






"""simple docstring"""



import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types




class  lowerCAmelCase__ (						unittest.TestCase       ,	__magic_name__      ):
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       load_tool("translation"       )
        self.tool.setup()
        UpperCAmelCase__	:						List[str]       =       load_tool("translation"       , remote=snake_case__       )
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       self.tool("Hey, what's up?"       , src_lang="English"       , tgt_lang="French"       )
        self.assertEqual(snake_case__       , "- Hé, comment ça va?"       )
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       self.remote_tool("Hey, what's up?"       , src_lang="English"       , tgt_lang="French"       )
        self.assertEqual(snake_case__       , "- Hé, comment ça va?"       )
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       self.tool(text="Hey, what's up?"       , src_lang="English"       , tgt_lang="French"       )
        self.assertEqual(snake_case__       , "- Hé, comment ça va?"       )
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[Any]       =       self.remote_tool(text="Hey, what's up?"       , src_lang="English"       , tgt_lang="French"       )
        self.assertEqual(snake_case__       , "- Hé, comment ça va?"       )
   def 			__a       (		self			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       ["Hey, what's up?", "English", "Spanish"]
        UpperCAmelCase__	:						str       =       self.tool(*snake_case__       )

        # There is a single output
        if len(self.tool.outputs       ) == 1:
             UpperCAmelCase__	:						Dict       =       [outputs]

        self.assertListEqual(output_types(snake_case__       )       , self.tool.outputs       )
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       ["Hey, what's up?", "English", "Spanish"]
        UpperCAmelCase__	:						Tuple       =       self.tool(*snake_case__       )

        if not isinstance(snake_case__       , snake_case__       ):
             UpperCAmelCase__	:						Dict       =       [outputs]

        self.assertEqual(len(snake_case__       )       , len(self.tool.outputs       )       )

        for output, output_type in zip(snake_case__       , self.tool.outputs       ):
             UpperCAmelCase__	:						Optional[Any]       =       AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(snake_case__       , snake_case__       )       )




   def 			__a       (		self			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       ["Hey, what's up?", "English", "Spanish"]

        UpperCAmelCase__	:						Union[str, Any]       =       []

        for _input, input_type in zip(snake_case__       , self.tool.inputs       ):
             if isinstance(snake_case__       , snake_case__       ):
                  _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input       ) for _input_type in input_type]       )
             else:
                  _inputs.append(AGENT_TYPE_MAPPING[input_type](_input       )       )

        # Should not raise an error
        UpperCAmelCase__	:						Optional[Any]       =       self.tool(*snake_case__       )

        if not isinstance(snake_case__       , snake_case__       ):
             UpperCAmelCase__	:						Optional[int]       =       [outputs]

        self.assertEqual(len(snake_case__       )       , len(self.tool.outputs       )       )










"""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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

_lowerCAmelCase  :  int   =			32


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Accelerator ,				snake_case					:   int = 16							)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__	:						Dict       =       AutoTokenizer.from_pretrained("bert-base-cased"							)
     UpperCAmelCase__	:						Union[str, Any]       =       load_dataset("glue" ,				"mrpc"							)

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

     def collate_fn(snake_case					:   int							):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               UpperCAmelCase__	:						Optional[Any]       =       16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase__	:						int       =       8
          else:
               UpperCAmelCase__	:						Union[str, Any]       =       None

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

     # Instantiate dataloaders.
     UpperCAmelCase__	:						int       =       DataLoader(
         tokenized_datasets["train"] ,				shuffle=snake_case ,				collate_fn=snake_case ,				batch_size=snake_case							)
     UpperCAmelCase__	:						Optional[int]       =       DataLoader(
         tokenized_datasets["validation"] ,				shuffle=snake_case ,				collate_fn=snake_case ,				batch_size=snake_case							)

     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  :  Optional[int]   =			mocked_dataloaders  # noqa: F811


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[str] ,				snake_case					:   Tuple							)->		Optional[int]:






     '''simple docstring'''



     if os.environ.get("TESTING_MOCKED_DATALOADERS" ,				snake_case							) == "1":
          UpperCAmelCase__	:						Any       =       2
     # Initialize accelerator
     UpperCAmelCase__	:						Tuple       =       Accelerator(cpu=args.cpu ,				mixed_precision=args.mixed_precision							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase__	:						Any       =       config["lr"]
     UpperCAmelCase__	:						str       =       int(config["num_epochs"]							)
     UpperCAmelCase__	:						str       =       int(config["seed"]							)
     UpperCAmelCase__	:						Dict       =       int(config["batch_size"]							)

     UpperCAmelCase__	:						Dict       =       evaluate.load("glue" ,				"mrpc"							)

     # New Code #
     # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
     # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
     # half each time. From this, we can calculate the number of gradient accumulation steps needed
     # and modify the Accelerator object as a result
     @find_executable_batch_size(starting_batch_size=int(snake_case							)							)
     def inner_training_loop(snake_case					:   Optional[int]							):
          # Since we need to modify the outside accelerator object, we need to bring it
          # to the local scope
          nonlocal accelerator

          # We can calculate the number of gradient accumulation steps based on the current
          # batch size vs the starting batch size
          UpperCAmelCase__	:						int       =       observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          UpperCAmelCase__	:						List[Any]       =       num_gradient_accumulation_steps

          # Next we need to free all of the stored model references in the Accelerator each time
          accelerator.free_memory()

          # And set the seed so our results are reproducable each reset
          set_seed(snake_case							)

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          UpperCAmelCase__	:						Optional[int]       =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,				return_dict=snake_case							)

          # 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__	:						Tuple       =       model.to(accelerator.device							)

          # Instantiate optimizer
          UpperCAmelCase__	:						str       =       AdamW(params=model.parameters() ,				lr=snake_case							)
          UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       get_dataloaders(snake_case ,				snake_case							)

          # Instantiate scheduler
          UpperCAmelCase__	:						Dict       =       get_linear_schedule_with_warmup(
              optimizer=snake_case ,				num_warmup_steps=100 ,				num_training_steps=(len(snake_case							) * 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__	:						Union[str, Any]       =       accelerator.prepare(
              snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case							)

          # Now we train the model
          for epoch in range(snake_case							):
               model.train()
               for step, batch in enumerate(snake_case							):
                    # And perform gradient accumulation
                    with accelerator.accumulate(snake_case							):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device							)
                         UpperCAmelCase__	:						Union[str, Any]       =       model(**snake_case							)
                         UpperCAmelCase__	:						int       =       outputs.loss
                         accelerator.backward(snake_case							)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

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

               UpperCAmelCase__	:						List[str]       =       metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f'epoch {epoch}:' ,				snake_case							)

    # 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   SCREAMING_SNAKE_CASE__					(     )->		str:






     '''simple docstring'''



     UpperCAmelCase__	:						str       =       argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision" ,				type=snake_case ,				default=snake_case ,				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__	:						str       =       parser.parse_args()
     # New Code #
     # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
     UpperCAmelCase__	:						Union[str, Any]       =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
     training_function(snake_case ,				snake_case							)


if __name__ == "__main__":
 main()


