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


__lowercase							 =					logging.get_logger(__name__)

# General docstring
__lowercase							 =					'''RegNetConfig'''

# Base docstring
__lowercase							 =					'''facebook/regnet-y-040'''
__lowercase							 =					[1, 1088, 7, 7]

# Image classification docstring
__lowercase							 =					'''facebook/regnet-y-040'''
__lowercase							 =					'''tabby, tabby cat'''

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


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






    '''simple docstring'''






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







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :Optional[int]			  =		self.convolution(self.padding(__lowercase))
        __UpperCamelCase     :Optional[int]			  =		self.normalization(__lowercase)
        __UpperCamelCase     :Tuple			  =		self.activation(__lowercase)
        return hidden_state


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






    '''simple docstring'''






    def __init__(	self , __lowercase , **__lowercase)       ->	int:
        super().__init__(**__lowercase)
        __UpperCamelCase     :Tuple			  =		config.num_channels
        __UpperCamelCase     :Optional[int]			  =		TFRegNetConvLayer(
            out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Dict:
        __UpperCamelCase     :Dict			  =		shape_list(__lowercase)[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)
        __UpperCamelCase     :Dict			  =		tf.transpose(__lowercase , perm=(0, 2, 3, 1))
        __UpperCamelCase     :Optional[int]			  =		self.embedder(__lowercase)
        return hidden_state


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






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase = 2 , **__lowercase)       ->	Optional[Any]:
        super().__init__(**__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		tf.keras.layers.ConvaD(
            filters=__lowercase , kernel_size=1 , strides=__lowercase , use_bias=__lowercase , name='''convolution''')
        __UpperCamelCase     :int			  =		tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = False)       ->	tf.Tensor:
        return self.normalization(self.convolution(__lowercase) , training=__lowercase)


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






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase , **__lowercase)       ->	Union[str, Any]:
        super().__init__(**__lowercase)
        __UpperCamelCase     :int			  =		tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase , name='''pooler''')
        __UpperCamelCase     :List[Any]			  =		[
            tf.keras.layers.ConvaD(filters=__lowercase , kernel_size=1 , activation='''relu''' , name='''attention.0'''),
            tf.keras.layers.ConvaD(filters=__lowercase , kernel_size=1 , activation='''sigmoid''' , name='''attention.2'''),
        ]







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Any:
        # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
        __UpperCamelCase     :List[str]			  =		self.pooler(__lowercase)
        for layer_module in self.attention:
            __UpperCamelCase     :List[str]			  =		layer_module(__lowercase)
        __UpperCamelCase     :int			  =		hidden_state * pooled
        return hidden_state


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






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase = 1 , **__lowercase)       ->	Tuple:
        super().__init__(**__lowercase)
        __UpperCamelCase     :str			  =		in_channels != out_channels or stride != 1
        __UpperCamelCase     :Any			  =		max(1 , out_channels // config.groups_width)
        __UpperCamelCase     :Optional[Any]			  =		(
            TFRegNetShortCut(__lowercase , stride=__lowercase , 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.
        __UpperCamelCase     :Optional[int]			  =		[
            TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=config.hidden_act , name='''layer.0'''),
            TFRegNetConvLayer(
                __lowercase , stride=__lowercase , groups=__lowercase , activation=config.hidden_act , name='''layer.1'''),
            TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=__lowercase , name='''layer.2'''),
        ]
        __UpperCamelCase     :Dict			  =		ACTaFN[config.hidden_act]







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Any:
        __UpperCamelCase     :Optional[int]			  =		hidden_state
        for layer_module in self.layers:
            __UpperCamelCase     :Any			  =		layer_module(__lowercase)
        __UpperCamelCase     :Tuple			  =		self.shortcut(__lowercase)
        hidden_state += residual
        __UpperCamelCase     :Optional[Any]			  =		self.activation(__lowercase)
        return hidden_state


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






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase = 1 , **__lowercase)       ->	Union[str, Any]:
        super().__init__(**__lowercase)
        __UpperCamelCase     :int			  =		in_channels != out_channels or stride != 1
        __UpperCamelCase     :Optional[int]			  =		max(1 , out_channels // config.groups_width)
        __UpperCamelCase     :Dict			  =		(
            TFRegNetShortCut(__lowercase , stride=__lowercase , name='''shortcut''')
            if should_apply_shortcut
            else tf.keras.layers.Activation('''linear''' , name='''shortcut''')
        )
        __UpperCamelCase     :Dict			  =		[
            TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=config.hidden_act , name='''layer.0'''),
            TFRegNetConvLayer(
                __lowercase , stride=__lowercase , groups=__lowercase , activation=config.hidden_act , name='''layer.1'''),
            TFRegNetSELayer(__lowercase , reduced_channels=int(round(in_channels / 4)) , name='''layer.2'''),
            TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=__lowercase , name='''layer.3'''),
        ]
        __UpperCamelCase     :Dict			  =		ACTaFN[config.hidden_act]







