
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

SCREAMING_SNAKE_CASE							:Optional[int]							 =				16
SCREAMING_SNAKE_CASE							:int							 =				32


def 						UpperCAmelCase			(	a_		,   a_ = 1_6						)		->	str:







				"""simple docstring"""



				__A   			= AutoTokenizer.from_pretrained("bert-base-cased"						)
				__A   			= load_dataset("glue"		,   "mrpc"						)

				def tokenize_function(a_						):
								# max_length=None => use the model max length (it's actually the default)
								__A   			= tokenizer(examples["sentence1"]		,   examples["sentence2"]		,   truncation=a_		,   max_length=a_						)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__A   			= datasets.map(
								    a_		,   batched=a_		,   remove_columns=["idx", "sentence1", "sentence2"]		,   )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__A   			= tokenized_datasets.rename_column("label"		,   "labels"						)

				def collate_fn(a_						):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__A   			= 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__A   			= 1_6
								elif accelerator.mixed_precision != "no":
												__A   			= 8
								else:
												__A   			= None

								return tokenizer.pad(
								    a_		,   padding="longest"		,   max_length=a_		,   pad_to_multiple_of=a_		,   return_tensors="pt"		,   )

				# Instantiate dataloaders.
				__A   			= DataLoader(
				    tokenized_datasets["train"]		,   shuffle=a_		,   collate_fn=a_		,   batch_size=a_						)
				__A   			= DataLoader(
				    tokenized_datasets["validation"]		,   shuffle=a_		,   collate_fn=a_		,   batch_size=a_						)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					SCREAMING_SNAKE_CASE							:int							 =				mocked_dataloaders  # noqa: F811


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				if os.environ.get("TESTING_MOCKED_DATALOADERS"		,   a_						) == "1":
								__A   			= 2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__A   			= Accelerator(
								    cpu=args.cpu		,   mixed_precision=args.mixed_precision		,   log_with="all"		,   project_dir=args.project_dir						)
				else:
								__A   			= Accelerator(cpu=args.cpu		,   mixed_precision=args.mixed_precision						)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__A   			= config["lr"]
				__A   			= int(config["num_epochs"]						)
				__A   			= int(config["seed"]						)
				__A   			= int(config["batch_size"]						)
				set_seed(a_						)

				__A			,							__A   			= get_dataloaders(a_		,   a_						)
				__A   			= evaluate.load("glue"		,   "mrpc"						)

				# If the batch size is too big we use gradient accumulation
				__A   			= 1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__A   			= batch_size // MAX_GPU_BATCH_SIZE
								__A   			= MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__A   			= AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,   return_dict=a_						)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__A   			= model.to(accelerator.device						)

				# Instantiate optimizer
				__A   			= AdamW(params=model.parameters()		,   lr=a_						)

				# Instantiate scheduler
				__A   			= get_linear_schedule_with_warmup(
				    optimizer=a_		,   num_warmup_steps=1_0_0		,   num_training_steps=(len(a_						) * num_epochs) // gradient_accumulation_steps		,   )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__A			,							__A			,							__A			,							__A			,							__A   			= accelerator.prepare(
				    a_		,   a_		,   a_		,   a_		,   a_						)

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__A   			= os.path.split(a_						)[-1].split("."						)[0]
								accelerator.init_trackers(a_		,   a_						)

				# Now we train the model
				for epoch in range(a_						):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__A   			= 0
								for step, batch in enumerate(a_						):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device						)
												__A   			= model(**a_						)
												__A   			= outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__A   			= loss / gradient_accumulation_steps
												accelerator.backward(a_						)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(a_						):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device						)
												with torch.no_grad():
																__A   			= model(**a_						)
												__A   			= outputs.logits.argmax(dim=-1						)
												__A			,							__A   			= accelerator.gather_for_metrics((predictions, batch["labels"])						)
												metric.add_batch(
												    predictions=a_		,   references=a_		,   )

								__A   			= metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:'''		,   a_						)

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(a_						),
												        "epoch": epoch,
												    }		,   step=a_		,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()


def 						UpperCAmelCase			(	)		->	Tuple:







				"""simple docstring"""



				__A   			= argparse.ArgumentParser(description="Simple example of training script."						)
				parser.add_argument(
				    "--mixed_precision"		,   type=a_		,   default=a_		,   choices=["no", "fp16", "bf16", "fp8"]		,   help="Whether to use mixed precision. Choose"
				    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
				    "and an Nvidia Ampere GPU."		,   )
				parser.add_argument("--cpu"		,   action="store_true"		,   help="If passed, will train on the CPU."						)
				parser.add_argument(
				    "--with_tracking"		,   action="store_true"		,   help="Whether to load in all available experiment trackers from the environment and use them for logging."		,   )
				parser.add_argument(
				    "--project_dir"		,   type=a_		,   default="logs"		,   help="Location on where to store experiment tracking logs` and relevent project information"		,   )
				__A   			= parser.parse_args()
				__A   			= {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(a_		,   a_						)


if __name__ == "__main__":
					main()


from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE							:str							 =				'RegNetConfig'

