config.use_return_dict

        lowerCamelCase											=self.resnet(
            pixel_values=_UpperCAmelCase       ,   output_hidden_states=_UpperCAmelCase       ,   return_dict=_UpperCAmelCase       ,   training=_UpperCAmelCase       ,   )
        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 		lowerCAmelCase_							(     UpperCamelCase							,    UpperCamelCase   ):





     def __init__(  self       ,   _UpperCAmelCase       ,   **_UpperCAmelCase     )		->							None:
        super().__init__(_UpperCAmelCase       ,   **_UpperCAmelCase     )
        lowerCamelCase											=config.num_labels
        lowerCamelCase											=TFResNetMainLayer(_UpperCAmelCase       ,   name="""resnet"""     )
        # classification head
        lowerCamelCase											=(
            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       ,   _UpperCAmelCase     )		->							tf.Tensor:
        lowerCamelCase											=tf.keras.layers.Flatten()(_UpperCAmelCase     )
        lowerCamelCase											=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       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = False       ,   )		->							Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
        lowerCamelCase											=return_dict if return_dict is not None else self.config.use_return_dict

        lowerCamelCase											=self.resnet(
            _UpperCAmelCase       ,   output_hidden_states=_UpperCAmelCase       ,   return_dict=_UpperCAmelCase       ,   training=_UpperCAmelCase     )

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

        lowerCamelCase											=self.classifier(_UpperCAmelCase     )

        lowerCamelCase											=None if labels is None else self.hf_compute_loss(_UpperCAmelCase       ,   _UpperCAmelCase     )

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

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







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCAmelCase				:	Optional[int]    						=    load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
_UpperCAmelCase						,						_UpperCAmelCase						,						_UpperCAmelCase						,						_UpperCAmelCase				:	List[Any]    						=    train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCAmelCase				:	Optional[int]    						=    KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCAmelCase				:	List[Any]    						=    [[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCAmelCase				:	Tuple    						=    knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)