






from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_MODEL_FOR_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_MASKED_LM_MAPPING,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       TFSharedEmbeddings,
   )

   if _tf_gpu_memory_limit is not None:
      _UpperCAmelCase					:						Optional[int]         =     tf.config.list_physical_devices('GPU')
      for gpu in gpus:
         # Restrict TensorFlow to only allocate x GB of memory on the GPUs
         try:
            tf.config.set_logical_device_configuration(
                gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
            )
            _UpperCAmelCase					:						str         =     tf.config.list_logical_devices('GPU')
            print('Logical GPUs', logical_gpus)
         except RuntimeError as e:
            # Virtual devices must be set before GPUs have been initialized
            print(e)


@require_tf
class 		lowerCAmelCase_				:
     __lowerCamelCase       :    Union[str, Any] 	=     None
     __lowerCamelCase       :    Dict 	=     ()
     __lowerCamelCase       :    int 	=     ()
     __lowerCamelCase       :    Dict 	=     True
     __lowerCamelCase       :    Dict 	=     True
     __lowerCamelCase       :    Optional[int] 	=     True
     __lowerCamelCase       :    Dict 	=     False





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase=False  )		->				dict:
        lowerCamelCase									=copy.deepcopy(_UpperCAmelCase  )

        if model_class in get_values(_UpperCAmelCase  ):
           lowerCamelCase									={
               k: tf.tile(tf.expand_dims(_UpperCAmelCase							, 1  )							, (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)  )
               if isinstance(_UpperCAmelCase							, tf.Tensor  ) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

        if return_labels:
           if model_class in get_values(_UpperCAmelCase  ):
              lowerCamelCase									=tf.ones(self.model_tester.batch_size							, dtype=tf.intaa  )
           elif model_class in get_values(_UpperCAmelCase  ):
              lowerCamelCase									=tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa  )
              lowerCamelCase									=tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa  )
           elif model_class in [
               *get_values(_UpperCAmelCase  ),
               *get_values(_UpperCAmelCase  ),
           ]:
              lowerCamelCase									=tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa  )
           elif model_class in get_values(_UpperCAmelCase  ):
              lowerCamelCase									=tf.zeros(self.model_tester.batch_size							, dtype=tf.intaa  )
           elif model_class in [
               *get_values(_UpperCAmelCase  ),
               *get_values(_UpperCAmelCase  ),
               *get_values(_UpperCAmelCase  ),
               *get_values(_UpperCAmelCase  ),
               *get_values(_UpperCAmelCase  ),
           ]:
              lowerCamelCase									=tf.zeros(
                  (self.model_tester.batch_size, self.model_tester.seq_length)							, dtype=tf.intaa  )
        return inputs_dict





     @slow
     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )
           lowerCamelCase									=model_class(_UpperCAmelCase  )

           @tf.function
           def run_in_graph_mode():
              return model(_UpperCAmelCase  )

           lowerCamelCase									=run_in_graph_mode()
           self.assertIsNotNone(_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )
           lowerCamelCase									=model_class(_UpperCAmelCase  )

           @tf.function(experimental_compile=_UpperCAmelCase  )
           def run_in_graph_mode():
              return model(_UpperCAmelCase  )

           lowerCamelCase									=run_in_graph_mode()
           self.assertIsNotNone(_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           if getattr(_UpperCAmelCase							, """hf_compute_loss"""							, _UpperCAmelCase  ):
              # Test that model correctly compute the loss with kwargs
              lowerCamelCase									=self._prepare_for_class(inputs_dict.copy()							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
              # Is there a better way to remove these decoder inputs?
              lowerCamelCase									={
                  key: val
                  for key, val in prepared_for_class.items()
                  if key not in ("""head_mask""", """decoder_head_mask""", """cross_attn_head_mask""", """decoder_input_ids""")
              }

              lowerCamelCase									={
                  """labels""",
                  """label""",
                  """label_ids""",
                  """start_positions""",
                  """start_position""",
                  """end_positions""",
                  """end_position""",
                  """next_sentence_label""",
              }
              lowerCamelCase									=possible_label_cols.intersection(set(_UpperCAmelCase  )  )
              self.assertGreater(len(_UpperCAmelCase  )							, 0							, msg="""No matching label names found!"""  )
              lowerCamelCase									={key: val for key, val in prepared_for_class.items() if key in label_names}
              lowerCamelCase									={key: val for key, val in prepared_for_class.items() if key not in label_names}
              self.assertGreater(len(_UpperCAmelCase  )							, 0  )

