





'''simple docstring'''


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase					:     List[Any]												='''true'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Optional[int]=82	,__lowerCamelCase					:					str=16 ):
   set_seed(42 )
   lowercase_			:List[str]    			= RegressionModel()
   lowercase_			:List[Any]    			= deepcopy(__lowerCamelCase )
   lowercase_			:Optional[Any]    			= RegressionDataset(length=__lowerCamelCase )
   lowercase_			:int    			= DataLoader(__lowerCamelCase	,batch_size=__lowerCamelCase )
   model.to(accelerator.device )
   lowercase_			,							lowercase_			:List[str]    			= accelerator.prepare(__lowerCamelCase	,__lowerCamelCase )
   return model, ddp_model, dataloader
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Accelerator	,__lowerCamelCase					:					Any=False ):
   lowercase_			:Union[str, Any]    			= AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
   lowercase_			:Any    			= load_dataset("glue"	,"mrpc"	,split="validation" )

   def tokenize_function(__lowerCamelCase					:					Optional[int] ):
      lowercase_			:Tuple    			= tokenizer(examples["sentence1"]	,examples["sentence2"]	,truncation=__lowerCamelCase	,max_length=__lowerCamelCase )
      return outputs

   with accelerator.main_process_first():
      lowercase_			:List[str]    			= dataset.map(
          __lowerCamelCase	,batched=__lowerCamelCase	,remove_columns=["idx", "sentence1", "sentence2"]	,)

   lowercase_			:List[str]    			= tokenized_datasets.rename_column("label"	,"labels" )

   def collate_fn(__lowerCamelCase					:					List[Any] ):
      if use_longest:
         return tokenizer.pad(__lowerCamelCase	,padding="longest"	,return_tensors="pt" )
      return tokenizer.pad(__lowerCamelCase	,padding="max_length"	,max_length=1_28	,return_tensors="pt" )

   return DataLoader(__lowerCamelCase	,shuffle=__lowerCamelCase	,collate_fn=__lowerCamelCase	,batch_size=16 )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Optional[Any] ):
   lowercase_			:Any    			= Accelerator(dispatch_batches=__lowerCamelCase	,split_batches=__lowerCamelCase )
   lowercase_			:int    			= get_dataloader(__lowerCamelCase	,not dispatch_batches )
   lowercase_			:Optional[Any]    			= AutoModelForSequenceClassification.from_pretrained(
       "hf-internal-testing/mrpc-bert-base-cased"	,return_dict=__lowerCamelCase )
   lowercase_			,							lowercase_			:str    			= accelerator.prepare(__lowerCamelCase	,__lowerCamelCase )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					int ):
   lowercase_			:Dict    			= []
   for batch in dataloader:
      lowercase_			,							lowercase_			:str    			= batch.values()
      with torch.no_grad():
         lowercase_			:List[str]    			= model(__lowerCamelCase )
         lowercase_			,							lowercase_			:Tuple    			= accelerator.gather_for_metrics((logit, target) )
         logits_and_targets.append((logit, target) )
   lowercase_			,							lowercase_			:Optional[int]    			= [], []
   for logit, targ in logits_and_targets:
      logits.append(__lowerCamelCase )
      targs.append(__lowerCamelCase )
   lowercase_			,							lowercase_			:List[Any]    			= torch.cat(__lowerCamelCase ), torch.cat(__lowerCamelCase )
   return logits, targs
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Accelerator	,__lowerCamelCase					:					Any=82	,__lowerCamelCase					:					Tuple=False	,__lowerCamelCase					:					Optional[Any]=False	,__lowerCamelCase					:					str=16 ):
   lowercase_			,							lowercase_			,							lowercase_			:Optional[int]    			= get_basic_setup(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase )
   lowercase_			,							lowercase_			:Optional[int]    			= generate_predictions(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase )
   assert (
       len(__lowerCamelCase ) == num_samples
   ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCamelCase )}'
def 							UpperCAmelCase_  (						__lowerCamelCase					:					bool = False	,__lowerCamelCase					:					bool = False ):
   lowercase_			:List[str]    			= evaluate.load("glue"	,"mrpc" )
   lowercase_			,							lowercase_			:str    			= get_mrpc_setup(__lowerCamelCase	,__lowerCamelCase )
   # First do baseline
   lowercase_			,							lowercase_			,							lowercase_			:Optional[Any]    			= setup["no"]
   model.to(__lowerCamelCase )
   model.eval()
   for batch in dataloader:
      batch.to(__lowerCamelCase )
      with torch.inference_mode():
         lowercase_			:Optional[Any]    			= model(**__lowerCamelCase )
      lowercase_			:Optional[int]    			= outputs.logits.argmax(dim=-1 )
      metric.add_batch(predictions=__lowerCamelCase	,references=batch["labels"] )
   lowercase_			:Dict    			= metric.compute()