# Base docstring
SCREAMING_SNAKE_CASE							:List[str]							 =				'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				[1, 1088, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE							:Any							 =				'tabby, tabby cat'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





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




	'''simple docstring'''





	def __init__(       self					: Tuple							,A					: int							,A					: int = 3							,A					: int = 1							,A					: int = 1							,A					: Optional[str] = "relu"							,**A					: Dict							,):
					super().__init__(**A )
					# The padding and conv has been verified in
					# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
					__A   			= tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
					__A   			= tf.keras.layers.ConvaD(
					    filters=A							,kernel_size=A							,strides=A							,padding="VALID"							,groups=A							,use_bias=A							,name="convolution"							,)
					__A   			= tf.keras.layers.BatchNormalization(epsilon=1E-5							,momentum=0.9							,name="normalization" )
					__A   			= ACTaFN[activation] if activation is not None else tf.identity







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Any ):
					__A   			= self.convolution(self.padding(A ) )
					__A   			= self.normalization(A )
					__A   			= self.activation(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: Tuple							,A					: RegNetConfig							,**A					: str ):
					super().__init__(**A )
					__A   			= config.num_channels
					__A   			= TFRegNetConvLayer(
					    out_channels=config.embedding_size							,kernel_size=3							,stride=2							,activation=config.hidden_act							,name="embedder"							,)







	def 				UpperCamelCase_  (       self					: Tuple							,A					: Optional[Any] ):
					__A   			= shape_list(A )[1]
					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." )

					# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
					# So change the input format from `NCHW` to `NHWC`.
					# shape = (batch_size, in_height, in_width, in_channels=num_channels)
					__A   			= tf.transpose(A							,perm=(0, 2, 3, 1) )
					__A   			= self.embedder(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: int							,A					: int = 2							,**A					: Tuple ):
					super().__init__(**A )
					__A   			= tf.keras.layers.ConvaD(
					    filters=A							,kernel_size=1							,strides=A							,use_bias=A							,name="convolution" )
					__A   			= tf.keras.layers.BatchNormalization(epsilon=1E-5							,momentum=0.9							,name="normalization" )







	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: tf.Tensor							,A					: bool = False ):
					return self.normalization(self.convolution(A )							,training=A )





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




	'''simple docstring'''





	def __init__(       self					: Dict							,A					: int							,A					: int							,**A					: str ):
					super().__init__(**A )
					__A   			= tf.keras.layers.GlobalAveragePoolingaD(keepdims=A							,name="pooler" )
					__A   			= [
					    tf.keras.layers.ConvaD(filters=A							,kernel_size=1							,activation="relu"							,name="attention.0" ),
					    tf.keras.layers.ConvaD(filters=A							,kernel_size=1							,activation="sigmoid"							,name="attention.2" ),
					]







	def 				UpperCamelCase_  (       self					: Dict							,A					: List[Any] ):
					# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
					__A   			= self.pooler(A )
					for layer_module in self.attention:
									__A   			= layer_module(A )
					__A   			= hidden_state * pooled
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: RegNetConfig							,A					: int							,A					: int							,A					: int = 1							,**A					: Optional[int] ):
					super().__init__(**A )
					__A   			= in_channels != out_channels or stride != 1
					__A   			= max(1							,out_channels // config.groups_width )
					__A   			= (
					    TFRegNetShortCut(A							,stride=A							,name="shortcut" )
					    if should_apply_shortcut
					    else tf.keras.layers.Activation("linear"							,name="shortcut" )
					)
					# `self.layers` instead of `self.layer` because that is a reserved argument.
					__A   			= [
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=config.hidden_act							,name="layer.0" ),
					    TFRegNetConvLayer(
					        A							,stride=A							,groups=A							,activation=config.hidden_act							,name="layer.1" ),
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=A							,name="layer.2" ),
					]
					__A   			= ACTaFN[config.hidden_act]







	def 				UpperCamelCase_  (       self					: int							,A					: Optional[int] ):
					__A   			= hidden_state
					for layer_module in self.layers:
									__A   			= layer_module(A )
					__A   			= self.shortcut(A )
					hidden_state += residual
					__A   			= self.activation(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: RegNetConfig							,A					: int							,A					: int							,A					: int = 1							,**A					: str ):
					super().__init__(**A )
					__A   			= in_channels != out_channels or stride != 1
					__A   			= max(1							,out_channels // config.groups_width )
					__A   			= (
					    TFRegNetShortCut(A							,stride=A							,name="shortcut" )
					    if should_apply_shortcut
					    else tf.keras.layers.Activation("linear"							,name="shortcut" )
					)
					__A   			= [
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=config.hidden_act							,name="layer.0" ),
					    TFRegNetConvLayer(
					        A							,stride=A							,groups=A							,activation=config.hidden_act							,name="layer.1" ),
					    TFRegNetSELayer(A							,reduced_channels=int(round(in_channels / 4 ) )							,name="layer.2" ),
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=A							,name="layer.3" ),
					]
					__A   			= ACTaFN[config.hidden_act]