              # Make sure it works with XLA!
              model.compile(optimizer=tf.keras.optimizers.SGD(0.0  )							, jit_compile=_UpperCAmelCase  )
              # Make sure the model fits without crashing regardless of where we pass the labels
              lowerCamelCase									=model.fit(
                  _UpperCAmelCase							, validation_data=_UpperCAmelCase							, steps_per_epoch=1							, validation_steps=1							, shuffle=_UpperCAmelCase							, verbose=0							, )
              lowerCamelCase									=history.history["""loss"""][0]
              self.assertTrue(not isnan(_UpperCAmelCase  )  )
              lowerCamelCase									=history.history["""val_loss"""][0]
              self.assertTrue(not isnan(_UpperCAmelCase  )  )

              # Now test it with separate labels, to make sure that path works in XLA too.
              lowerCamelCase									=model_class(_UpperCAmelCase  )
              model.compile(optimizer=tf.keras.optimizers.SGD(0.0  )							, jit_compile=_UpperCAmelCase  )
              lowerCamelCase									=model.fit(
                  _UpperCAmelCase							, _UpperCAmelCase							, validation_data=(inputs_minus_labels, labels)							, steps_per_epoch=1							, validation_steps=1							, shuffle=_UpperCAmelCase							, verbose=0							, )

              lowerCamelCase									=history.history["""loss"""][0]
              self.assertTrue(not isnan(_UpperCAmelCase  )  )
              lowerCamelCase									=history.history["""val_loss"""][0]
              self.assertTrue(not isnan(_UpperCAmelCase  )  )





     @slow
     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        lowerCamelCase									=True
        lowerCamelCase									=True

        if hasattr(_UpperCAmelCase							, """use_cache"""  ):
           lowerCamelCase									=True

        lowerCamelCase									=getattr(self.model_tester							, """encoder_seq_length"""							, self.model_tester.seq_length  )
        lowerCamelCase									=getattr(self.model_tester							, """key_length"""							, _UpperCAmelCase  )

        for model_class in self.all_model_classes[:2]:
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.build()
           lowerCamelCase									=len(model(_UpperCAmelCase  )  )

           for key in list(class_inputs_dict.keys()  ):
              # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
              if key not in model.input_signature:
                 del class_inputs_dict[key]
              # Check it's a tensor, in case the inputs dict has some bools in it too
              elif isinstance(class_inputs_dict[key]							, tf.Tensor  ) and class_inputs_dict[key].dtype.is_integer:
                 lowerCamelCase									=tf.cast(class_inputs_dict[key]							, tf.intaa  )

           if set(class_inputs_dict.keys()  ) != set(model.input_signature.keys()  ):
              continue  # Some models have inputs that the preparation functions don't create, we skip those

           with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(_UpperCAmelCase							, saved_model=_UpperCAmelCase  )
              lowerCamelCase									=os.path.join(_UpperCAmelCase							, """saved_model"""							, """1"""  )
              lowerCamelCase									=tf.keras.models.load_model(_UpperCAmelCase  )
              lowerCamelCase									=model(_UpperCAmelCase  )

              if self.is_encoder_decoder:
                 lowerCamelCase									=outputs["""encoder_hidden_states"""]
                 lowerCamelCase									=outputs["""encoder_attentions"""]
              else:
                 lowerCamelCase									=outputs["""hidden_states"""]
                 lowerCamelCase									=outputs["""attentions"""]

              self.assertEqual(len(_UpperCAmelCase  )							, _UpperCAmelCase  )