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[int]:
        __UpperCamelCase     :str			  =		hidden_state
        for layer_module in self.layers:
            __UpperCamelCase     :Optional[Any]			  =		layer_module(__lowercase)
        __UpperCamelCase     :List[Any]			  =		self.shortcut(__lowercase)
        hidden_state += residual
        __UpperCamelCase     :List[str]			  =		self.activation(__lowercase)
        return hidden_state


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






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase = 2 , __lowercase = 2 , **__lowercase)       ->	int:
        super().__init__(**__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        for layer_module in self.layers:
            __UpperCamelCase     :Tuple			  =		layer_module(__lowercase)
        return hidden_state


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






    '''simple docstring'''






    def __init__(	self , __lowercase , **__lowercase)       ->	str:
        super().__init__(**__lowercase)
        __UpperCamelCase     :Any			  =		[]
        # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
        self.stages.append(
            TFRegNetStage(
                __lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ))
        __UpperCamelCase     :str			  =		zip(config.hidden_sizes , config.hidden_sizes[1:])
        for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowercase , config.depths[1:])):
            self.stages.append(TFRegNetStage(__lowercase , __lowercase , __lowercase , depth=__lowercase , name=f"""stages.{i+1}"""))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = False , __lowercase = True)       ->	TFBaseModelOutputWithNoAttention:
        __UpperCamelCase     :Optional[Any]			  =		() if output_hidden_states else None

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

            __UpperCamelCase     :Optional[Any]			  =		stage_module(__lowercase)

        if output_hidden_states:
            __UpperCamelCase     :Tuple			  =		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=__lowercase , hidden_states=__lowercase)


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






    '''simple docstring'''






    a__	:						Optional[Any]						=      RegNetConfig







    def __init__(	self , __lowercase , **__lowercase)       ->	Union[str, Any]:
        super().__init__(**__lowercase)
        __UpperCamelCase     :List[str]			  =		config
        __UpperCamelCase     :List[Any]			  =		TFRegNetEmbeddings(__lowercase , name='''embedder''')
        __UpperCamelCase     :Any			  =		TFRegNetEncoder(__lowercase , name='''encoder''')
        __UpperCamelCase     :Optional[int]			  =		tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase , name='''pooler''')







    @unpack_inputs
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = False , )       ->	TFBaseModelOutputWithPoolingAndNoAttention:
        __UpperCamelCase     :Optional[Any]			  =		(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __UpperCamelCase     :Union[str, Any]			  =		return_dict if return_dict is not None else self.config.use_return_dict

        __UpperCamelCase     :Union[str, Any]			  =		self.embedder(__lowercase , training=__lowercase)

        __UpperCamelCase     :Any			  =		self.encoder(
            __lowercase , output_hidden_states=__lowercase , return_dict=__lowercase , training=__lowercase)

        __UpperCamelCase     :List[str]			  =		encoder_outputs[0]
        __UpperCamelCase     :List[str]			  =		self.pooler(__lowercase)

        # Change to NCHW output format have uniformity in the modules
        __UpperCamelCase     :str			  =		tf.transpose(__lowercase , perm=(0, 3, 1, 2))
        __UpperCamelCase     :List[Any]			  =		tf.transpose(__lowercase , perm=(0, 3, 1, 2))

        # Change the other hidden state outputs to NCHW as well
        if output_hidden_states:
            __UpperCamelCase     :List[str]			  =		tuple([tf.transpose(__lowercase , 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=__lowercase , pooler_output=__lowercase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      RegNetConfig
    a__	:						List[str]						=      """regnet"""
    a__	:						Optional[int]						=      """pixel_values"""







    @property
    def 			UpperCamelCase__   (	self)       ->	str:
        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa)}
__lowercase							 =					r'''
    Parameters:
    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.
        config ([`RegNetConfig`]): 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.
'''

__lowercase							 =					r'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__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.
'''


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






    '''simple docstring'''






    def __init__(	self , __lowercase , *__lowercase , **__lowercase)       ->	List[Any]:
        super().__init__(__lowercase , *__lowercase , **__lowercase)
        __UpperCamelCase     :Tuple			  =		TFRegNetMainLayer(__lowercase , name='''regnet''')







