




import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


__a :    str								=				logging.get_logger(__name__)


class  __lowercase :







     '''simple docstring'''




     def __init__(			self    :     Tuple	,		UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     Optional[int]=21	,		UpperCamelCase_    :     Optional[Any]=[]	,		UpperCamelCase_    :     Optional[int]=None			):


         """simple docstring"""



         __A             =		model
         __A             =		trace_batch_nums
         __A             =		abort_after_batch_num

         # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
         __A             =		collections.deque([]	,		UpperCamelCase_			)
         __A             =		[]
         __A             =		0
         __A             =		0
         __A             =		False
         __A             =		"""                 """

         self.analyse_model()

         self.register_forward_hook()




     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     Tuple=None			):


         """simple docstring"""



         if frame is not None:
             self.expand_frame(UpperCamelCase_			)
         self.frames.append("""\n""".join(self.frame			)			)
         __A             =		[]  # start a new frame




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         self.frame.append(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         print("""\n""".join(self.frames			)			)
         __A             =		[]




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		[]




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         print(F"\nDetected inf/nan during batch_number={self.batch_number}"			)
         print(F"Last {len(self.frames			)} forward frames:"			)
         print(F"{'abs min':8} {'abs max':8} metadata"			)
         print("""\n""".join(self.frames			)			)
         print("""\n\n"""			)
         __A             =		[]




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         __A             =		{m: name for name, m in self.model.named_modules()}
         # self.longest_module_name = max(len(v) for v in self.module_names.values())




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     Dict	,		UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         if torch.is_tensor(UpperCamelCase_			):
             self.expand_frame(get_abs_min_max(UpperCamelCase_	,		UpperCamelCase_			)			)
             if detect_overflow(UpperCamelCase_	,		UpperCamelCase_			):
                 __A             =		True
         elif var is None:
             self.expand_frame(F"{'None':>17} {ctx}"			)
         else:
             self.expand_frame(F"{'not a tensor':>17} {ctx}"			)




     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"			)
         self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"			)




     def 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"			)




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     Dict			):


         """simple docstring"""



         self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"			)

         # params
         for name, p in module.named_parameters(recurse=UpperCamelCase_			):
             self.analyse_variable(UpperCamelCase_	,		UpperCamelCase_			)

         # inputs
         if isinstance(UpperCamelCase_	,		UpperCamelCase_			):
             for i, x in enumerate(UpperCamelCase_			):
                 self.analyse_variable(UpperCamelCase_	,		F"input[{i}]"			)
         else:
             self.analyse_variable(UpperCamelCase_	,		"""input"""			)

         # outputs
         if isinstance(UpperCamelCase_	,		UpperCamelCase_			):
             for i, x in enumerate(UpperCamelCase_			):
                 # possibly a tuple of tuples
                 if isinstance(UpperCamelCase_	,		UpperCamelCase_			):
                     for j, y in enumerate(UpperCamelCase_			):
                         self.analyse_variable(UpperCamelCase_	,		F"output[{i}][{j}]"			)
                 else:
                     self.analyse_variable(UpperCamelCase_	,		F"output[{i}]"			)
         else:
             self.analyse_variable(UpperCamelCase_	,		"""output"""			)

         self.save_frame()




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         self.model.apply(self._register_forward_hook			)




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Any			):


         """simple docstring"""



         module.register_forward_hook(self.forward_hook			)





     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     Union[str, Any]	,		UpperCamelCase_    :     List[str]	,		UpperCamelCase_    :     Optional[int]			):


         """simple docstring"""



         __A             =		False

         __A             =		True if self.batch_number in self.trace_batch_nums else False
         if trace_mode:
             self.reset_saved_frames()

         if self.total_calls == 0:
             self.batch_start_frame()
         self.total_calls += 1

         # count batch numbers - the very first forward hook of the batch will be called when the
         # batch completes - i.e. it gets called very last - we know this batch has finished
         if module == self.model:
             self.batch_number += 1
             __A             =		True

         self.create_frame(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)

         # if last_frame_of_batch:
         #     self.batch_end_frame()

         if trace_mode:
             self.trace_frames()

         if last_frame_of_batch:
             self.batch_start_frame()

         if self.detected_overflow and not trace_mode:
             self.dump_saved_frames()

             # now we can abort, as it's pointless to continue running
             raise ValueError(
                 """DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. """
                 """Please scroll up above this traceback to see the activation values prior to this event."""			)

         # abort after certain batch if requested to do so
         if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
             raise ValueError(
                 F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
                 F" `abort_after_batch_num={self.abort_after_batch_num}` arg"			)



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any     ,			__lowercase				:     Union[str, Any]      )  -> Any:





    """simple docstring"""

    __A             =		var.abs()
    return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     List[Any]      )  -> int:





    """simple docstring"""

    __A             =		False
    if torch.isnan(__lowercase      ).any().item():
        __A             =		True
        print(f"{ctx} has nans"      )
    if torch.isinf(__lowercase      ).any().item():
        __A             =		True
        print(f"{ctx} has infs"      )

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        __A             =		var[torch.ge(var.abs()     ,			1_0_0      )]
        if naaa.numel() > 0:
            print(f"{ctx}:  n100={naaa.numel()}"      )
        __A             =		var[torch.ge(var.abs()     ,			1_0_0_0      )]
        if naaaa.numel() > 0:
            print(f"{ctx}: n1000={naaaa.numel()}"      )
        __A             =		var[torch.ge(var.abs()     ,			1_0_0_0_0      )]
        if naaaaa.numel() > 0:
            print(f"{ctx}: n10000={naaaaa.numel()}"      )

    if 0:
        print(f"min={var.min():9.2e} max={var.max():9.2e}"      )

    if 0:
        print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"      )

    return detected






class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"underflow_overflow"
     SCREAMING_SNAKE_CASE				  =			"tpu_metrics_debug"












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			)