              lowerCamelCase									=getattr(
                  self.model_tester							, """expected_num_hidden_layers"""							, self.model_tester.num_hidden_layers + 1  )

              self.assertEqual(len(_UpperCAmelCase  )							, _UpperCAmelCase  )
              self.assertListEqual(
                  list(output_hidden_states[0].shape[-2:]  )							, [self.model_tester.seq_length, self.model_tester.hidden_size]							, )

              self.assertEqual(len(_UpperCAmelCase  )							, self.model_tester.num_hidden_layers  )
              self.assertListEqual(
                  list(output_attentions[0].shape[-3:]  )							, [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							, )





     @slow
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        tf.keras.mixed_precision.set_global_policy("""mixed_float16"""  )

        # try/finally block to ensure subsequent tests run in float32
        try:
           lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes[:2]:
              lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )
              lowerCamelCase									=model_class(_UpperCAmelCase  )
              lowerCamelCase									=model(_UpperCAmelCase  )

              self.assertIsNotNone(_UpperCAmelCase  )
        finally:
           tf.keras.mixed_precision.set_global_policy("""float32"""  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        # head_mask and decoder_head_mask has different shapes than other input args
        if "head_mask" in inputs_dict:
           del inputs_dict["head_mask"]
        if "decoder_head_mask" in inputs_dict:
           del inputs_dict["decoder_head_mask"]
        if "cross_attn_head_mask" in inputs_dict:
           del inputs_dict["cross_attn_head_mask"]
        lowerCamelCase									={
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__  ),)
            for module_member_name in dir(_UpperCAmelCase  )
            if module_member_name.endswith("""MainLayer"""  )
            for module_member in (getattr(_UpperCAmelCase							, _UpperCAmelCase  ),)
            if isinstance(_UpperCAmelCase							, _UpperCAmelCase  )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(_UpperCAmelCase							, """_keras_serializable"""							, _UpperCAmelCase  )
        }

        for main_layer_class in tf_main_layer_classes:
           # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
           if "T5" in main_layer_class.__name__:
              # Take the same values than in TFT5ModelTester for this shared layer
              lowerCamelCase									=TFSharedEmbeddings(self.model_tester.vocab_size							, self.model_tester.hidden_size							, name="""shared"""  )
              lowerCamelCase									=False
              lowerCamelCase									=main_layer_class(_UpperCAmelCase							, embed_tokens=_UpperCAmelCase  )
           else:
              lowerCamelCase									=main_layer_class(_UpperCAmelCase  )

           lowerCamelCase									={
               name: tf.keras.Input(tensor.shape[1:]							, dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
           }

           if hasattr(self.model_tester							, """num_labels"""  ):
              lowerCamelCase									=self.model_tester.num_labels
           else:
              lowerCamelCase									=2

           lowerCamelCase									=tf.data.Dataset.from_tensor_slices(
               (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)  ))  ).batch(1  )

           lowerCamelCase									=main_layer(_UpperCAmelCase  )[0]
           lowerCamelCase									=tf.keras.layers.Dense(_UpperCAmelCase							, activation="""softmax"""							, name="""outputs"""  )(_UpperCAmelCase  )
           lowerCamelCase									=tf.keras.models.Model(inputs=_UpperCAmelCase							, outputs=[outputs]  )

           model.compile(loss="""binary_crossentropy"""							, optimizer="""adam"""							, metrics=["""binary_accuracy"""]  )
           model.fit(_UpperCAmelCase							, epochs=1  )

           with tempfile.TemporaryDirectory() as tmpdirname:
              lowerCamelCase									=os.path.join(_UpperCAmelCase							, """keras_model.h5"""  )
              model.save(_UpperCAmelCase  )
              if "T5" in main_layer_class.__name__:
                 lowerCamelCase									=tf.keras.models.load_model(
                     _UpperCAmelCase							, custom_objects={
                         main_layer_class.__name__: main_layer_class,
                         """TFSharedEmbeddings""": TFSharedEmbeddings,
                     }							, )
              else:
                 lowerCamelCase									=tf.keras.models.load_model(
                     _UpperCAmelCase							, custom_objects={main_layer_class.__name__: main_layer_class}  )
              assert isinstance(_UpperCAmelCase							, tf.keras.Model  )
              model(_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
           lowerCamelCase									=model_class(_UpperCAmelCase  )

