


'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import OwlViTImageProcessor







class 			UpperCAmelCase			(							unittest.TestCase):

 def __init__(			self			:						Optional[Any],       a_			:						Optional[int],       a_			:						Dict=7,       a_			:						List[str]=3,       a_			:						int=18,       a_			:						Tuple=30,       a_			:						Any=400,       a_			:						Optional[int]=True,       a_			:						Optional[int]=None,       a_			:						Any=True,       a_			:						Optional[int]=None,       a_			:						int=True,       a_			:						Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073],       a_			:						Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711],       a_			:						Optional[int]=True,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    min_resolution
    UpperCamelCase__			    =    max_resolution
    UpperCamelCase__			    =    do_resize
    UpperCamelCase__			    =    size if size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_center_crop
    UpperCamelCase__			    =    crop_size if crop_size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_normalize
    UpperCamelCase__			    =    image_mean
    UpperCamelCase__			    =    image_std
    UpperCamelCase__			    =    do_convert_rgb

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }







@require_torch
@require_vision
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Tuple            =       OwlViTImageProcessor if is_vision_available() else None

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    OwlViTImageProcessingTester(self       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(a_,       "do_resize"       )       )
    self.assertTrue(hasattr(a_,       "size"       )       )
    self.assertTrue(hasattr(a_,       "do_center_crop"       )       )
    self.assertTrue(hasattr(a_,       "center_crop"       )       )
    self.assertTrue(hasattr(a_,       "do_normalize"       )       )
    self.assertTrue(hasattr(a_,       "image_mean"       )       )
    self.assertTrue(hasattr(a_,       "image_std"       )       )
    self.assertTrue(hasattr(a_,       "do_convert_rgb"       )       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size,       {"height": 18, "width": 18}       )
    self.assertEqual(image_processor.crop_size,       {"height": 18, "width": 18}       )

    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict,       size=42,       crop_size=84       )
    self.assertEqual(image_processor.size,       {"height": 42, "width": 42}       )
    self.assertEqual(image_processor.crop_size,       {"height": 84, "width": 84}       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_       )

    for image in image_inputs:
       self.assertIsInstance(a_,       Image.Image       )

    # Test not batched input
    UpperCamelCase__			    =    image_processing(image_inputs[0],       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       numpify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       np.ndarray       )

    # Test not batched input
    UpperCamelCase__			    =    image_processing(image_inputs[0],       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       torchify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       torch.Tensor       )

    # Test not batched input
    UpperCamelCase__			    =    image_processing(image_inputs[0],       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )





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    :				int    						=	logging.get_logger(__name__)

# General docstring
__A    :				str    						=	"""ResNetConfig"""

# Base docstring
__A    :				Optional[Any]    						=	"""microsoft/resnet-50"""
__A    :				Tuple    						=	[1, 2_0_4_8, 7, 7]

# Image classification docstring
__A    :				Tuple    						=	"""microsoft/resnet-50"""
__A    :				List[str]    						=	"""tiger cat"""

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




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




							'''simple docstring'''



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





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







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





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




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						ResNetConfig							,  **a							:						Dict      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(
										    config.embedding_size							,  kernel_size=7							,  stride=2							,  activation=config.hidden_act							,  name="""embedder"""							,  )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.MaxPoolaD(pool_size=3							,  strides=2							,  padding="""valid"""							,  name="""pooler"""      )
										SCREAMING_SNAKE_CASE     =       config.num_channels







							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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."""      )
										SCREAMING_SNAKE_CASE     =       pixel_values
										SCREAMING_SNAKE_CASE     =       self.embedder(a      )
										SCREAMING_SNAKE_CASE     =       tf.pad(a							,  [[0, 0], [1, 1], [1, 1], [0, 0]]      )
										SCREAMING_SNAKE_CASE     =       self.pooler(a      )
										return hidden_state





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




							'''simple docstring'''



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







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





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




							'''simple docstring'''



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







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





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




							'''simple docstring'''



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







							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       hidden_state
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.shortcut(a							,  training=a      )
										hidden_state += residual
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						ResNetConfig							,  a							:						int							,  a							:						int							,  a							:						int = 2							,  a							:						int = 2							,  **a							:						Union[str, Any]      )      ->       None:
										super().__init__(**a      )

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

										SCREAMING_SNAKE_CASE     =       [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      )
										]
										SCREAMING_SNAKE_CASE     =       layers







							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										for layer in self.stage_layers:
													SCREAMING_SNAKE_CASE     =       layer(a							,  training=a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						ResNetConfig							,  **a							:						Tuple      )      ->       None:
										super().__init__(**a      )
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										SCREAMING_SNAKE_CASE     =       [
										    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  _UpperCAmelCase  (       self							:						Any							,  a							:						tf.Tensor							,  a							:						bool = False							,  a							:						bool = True							,  a							:						bool = False							,  )      ->       TFBaseModelOutputWithNoAttention:
										SCREAMING_SNAKE_CASE     =       () if output_hidden_states else None

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

													SCREAMING_SNAKE_CASE     =       stage_module(a							,  training=a      )

										if output_hidden_states:
													SCREAMING_SNAKE_CASE     =       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       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  ResNetConfig
							a__    	=  '''resnet'''
							a__    	=  '''pixel_values'''





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





__A    :				Any    						=	r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


__A    :				Optional[Any]    						=	r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""




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




							'''simple docstring'''



							a__    	=  ResNetConfig





							def __init__(       self							:						Optional[Any]							,  a							:						ResNetConfig							,  **a							:						List[Any]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       config
										SCREAMING_SNAKE_CASE     =       TFResNetEmbeddings(a							,  name="""embedder"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetEncoder(a							,  name="""encoder"""      )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.GlobalAveragePoolingaD(keepdims=a      )







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

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

										SCREAMING_SNAKE_CASE     =       encoder_outputs[0]

										SCREAMING_SNAKE_CASE     =       self.pooler(a      )

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

										SCREAMING_SNAKE_CASE     =       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.'''		,   A		,   )
class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							def __init__(       self							:						List[Any]							,  a							:						ResNetConfig							,  **a							:						str      )      ->       None:
										super().__init__(a							,  **a      )
										SCREAMING_SNAKE_CASE     =       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  _UpperCAmelCase  (       self							:						List[Any]							,  a							:						tf.Tensor							,  a							:						Optional[bool] = None							,  a							:						Optional[bool] = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
										SCREAMING_SNAKE_CASE     =       (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										SCREAMING_SNAKE_CASE     =       return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE     =       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.
    '''		,   A		,   )
class       UpperCAmelCase_   ( A		,   A							):




							'''simple docstring'''



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

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

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

										SCREAMING_SNAKE_CASE     =       self.classifier(a      )

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

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

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