   # Then do distributed
   lowercase_			,							lowercase_			,							lowercase_			:Optional[int]    			= setup["ddp"]
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         lowercase_			:Dict    			= model(**__lowerCamelCase )
      lowercase_			:Dict    			= outputs.logits.argmax(dim=-1 )
      lowercase_			:str    			= batch["labels"]
      lowercase_			,							lowercase_			:Optional[int]    			= accelerator.gather_for_metrics((preds, references) )
      metric.add_batch(predictions=__lowerCamelCase	,references=__lowerCamelCase )
   lowercase_			:List[Any]    			= metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]	,distributed[key] ), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def 							UpperCAmelCase_  (						):
   lowercase_			:Optional[Any]    			= Accelerator(split_batches=__lowerCamelCase	,dispatch_batches=__lowerCamelCase )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print("**Testing gather_for_metrics**" )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
            test_mrpc(__lowerCamelCase	,__lowerCamelCase )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("**Test torch metrics**" )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         lowercase_			:Union[str, Any]    			= Accelerator(split_batches=__lowerCamelCase	,dispatch_batches=__lowerCamelCase )
         if accelerator.is_local_main_process:
            print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
         test_torch_metrics(__lowerCamelCase	,99 )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("**Test last batch is not dropped when perfectly divisible**" )
   lowercase_			:Optional[int]    			= Accelerator()
   test_torch_metrics(__lowerCamelCase	,5_12 )
   accelerator.state._reset_state()
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any] ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()









'''simple docstring'''


import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[Any] ):
   return x + 2


class       a_   (				unittest.TestCase							):





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= "x = 3"
        lowercase_			:Any    			= {}
        lowercase_			:Any    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        assert result == 3
        self.assertDictEqual(lowercase	,      {"x": 3}						)

        lowercase_			:Dict    			= "x = y"
        lowercase_			:Dict    			= {"y": 5}
        lowercase_			:str    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        # evaluate returns the value of the last assignment.
        assert result == 5
        self.assertDictEqual(lowercase	,      {"x": 5, "y": 5}						)





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Dict    			= "y = add_two(x)"
        lowercase_			:Optional[Any]    			= {"x": 3}
        lowercase_			:int    			= evaluate(lowercase	,      {"add_two": add_two}	,      state=lowercase						)
        assert result == 5
        self.assertDictEqual(lowercase	,      {"x": 3, "y": 5}						)

        # Won't work without the tool
        with CaptureStdout() as out:
           lowercase_			:List[Any]    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        assert result is None
        assert "tried to execute add_two" in out.out





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= "x = 3"
        lowercase_			:Any    			= {}
        lowercase_			:Any    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        assert result == 3
        self.assertDictEqual(lowercase	,      {"x": 3}						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= "test_dict = {'x': x, 'y': add_two(x)}"
        lowercase_			:List[str]    			= {"x": 3}
        lowercase_			:Any    			= evaluate(lowercase	,      {"add_two": add_two}	,      state=lowercase						)
        self.assertDictEqual(lowercase	,      {"x": 3, "y": 5}						)
        self.assertDictEqual(lowercase	,      {"x": 3, "test_dict": {"x": 3, "y": 5}}						)





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= "x = 3\ny = 5"
        lowercase_			:Union[str, Any]    			= {}
        lowercase_			:Optional[int]    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        # evaluate returns the value of the last assignment.
        assert result == 5
        self.assertDictEqual(lowercase	,      {"x": 3, "y": 5}						)





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[str]    			= "text = f'This is x: {x}.'"
        lowercase_			:int    			= {"x": 3}
        lowercase_			:int    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        # evaluate returns the value of the last assignment.
        assert result == "This is x: 3."
        self.assertDictEqual(lowercase	,      {"x": 3, "text": "This is x: 3."}						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "if x <= 3:\n    y = 2\nelse:\n    y = 5"
        lowercase_			:List[str]    			= {"x": 3}
        lowercase_			:Union[str, Any]    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        # evaluate returns the value of the last assignment.
        assert result == 2
        self.assertDictEqual(lowercase	,      {"x": 3, "y": 2}						)

        lowercase_			:List[str]    			= {"x": 8}
        lowercase_			:Tuple    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        # evaluate returns the value of the last assignment.
        assert result == 5
        self.assertDictEqual(lowercase	,      {"x": 8, "y": 5}						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Any    			= "test_list = [x, add_two(x)]"
        lowercase_			:Optional[int]    			= {"x": 3}
        lowercase_			:List[str]    			= evaluate(lowercase	,      {"add_two": add_two}	,      state=lowercase						)
        self.assertListEqual(lowercase	,      [3, 5]						)
        self.assertDictEqual(lowercase	,      {"x": 3, "test_list": [3, 5]}						)





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:str    			= "y = x"
        lowercase_			:Any    			= {"x": 3}
        lowercase_			:Tuple    			= evaluate(lowercase	,      {}	,      state=lowercase						)
        assert result == 3
        self.assertDictEqual(lowercase	,      {"x": 3, "y": 3}						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "test_list = [x, add_two(x)]\ntest_list[1]"
        lowercase_			:Dict    			= {"x": 3}
        lowercase_			:Any    			= evaluate(lowercase	,      {"add_two": add_two}	,      state=lowercase						)
        assert result == 5
        self.assertDictEqual(lowercase	,      {"x": 3, "test_list": [3, 5]}						)

        lowercase_			:Union[str, Any]    			= "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
        lowercase_			:List[Any]    			= {"x": 3}
        lowercase_			:List[Any]    			= evaluate(lowercase	,      {"add_two": add_two}	,      state=lowercase						)
        assert result == 5
        self.assertDictEqual(lowercase	,      {"x": 3, "test_dict": {"x": 3, "y": 5}}						)







     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Dict    			= "x = 0\nfor i in range(3):\n    x = i"
        lowercase_			:Any    			= {}
        lowercase_			:Tuple    			= evaluate(lowercase	,      {"range": range}	,      state=lowercase						)
        assert result == 2
        self.assertDictEqual(lowercase	,      {"x": 2, "i": 2}						)


