from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= LEDConfig
 UpperCAmelCase_     			= {}
 UpperCAmelCase_     			= 'gelu'




 def __init__(      self :  List[str]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Optional[Any]=13      ,   lowercase__ :  List[Any]=7      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  List[Any]=False      ,   lowercase__ :  Union[str, Any]=99      ,   lowercase__ :  Tuple=32      ,   lowercase__ :  Optional[Any]=2      ,   lowercase__ :  Optional[int]=4      ,   lowercase__ :  Tuple=37      ,   lowercase__ :  Optional[int]=0.1      ,   lowercase__ :  List[str]=0.1      ,   lowercase__ :  int=20      ,   lowercase__ :  Any=2      ,   lowercase__ :  Optional[int]=1      ,   lowercase__ :  int=0      ,   lowercase__ :  int=4      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = seq_length
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = intermediate_size

        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = eos_token_id
        lowerCAmelCase__					     = pad_token_id
        lowerCAmelCase__					     = bos_token_id
        lowerCAmelCase__					     = attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        lowerCAmelCase__					     = self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        lowerCAmelCase__					     = (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size)
        lowerCAmelCase__					     = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)      ,   1)
        lowerCAmelCase__					     = tf.concat([input_ids, eos_tensor]      ,   axis=1)

        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

        lowerCAmelCase__					     = self.config_cls(
            vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   encoder_layers=self.num_hidden_layers      ,   decoder_layers=self.num_hidden_layers      ,   encoder_attention_heads=self.num_attention_heads      ,   decoder_attention_heads=self.num_attention_heads      ,   encoder_ffn_dim=self.intermediate_size      ,   decoder_ffn_dim=self.intermediate_size      ,   dropout=self.hidden_dropout_prob      ,   attention_dropout=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   eos_token_ids=[2]      ,   bos_token_id=self.bos_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.pad_token_id      ,   attention_window=self.attention_window      ,   **self.config_updates      ,   )
        lowerCAmelCase__					     = prepare_led_inputs_dict(lowercase__      ,   lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = tf.concat(
            [tf.zeros_like(lowercase__)[:, :-1], tf.ones_like(lowercase__)[:, -1:]]      ,   axis=-1      ,   )
        lowerCAmelCase__					     = global_attention_mask
        return config, inputs_dict




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDModel(config=lowercase__).get_decoder()
        lowerCAmelCase__					     = inputs_dict['input_ids']

        lowerCAmelCase__					     = input_ids[:1, :]
        lowerCAmelCase__					     = inputs_dict['attention_mask'][:1, :]
        lowerCAmelCase__					     = 1

        # first forward pass
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   use_cache=lowercase__)

        lowerCAmelCase__     , lowerCAmelCase__					     = outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        lowerCAmelCase__					     = ids_tensor((self.batch_size, 3)      ,   config.vocab_size)
        lowerCAmelCase__					     = tf.cast(ids_tensor((self.batch_size, 3)      ,   2)      ,   tf.inta)

        # append to next input_ids and
        lowerCAmelCase__					     = tf.concat([input_ids, next_tokens]      ,   axis=-1)
        lowerCAmelCase__					     = tf.concat([attention_mask, next_attn_mask]      ,   axis=-1)

        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__)[0]
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   past_key_values=lowercase__)[0]

        self.parent.assertEqual(next_tokens.shape[1]      ,   output_from_past.shape[1])

        # select random slice
        lowerCAmelCase__					     = int(ids_tensor((1,)      ,   output_from_past.shape[-1]))
        lowerCAmelCase__					     = output_from_no_past[:, -3:, random_slice_idx]
        lowerCAmelCase__					     = output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowercase__      ,   lowercase__      ,   rtol=1e-3)






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  ):
       if attention_mask is None:
              lowerCAmelCase__					     = tf.cast(tf.math.not_equal(lowerCAmelCase__     ,  config.pad_token_id     )     ,  tf.inta     )
       if decoder_attention_mask is None:
              lowerCAmelCase__					     = tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape     ,  dtype=tf.inta     ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,  config.pad_token_id     )     ,  tf.inta     ),
                  ]     ,  axis=-1     ,  )
       if head_mask is None:
              lowerCAmelCase__					     = tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              lowerCAmelCase__					     = tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "decoder_input_ids": decoder_input_ids,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
       }
@require_tf
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 UpperCAmelCase_     			= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
 UpperCAmelCase_     			= (
     {
         'conversational': TFLEDForConditionalGeneration,
         'feature-extraction': TFLEDModel,
         'summarization': TFLEDForConditionalGeneration,
         'text2text-generation': TFLEDForConditionalGeneration,
         'translation': TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase__)




