


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
#
########################################################################

UpperCAmelCase_				:			Union[str, Any]   =	16
UpperCAmelCase_				:			str   =	32







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Accelerator  ,   __A			:      int = 16    )     ->		Union[str, Any]:

       """simple docstring"""

       a_ :		str							=  AutoTokenizer.from_pretrained('bert-base-cased'    )
       a_ :		List[str]							=  load_dataset('glue'  ,   'mrpc'    )

       def tokenize_function(__A			:      Any    ):
              # max_length=None => use the model max length (it's actually the default)
              a_ :		List[Any]							=  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_ :		Union[str, Any]							=  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_ :		int							=  tokenized_datasets.rename_column('label'  ,   'labels'    )

       def collate_fn(__A			:      Optional[int]    ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              a_ :		Optional[int]							=  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":
                     a_ :		Tuple							=  16
              elif accelerator.mixed_precision != "no":
                     a_ :		int							=  8
              else:
                     a_ :		List[str]							=  None

              return tokenizer.pad(
                  __A  ,   padding='longest'  ,   max_length=__A  ,   pad_to_multiple_of=__A  ,   return_tensors='pt'  ,   )

       # Instantiate dataloaders.
       a_ :		str							=  DataLoader(
           tokenized_datasets['train']  ,   shuffle=__A  ,   collate_fn=__A  ,   batch_size=__A    )
       a_ :		List[str]							=  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

   UpperCAmelCase_				:			str   =	mocked_dataloaders  # noqa: F811







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		int:

       """simple docstring"""

       if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,   __A    ) == "1":
              a_ :		Dict							=  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_ :		Optional[int]							=  Accelerator(
                  cpu=args.cpu  ,   mixed_precision=args.mixed_precision  ,   log_with='all'  ,   project_dir=args.project_dir    )
       else:
              a_ :		Tuple							=  Accelerator(cpu=args.cpu  ,   mixed_precision=args.mixed_precision    )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       a_ :		List[str]							=  config['lr']
       a_ :		Dict							=  int(config['num_epochs']    )
       a_ :		Union[str, Any]							=  int(config['seed']    )
       a_ :		Dict							=  int(config['batch_size']    )
       set_seed(__A    )

       a_							,		a_ :		List[Any]							=  get_dataloaders(__A  ,   __A    )
       a_ :		Optional[Any]							=  evaluate.load('glue'  ,   'mrpc'    )

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a_ :		List[str]							=  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_ :		Any							=  model.to(accelerator.device    )

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

       # Instantiate scheduler
       a_ :		Tuple							=  get_linear_schedule_with_warmup(
           optimizer=__A  ,   num_warmup_steps=1_00  ,   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_ :		Optional[int]							=  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_ :		Dict							=  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_ :		Union[str, Any]							=  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_ :		Union[str, Any]							=  model(**__A    )
                     a_ :		Optional[int]							=  outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     a_ :		int							=  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_ :		Any							=  model(**__A    )
                     a_ :		List[str]							=  outputs.logits.argmax(dim=-1    )
                     a_							,		a_ :		Any							=  accelerator.gather_for_metrics((predictions, batch['labels'])    )
                     metric.add_batch(
                         predictions=__A  ,   references=__A  ,   )

              a_ :		Any							=  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 	SCREAMING_SNAKE_CASE_						(      )     ->		List[Any]:

       """simple docstring"""

       a_ :		Optional[int]							=  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_ :		Optional[Any]							=  parser.parse_args()
       a_ :		str							=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(__A  ,   __A    )


if __name__ == "__main__":
   main()






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase_				:			str   =	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		List[Any]							=  os.path.join(__A  ,   F"""{split}_results.json"""    )
       if os.path.exists(__A    ):
              with open(__A  ,   'r'    ) as f:
                     return json.load(__A    )
       raise ValueError(F"""can't find {path}"""    )


UpperCAmelCase_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, Any]							=  F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					,							split='test'					)
                    self.assertGreaterEqual(result['test_rouge1']					,							1_0					)
                    self.assertGreaterEqual(result['test_rouge2']					,							2					)
                    self.assertGreaterEqual(result['test_rougeL']					,							7					)
                    self.assertGreaterEqual(result['test_rougeLsum']					,							7					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		int							=  F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