           lowerCamelCase									=copy.deepcopy(_UpperCAmelCase  )

           if not self.is_encoder_decoder:
              lowerCamelCase									=inputs["""input_ids"""]
              del inputs["input_ids"]
           else:
              lowerCamelCase									=inputs["""input_ids"""]
              lowerCamelCase									=inputs.get("""decoder_input_ids"""							, _UpperCAmelCase  )
              del inputs["input_ids"]
              inputs.pop("""decoder_input_ids"""							, _UpperCAmelCase  )

           if not self.is_encoder_decoder:
              lowerCamelCase									=model.get_input_embeddings()(_UpperCAmelCase  )
           else:
              lowerCamelCase									=model.get_input_embeddings()(_UpperCAmelCase  )
              lowerCamelCase									=model.get_input_embeddings()(_UpperCAmelCase  )

           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )

           @tf.function
           def run_in_graph_mode():
              return model(_UpperCAmelCase  )

           lowerCamelCase									=run_in_graph_mode()
           self.assertIsNotNone(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Any:
        # special tokens cannot be bad tokens
        lowerCamelCase									=[]
        if model.config.bos_token_id is not None:
           special_tokens.append(model.config.bos_token_id  )
        if model.config.pad_token_id is not None:
           special_tokens.append(model.config.pad_token_id  )
        if model.config.eos_token_id is not None:
           special_tokens.append(model.config.eos_token_id  )

        # create random bad tokens that are not special tokens
        lowerCamelCase									=[]
        while len(_UpperCAmelCase  ) < num_bad_tokens:
           lowerCamelCase									=tf.squeeze(ids_tensor((1, 1)							, self.model_tester.vocab_size  )							, 0  ).numpy()[0]
           if token not in special_tokens:
              bad_tokens.append(_UpperCAmelCase  )
        return bad_tokens





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Tuple:
        for token_id in output_ids[0].numpy().tolist():
           self.assertGreaterEqual(_UpperCAmelCase							, 0  )
           self.assertLess(_UpperCAmelCase							, self.model_tester.vocab_size  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				List[Any]:
        # for all bad word tokens
        for bad_word_ids in bad_words_ids:
           # for all slices in batch
           for generated_ids_slice in generated_ids:
              # for all word idx
              for i in range(len(_UpperCAmelCase  )							, len(_UpperCAmelCase  )  ):
                 # if tokens match
                 if generated_ids_slice[i - len(_UpperCAmelCase  ) : i] == bad_word_ids:
                    return True
        return False







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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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					:						Union[str, Any]         =     16
_UpperCAmelCase					:						Dict         =     32






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]			,lowerCamelCase	:			List[Any]    ):
   # Initialize accelerator
   if args.with_tracking:
      lowerCamelCase									=Accelerator(
          cpu=args.cpu			,mixed_precision=args.mixed_precision			,log_with="""all"""			,project_dir=args.project_dir    )
   else:
      lowerCamelCase									=Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision    )

   if hasattr(args.checkpointing_steps			,"""isdigit"""    ):
      if args.checkpointing_steps == "epoch":
         lowerCamelCase									=args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         lowerCamelCase									=int(args.checkpointing_steps    )
      else:
         raise ValueError(
             f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
   else:
      lowerCamelCase									=None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCamelCase									=config["""lr"""]
   lowerCamelCase									=int(config["""num_epochs"""]    )
   lowerCamelCase									=int(config["""seed"""]    )
   lowerCamelCase									=int(config["""batch_size"""]    )