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__					     = tf.zeros_like(inputs_dict['attention_mask'])
        lowerCAmelCase__					     = 2
        lowerCAmelCase__					     = tf.where(
            tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices      ,   1      ,   inputs_dict['global_attention_mask']      ,   )

        lowerCAmelCase__					     = True
        lowerCAmelCase__					     = self.model_tester.seq_length
        lowerCAmelCase__					     = self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowercase__ :  Union[str, Any]):
               lowerCAmelCase__					     = outputs.decoder_attentions
               self.assertEqual(len(lowercase__)      ,   self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(decoder_attentions[0].shape[-3:])      ,   [self.model_tester.num_attention_heads, seq_length, seq_length]      ,   )

        def check_encoder_attentions_output(lowercase__ :  Optional[int]):
               lowerCAmelCase__					     = [t.numpy() for t in outputs.encoder_attentions]
               lowerCAmelCase__					     = [t.numpy() for t in outputs.encoder_global_attentions]
               self.assertEqual(len(lowercase__)      ,   self.model_tester.num_hidden_layers)
               self.assertEqual(len(lowercase__)      ,   self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(attentions[0].shape[-3:])      ,   [self.model_tester.num_attention_heads, seq_length, seq_length]      ,   )
               self.assertListEqual(
                   list(global_attentions[0].shape[-3:])      ,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]      ,   )

        for model_class in self.all_model_classes:
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = False
               lowerCAmelCase__					     = False
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))
               lowerCAmelCase__					     = len(lowercase__)
               self.assertEqual(config.output_hidden_states      ,   lowercase__)
               check_encoder_attentions_output(lowercase__)

               if self.is_encoder_decoder:
                      lowerCAmelCase__					     = model_class(lowercase__)
                      lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))
                      self.assertEqual(config.output_hidden_states      ,   lowercase__)
                      check_decoder_attentions_output(lowercase__)

               # Check that output attentions can also be changed via the config
               del inputs_dict["output_attentions"]
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))
               self.assertEqual(config.output_hidden_states      ,   lowercase__)
               check_encoder_attentions_output(lowercase__)

               # Check attention is always last and order is fine
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = model_class(lowercase__)
               lowerCAmelCase__					     = model(self._prepare_for_class(lowercase__      ,   lowercase__))

               self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)      ,   len(lowercase__))
               self.assertEqual(model.config.output_hidden_states      ,   lowercase__)
               check_encoder_attentions_output(lowercase__)




 @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.')
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        pass






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return tf.constant(lowerCAmelCase__     ,  dtype=tf.intaa     )


lowerCAmelCase__     						=			1e-4



@slow
@require_tf
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384').led

        # change to intended input here
        lowerCAmelCase__					     = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = prepare_led_inputs_dict(model.config      ,   lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = model(**lowercase__)[0]
        lowerCAmelCase__					     = (1, 1_024, 768)
        self.assertEqual(output.shape      ,   lowercase__)
        # change to expected output here
        lowerCAmelCase__					     = tf.convert_to_tensor(
            [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]      ,   )
        tf.debugging.assert_near(output[:, :3, :3]      ,   lowercase__      ,   atol=1e-3)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')

        # change to intended input here
        lowerCAmelCase__					     = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]])
        lowerCAmelCase__					     = prepare_led_inputs_dict(model.config      ,   lowercase__      ,   lowercase__)
        lowerCAmelCase__					     = model(**lowercase__)[0]
        lowerCAmelCase__					     = (1, 1_024, model.config.vocab_size)
        self.assertEqual(output.shape      ,   lowercase__)
        # change to expected output here
        lowerCAmelCase__					     = tf.convert_to_tensor(
            [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]      ,   )
        tf.debugging.assert_near(output[:, :3, :3]      ,   lowercase__      ,   atol=1e-3      ,   rtol=1e-3)

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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


lowerCAmelCase__     						=			16
lowerCAmelCase__     						=			32






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = 1_6     ):
       lowerCAmelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'     )
       lowerCAmelCase__					     = load_dataset('glue'     ,  'mrpc'     )

       def tokenize_function(lowerCAmelCase__     ):
              # 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'     )

       def collate_fn(lowerCAmelCase__     ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowerCAmelCase__					     = 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":
                     lowerCAmelCase__					     = 1_6
              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__     )

       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

    lowerCAmelCase__     						=			mocked_dataloaders  # noqa: F811






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  lowerCAmelCase__     ) == "1":
              lowerCAmelCase__					     = 2
       # Initialize accelerator
       lowerCAmelCase__					     = Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision     )
       # 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']     )

       lowerCAmelCase__					     = evaluate.load('glue'     ,  'mrpc'     )

       # 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

       set_seed(lowerCAmelCase__     )
       lowerCAmelCase__     , lowerCAmelCase__					     = get_dataloaders(lowerCAmelCase__     ,  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_0_0     ,  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__     )

       # Now we train the model
       for epoch in range(lowerCAmelCase__     ):
              model.train()
              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
                     accelerator.backward(lowerCAmelCase__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              lowerCAmelCase__					     = 0
              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((predictions, batch['labels'])     )
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(lowerCAmelCase__     ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   lowerCAmelCase__					     = predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                                   lowerCAmelCase__					     = references[: len(eval_dataloader.dataset     ) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # 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__     )






def 				__lowerCamelCase		(						):
       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.'     )
       lowerCAmelCase__					     = parser.parse_args()
       lowerCAmelCase__					     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(lowerCAmelCase__     ,  lowerCAmelCase__     )


if __name__ == "__main__":
    main()
