


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


SCREAMING_SNAKE_CASE							    =							'true'




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=82		,				lowerCAmelCase__=16   ):
   set_seed(42   )
   __a					=  RegressionModel()
   __a					=  deepcopy(lowerCAmelCase__   )
   __a					=  RegressionDataset(length=lowerCAmelCase__   )
   __a					=  DataLoader(lowerCAmelCase__		,				batch_size=lowerCAmelCase__   )
   model.to(accelerator.device   )
   __a	,    __a					=  accelerator.prepare(lowerCAmelCase__		,				lowerCAmelCase__   )
   return model, ddp_model, dataloader




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=False   ):
   __a					=  AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""   )
   __a					=  load_dataset("""glue"""		,				"""mrpc"""		,				split="""validation"""   )

   def tokenize_function(lowerCAmelCase__   ):
      __a					=  tokenizer(examples["""sentence1"""]		,				examples["""sentence2"""]		,				truncation=lowerCAmelCase__		,				max_length=lowerCAmelCase__   )
      return outputs

   with accelerator.main_process_first():
      __a					=  dataset.map(
          lowerCAmelCase__		,				batched=lowerCAmelCase__		,				remove_columns=["""idx""", """sentence1""", """sentence2"""]		,				)

   __a					=  tokenized_datasets.rename_column("""label"""		,				"""labels"""   )

   def collate_fn(lowerCAmelCase__   ):
      if use_longest:
         return tokenizer.pad(lowerCAmelCase__		,				padding="""longest"""		,				return_tensors="""pt"""   )
      return tokenizer.pad(lowerCAmelCase__		,				padding="""max_length"""		,				max_length=128		,				return_tensors="""pt"""   )

   return DataLoader(lowerCAmelCase__		,				shuffle=lowerCAmelCase__		,				collate_fn=lowerCAmelCase__		,				batch_size=16   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  Accelerator(dispatch_batches=lowerCAmelCase__		,				split_batches=lowerCAmelCase__   )
   __a					=  get_dataloader(lowerCAmelCase__		,				not dispatch_batches   )
   __a					=  AutoModelForSequenceClassification.from_pretrained(
       """hf-internal-testing/mrpc-bert-base-cased"""		,				return_dict=lowerCAmelCase__   )
   __a	,    __a					=  accelerator.prepare(lowerCAmelCase__		,				lowerCAmelCase__   )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  []
   for batch in dataloader:
      __a	,    __a					=  batch.values()
      with torch.no_grad():
         __a					=  model(lowerCAmelCase__   )
         __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(lowerCAmelCase__   )
      targs.append(lowerCAmelCase__   )
   __a	,    __a					=  torch.cat(lowerCAmelCase__   ), torch.cat(lowerCAmelCase__   )
   return logits, targs




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=82		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False		,				lowerCAmelCase__=16   ):
   __a	,    __a	,    __a					=  get_basic_setup(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   __a	,    __a					=  generate_predictions(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   assert (
       len(lowerCAmelCase__   ) == num_samples
   ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase__   )}'''




def  a   (lowerCAmelCase__ = False		,				lowerCAmelCase__ = False   ):
   __a					=  evaluate.load("""glue"""		,				"""mrpc"""   )
   __a	,    __a					=  get_mrpc_setup(lowerCAmelCase__		,				lowerCAmelCase__   )
   # First do baseline
   __a	,    __a	,    __a					=  setup["""no"""]
   model.to(lowerCAmelCase__   )
   model.eval()
   for batch in dataloader:
      batch.to(lowerCAmelCase__   )
      with torch.inference_mode():
         __a					=  model(**lowerCAmelCase__   )
      __a					=  outputs.logits.argmax(dim=-1   )
      metric.add_batch(predictions=lowerCAmelCase__		,				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(**lowerCAmelCase__   )
      __a					=  outputs.logits.argmax(dim=-1   )
      __a					=  batch["""labels"""]
      __a	,    __a					=  accelerator.gather_for_metrics((preds, references)   )
      metric.add_batch(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )
   __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  a   ():
   __a					=  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]:
         __a					=  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**"""   )
   __a					=  Accelerator()
   test_torch_metrics(lowerCAmelCase__		,				512   )
   accelerator.state._reset_state()




def  a   (lowerCAmelCase__   ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()







import unittest

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 ViTImageProcessor


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=224  ,       __A=30  ,       __A=400  ,       __A=True  ,       __A=None  ,       __A=True  ,       __A=[0.5, 0.5, 0.5]  ,       __A=[0.5, 0.5, 0.5]  ,       ):
        __a					=  size if size is not None else {"""height""": 18, """width""": 18}
        __a					=  parent
        __a					=  batch_size
        __a					=  num_channels
        __a					=  image_size
        __a					=  min_resolution
        __a					=  max_resolution
        __a					=  do_resize
        __a					=  size
        __a					=  do_normalize
        __a					=  image_mean
        __a					=  image_std


     def 	snake_case_  (	self    ):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }







@require_torch
@require_vision
class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      ViTImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  EfficientFormerImageProcessorTester(self    )


     @property
     def 	snake_case_  (	self    ):
        return self.image_proc_tester.prepare_image_processor_dict()


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__A  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__A  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__A  ,       """size"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       Image.Image    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       numpify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       np.ndarray    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       torchify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       torch.Tensor    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )