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      lowerCamelCase									=os.path.split(lowerCamelCase    )[-1].split("""."""    )[0]
      accelerator.init_trackers(lowerCamelCase			,lowerCamelCase    )

   lowerCamelCase									=AutoTokenizer.from_pretrained("""bert-base-cased"""    )
   lowerCamelCase									=load_dataset("""glue"""			,"""mrpc"""    )
   lowerCamelCase									=evaluate.load("""glue"""			,"""mrpc"""    )

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

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

   def collate_fn(lowerCamelCase	:			Any    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowerCamelCase									=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":
         lowerCamelCase									=16
      elif accelerator.mixed_precision != "no":
         lowerCamelCase									=8
      else:
         lowerCamelCase									=None

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

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

   set_seed(lowerCamelCase    )

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

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

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

   # Instantiate scheduler
   lowerCamelCase									=get_linear_schedule_with_warmup(
       optimizer=lowerCamelCase			,num_warmup_steps=1_00			,num_training_steps=(len(lowerCamelCase    ) * 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.
   lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=accelerator.prepare(
       lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )

   # We need to keep track of how many total steps we have iterated over
   lowerCamelCase									=0
   # We also need to keep track of the stating epoch so files are named properly
   lowerCamelCase									=0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
         accelerator.load_state(args.resume_from_checkpoint    )
         lowerCamelCase									=os.path.basename(args.resume_from_checkpoint    )
      else:
         # Get the most recent checkpoint
         lowerCamelCase									=[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
         dirs.sort(key=os.path.getctime    )
         lowerCamelCase									=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      lowerCamelCase									=os.path.splitext(lowerCamelCase    )[0]

      if "epoch" in training_difference:
         lowerCamelCase									=int(training_difference.replace("""epoch_"""			,""""""    )    ) + 1
         lowerCamelCase									=None
      else:
         lowerCamelCase									=int(training_difference.replace("""step_"""			,""""""    )    )
         lowerCamelCase									=resume_step // len(lowerCamelCase    )
         resume_step -= starting_epoch * len(lowerCamelCase    )

    # Now we train the model
   for epoch in range(lowerCamelCase			,lowerCamelCase    ):
      model.train()
      if args.with_tracking:
         lowerCamelCase									=0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         lowerCamelCase									=accelerator.skip_first_batches(lowerCamelCase			,lowerCamelCase    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowerCamelCase									=train_dataloader
      for step, batch in enumerate(lowerCamelCase    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         lowerCamelCase									=model(**lowerCamelCase    )
         lowerCamelCase									=outputs.loss
         lowerCamelCase									=loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(lowerCamelCase    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(lowerCamelCase			,lowerCamelCase    ):
            lowerCamelCase									=f'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
               accelerator.save_state(lowerCamelCase    )

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

      lowerCamelCase									=metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,lowerCamelCase    )
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(lowerCamelCase    ),
                 """epoch""": epoch,
             }			,step=lowerCamelCase			,)

      if checkpointing_steps == "epoch":
         lowerCamelCase									=f'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
         accelerator.save_state(lowerCamelCase    )

   if args.with_tracking:
      accelerator.end_training()






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=argparse.ArgumentParser(description="""Simple example of training script."""    )
   parser.add_argument(
       """--mixed_precision"""			,type=lowerCamelCase			,default=lowerCamelCase			,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(
       """--checkpointing_steps"""			,type=lowerCamelCase			,default=lowerCamelCase			,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,)
   parser.add_argument(
       """--resume_from_checkpoint"""			,type=lowerCamelCase			,default=lowerCamelCase			,help="""If the training should continue from a checkpoint folder."""			,)
   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(
       """--output_dir"""			,type=lowerCamelCase			,default="""."""			,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,)
   parser.add_argument(
       """--project_dir"""			,type=lowerCamelCase			,default="""logs"""			,help="""Location on where to store experiment tracking logs` and relevent project information"""			,)
   lowerCamelCase									=parser.parse_args()
   lowerCamelCase									={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(lowerCamelCase			,lowerCamelCase    )


if __name__ == "__main__":
   main()