    @unpack_inputs
    @add_start_docstrings_to_model_forward(__lowercase)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=False , )       ->	Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
        __UpperCamelCase     :int			  =		(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __UpperCamelCase     :Optional[Any]			  =		return_dict if return_dict is not None else self.config.use_return_dict

        __UpperCamelCase     :List[Any]			  =		self.regnet(
            pixel_values=__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase , training=__lowercase , )
        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(
    """
    RegNet 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_ ):






    '''simple docstring'''






    def __init__(	self , __lowercase , *__lowercase , **__lowercase)       ->	int:
        super().__init__(__lowercase , *__lowercase , **__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		config.num_labels
        __UpperCamelCase     :str			  =		TFRegNetMainLayer(__lowercase , name='''regnet''')
        # classification head
        __UpperCamelCase     :List[Any]			  =		[
            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(__lowercase)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
    def 			UpperCamelCase__   (	self , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase=False , )       ->	Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        __UpperCamelCase     :Optional[int]			  =		(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __UpperCamelCase     :int			  =		return_dict if return_dict is not None else self.config.use_return_dict

        __UpperCamelCase     :Union[str, Any]			  =		self.regnet(
            __lowercase , output_hidden_states=__lowercase , return_dict=__lowercase , training=__lowercase)

        __UpperCamelCase     :List[str]			  =		outputs.pooler_output if return_dict else outputs[1]

        __UpperCamelCase     :str			  =		self.classifier[0](__lowercase)
        __UpperCamelCase     :List[Any]			  =		self.classifier[1](__lowercase)

        __UpperCamelCase     :Tuple			  =		None if labels is None else self.hf_compute_loss(labels=__lowercase , logits=__lowercase)

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

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






"""simple docstring"""


import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


UpperCAmelCase__     						=  1_6
UpperCAmelCase__     						=  3_2






def 			__UpperCAmelCase    (	lowercase      ,lowercase = 16						):







  """simple docstring"""
  _UpperCAmelCase         =					AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  _UpperCAmelCase         =					load_dataset("""glue"""      ,"""mrpc"""						)

  def tokenize_function(lowercase						):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCAmelCase         =					tokenizer(examples["""sentence1"""]      ,examples["""sentence2"""]      ,truncation=lowercase      ,max_length=lowercase						)
    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():
    _UpperCAmelCase         =					datasets.map(
        lowercase      ,batched=lowercase      ,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
  _UpperCAmelCase         =					tokenized_datasets.rename_column("""label"""      ,"""labels"""						)

  def collate_fn(lowercase						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCAmelCase         =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _UpperCAmelCase         =					16
    elif accelerator.mixed_precision != "no":
      _UpperCAmelCase         =					8
    else:
      _UpperCAmelCase         =					None

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

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

  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

    UpperCAmelCase__     						=  mocked_dataloaders  # noqa: F811






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,lowercase						) == "1":
    _UpperCAmelCase         =					2
  # Initialize accelerator
  _UpperCAmelCase         =					Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCAmelCase         =					config["""lr"""]
  _UpperCAmelCase         =					int(config["""num_epochs"""]						)
  _UpperCAmelCase         =					int(config["""seed"""]						)
  _UpperCAmelCase         =					int(config["""batch_size"""]						)

  _UpperCAmelCase         =					evaluate.load("""glue"""      ,"""mrpc"""						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowercase						)
  def inner_training_loop(lowercase						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(lowercase						)

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

    # 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).
    _UpperCAmelCase         =					model.to(accelerator.device						)

    # Instantiate optimizer
    _UpperCAmelCase         =					AdamW(params=model.parameters()      ,lr=lowercase						)
    _UpperCAmelCase       , _UpperCAmelCase         =					get_dataloaders(lowercase      ,lowercase						)

    # Instantiate scheduler
    _UpperCAmelCase         =					get_linear_schedule_with_warmup(
        optimizer=lowercase      ,num_warmup_steps=1_00      ,num_training_steps=(len(lowercase						) * num_epochs)      ,)

    # 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.
    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase         =					accelerator.prepare(
        lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # Now we train the model
    for epoch in range(lowercase						):
      model.train()
      for step, batch in enumerate(lowercase						):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        _UpperCAmelCase         =					model(**lowercase						)
        _UpperCAmelCase         =					outputs.loss
        accelerator.backward(lowercase						)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

      _UpperCAmelCase         =					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''      ,lowercase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					argparse.ArgumentParser(description="""Simple example of training script."""						)
  parser.add_argument(
      """--mixed_precision"""      ,type=lowercase      ,default=lowercase      ,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."""						)
  _UpperCAmelCase         =					parser.parse_args()
  _UpperCAmelCase         =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase      ,lowercase						)


if __name__ == "__main__":
    main()
