

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


lowercase_			=  "true"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict=82   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]=16				)							->				List[str]:





    '''simple docstring'''






    set_seed(42				)
    A__   				=       RegressionModel()
    A__   				=       deepcopy(SCREAMING_SNAKE_CASE__				)
    A__   				=       RegressionDataset(length=SCREAMING_SNAKE_CASE__				)
    A__   				=       DataLoader(SCREAMING_SNAKE_CASE__   ,      batch_size=SCREAMING_SNAKE_CASE__				)
    model.to(accelerator.device				)
    A__				,						A__   				=       accelerator.prepare(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    return model, ddp_model, dataloader
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Accelerator   ,      SCREAMING_SNAKE_CASE__      :     str=False				)							->				int:





    '''simple docstring'''






    A__   				=       AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'				)
    A__   				=       load_dataset('glue'   ,      'mrpc'   ,      split='validation'				)

    def tokenize_function(SCREAMING_SNAKE_CASE__      :     Union[str, Any]				):
        A__   				=       tokenizer(examples['sentence1']   ,      examples['sentence2']   ,      truncation=SCREAMING_SNAKE_CASE__   ,      max_length=SCREAMING_SNAKE_CASE__				)
        return outputs

    with accelerator.main_process_first():
        A__   				=       dataset.map(
            SCREAMING_SNAKE_CASE__   ,      batched=SCREAMING_SNAKE_CASE__   ,      remove_columns=['idx', 'sentence1', 'sentence2']   ,      )

    A__   				=       tokenized_datasets.rename_column('label'   ,      'labels'				)

    def collate_fn(SCREAMING_SNAKE_CASE__      :     List[str]				):
        if use_longest:
            return tokenizer.pad(SCREAMING_SNAKE_CASE__   ,      padding='longest'   ,      return_tensors='pt'				)
        return tokenizer.pad(SCREAMING_SNAKE_CASE__   ,      padding='max_length'   ,      max_length=128   ,      return_tensors='pt'				)

    return DataLoader(SCREAMING_SNAKE_CASE__   ,      shuffle=SCREAMING_SNAKE_CASE__   ,      collate_fn=SCREAMING_SNAKE_CASE__   ,      batch_size=16				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Tuple:





    '''simple docstring'''






    A__   				=       Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__   ,      split_batches=SCREAMING_SNAKE_CASE__				)
    A__   				=       get_dataloader(SCREAMING_SNAKE_CASE__   ,      not dispatch_batches				)
    A__   				=       AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'   ,      return_dict=SCREAMING_SNAKE_CASE__				)
    A__				,						A__   				=       accelerator.prepare(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				List[Any]:





    '''simple docstring'''






    A__   				=       []
    for batch in dataloader:
        A__				,						A__   				=       batch.values()
        with torch.no_grad():
            A__   				=       model(SCREAMING_SNAKE_CASE__				)
            A__				,						A__   				=       accelerator.gather_for_metrics((logit, target)				)
            logits_and_targets.append((logit, target)				)
    A__				,						A__   				=       [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__				)
        targs.append(SCREAMING_SNAKE_CASE__				)
    A__				,						A__   				=       torch.cat(SCREAMING_SNAKE_CASE__				), torch.cat(SCREAMING_SNAKE_CASE__				)
    return logits, targs
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Accelerator   ,      SCREAMING_SNAKE_CASE__      :     List[Any]=82   ,      SCREAMING_SNAKE_CASE__      :     Any=False   ,      SCREAMING_SNAKE_CASE__      :     str=False   ,      SCREAMING_SNAKE_CASE__      :     Dict=16				)							->				Any:





    '''simple docstring'''






    A__				,						A__				,						A__   				=       get_basic_setup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    A__				,						A__   				=       generate_predictions(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    assert (
        len(SCREAMING_SNAKE_CASE__				) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__				)}'
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     bool = False   ,      SCREAMING_SNAKE_CASE__      :     bool = False				)							->				Dict:





    '''simple docstring'''






    A__   				=       evaluate.load('glue'   ,      'mrpc'				)
    A__				,						A__   				=       get_mrpc_setup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # First do baseline
    A__				,						A__				,						A__   				=       setup['no']
    model.to(SCREAMING_SNAKE_CASE__				)
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE__				)
        with torch.inference_mode():
            A__   				=       model(**SCREAMING_SNAKE_CASE__				)
        A__   				=       outputs.logits.argmax(dim=-1				)
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__   ,      references=batch['labels']				)
    A__   				=       metric.compute()

    # Then do distributed
    A__				,						A__				,						A__   				=       setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__   				=       model(**SCREAMING_SNAKE_CASE__				)
        A__   				=       outputs.logits.argmax(dim=-1				)
        A__   				=       batch['labels']
        A__				,						A__   				=       accelerator.gather_for_metrics((preds, references)				)
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__   ,      references=SCREAMING_SNAKE_CASE__				)
    A__   				=       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 						_snake_case(   )							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       Accelerator(split_batches=SCREAMING_SNAKE_CASE__   ,      dispatch_batches=SCREAMING_SNAKE_CASE__				)
    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(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                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]:
            A__   				=       Accelerator(split_batches=SCREAMING_SNAKE_CASE__   ,      dispatch_batches=SCREAMING_SNAKE_CASE__				)
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'				)
            test_torch_metrics(SCREAMING_SNAKE_CASE__   ,      99				)
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test last batch is not dropped when perfectly divisible**'				)
    A__   				=       Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__   ,      512				)
    accelerator.state._reset_state()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				List[str]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