	def 				UpperCamelCase_  (       self					: Dict							,A					: Any ):
					__A   			= hidden_state
					for layer_module in self.layers:
									__A   			= layer_module(A )
					__A   			= self.shortcut(A )
					hidden_state += residual
					__A   			= self.activation(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: RegNetConfig							,A					: int							,A					: int							,A					: int = 2							,A					: int = 2							,**A					: Optional[int] ):
					super().__init__(**A )

					__A   			= TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
					__A   			= [
					    # downsampling is done in the first layer with stride of 2
					    layer(A							,A							,A							,stride=A							,name="layers.0" ),
					    *[layer(A							,A							,A							,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
					]







	def 				UpperCamelCase_  (       self					: Any							,A					: List[str] ):
					for layer_module in self.layers:
									__A   			= layer_module(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: Any							,A					: RegNetConfig							,**A					: List[str] ):
					super().__init__(**A )
					__A   			= []
					# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
					self.stages.append(
					    TFRegNetStage(
					        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"							,) )
					__A   			= zip(config.hidden_sizes							,config.hidden_sizes[1:] )
					for i, ((in_channels, out_channels), depth) in enumerate(zip(A							,config.depths[1:] ) ):
									self.stages.append(TFRegNetStage(A							,A							,A							,depth=A							,name=f'''stages.{i+1}''' ) )







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

					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 )





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




	'''simple docstring'''





	snake_case_					    =     RegNetConfig






	def __init__(       self					: int							,A					: Optional[int]							,**A					: Dict ):
					super().__init__(**A )
					__A   			= config
					__A   			= TFRegNetEmbeddings(A							,name="embedder" )
					__A   			= TFRegNetEncoder(A							,name="encoder" )
					__A   			= tf.keras.layers.GlobalAveragePoolingaD(keepdims=A							,name="pooler" )







	@unpack_inputs
	def 				UpperCamelCase_  (       self					: Tuple							,A					: tf.Tensor							,A					: Optional[bool] = None							,A					: Optional[bool] = None							,A					: bool = False							,):
					__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.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 )

					# Change to NCHW output format have uniformity in the modules
					__A   			= tf.transpose(A							,perm=(0, 3, 1, 2) )
					__A   			= tf.transpose(A							,perm=(0, 3, 1, 2) )

					# Change the other hidden state outputs to NCHW as well
					if output_hidden_states:
									__A   			= tuple([tf.transpose(A							,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )

					if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

					return TFBaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=A							,pooler_output=A							,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states							,)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     RegNetConfig
	snake_case_					    =     "regnet"
	snake_case_					    =     "pixel_values"






	@property
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)							,dtype=tf.floataa )}





SCREAMING_SNAKE_CASE							:Dict							 =				R'\n    Parameters:\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        config ([`RegNetConfig`]): 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'

SCREAMING_SNAKE_CASE							: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            [`ConveNextImageProcessor.__call__`] for details.\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'





@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."			,			__SCREAMING_SNAKE_CASE			,			)
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: RegNetConfig							,*A					: List[Any]							,**A					: str ):
					super().__init__(A							,*A							,**A )
					__A   			= TFRegNetMainLayer(A							,name="regnet" )







	@unpack_inputs
	@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							,)
	def 				UpperCamelCase_  (       self					: Tuple							,A					: tf.Tensor							,A					: Optional[bool] = None							,A					: Optional[bool] = None							,A					: int=False							,):
					__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.regnet(
					    pixel_values=A							,output_hidden_states=A							,return_dict=A							,training=A							,)
					if not return_dict:
									return (outputs[0],) + outputs[1:]

					return TFBaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=outputs.last_hidden_state							,pooler_output=outputs.pooler_output							,hidden_states=outputs.hidden_states							,)





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




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: RegNetConfig							,*A					: str							,**A					: Tuple ):
					super().__init__(A							,*A							,**A )
					__A   			= config.num_labels
					__A   			= TFRegNetMainLayer(A							,name="regnet" )
					# classification head
					__A   			= [
					    tf.keras.layers.Flatten(),
					    tf.keras.layers.Dense(config.num_labels							,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
					]







	@unpack_inputs
	@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							,)
	def 				UpperCamelCase_  (       self					: List[str]							,A					: tf.Tensor = None							,A					: tf.Tensor = None							,A					: bool = None							,A					: bool = None							,A					: Union[str, Any]=False							,):
					__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.regnet(
					    A							,output_hidden_states=A							,return_dict=A							,training=A )

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

					__A   			= self.classifier[0](A )
					__A   			= self.classifier[1](A )

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

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

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