





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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=82			,						_SCREAMING_SNAKE_CASE=16     )			->       List[Any]:


      '''simple docstring'''


      set_seed(42     )
      SCREAMING_SNAKE_CASE		=						RegressionModel()
      SCREAMING_SNAKE_CASE		=						deepcopy(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						RegressionDataset(length=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						DataLoader(_SCREAMING_SNAKE_CASE			,						batch_size=_SCREAMING_SNAKE_CASE     )
      model.to(accelerator.device     )
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.prepare(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )
      return model, ddp_model, dataloader



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=False     )			->       Any:


      '''simple docstring'''


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

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

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

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

      def collate_fn(_SCREAMING_SNAKE_CASE     ):
            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=1_28			,						return_tensors="""pt"""     )

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[]
      for batch in dataloader:
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						batch.values()
            with torch.no_grad():
                  SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE     )
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.gather_for_metrics((logit, target)     )
                  logits_and_targets.append((logit, target)     )
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						[], []
      for logit, targ in logits_and_targets:
            logits.append(_SCREAMING_SNAKE_CASE     )
            targs.append(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						torch.cat(_SCREAMING_SNAKE_CASE     ), torch.cat(_SCREAMING_SNAKE_CASE     )
      return logits, targs



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=82			,						_SCREAMING_SNAKE_CASE=False			,						_SCREAMING_SNAKE_CASE=False			,						_SCREAMING_SNAKE_CASE=16     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						get_basic_setup(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						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        __lowercase       (				_SCREAMING_SNAKE_CASE = False			,						_SCREAMING_SNAKE_CASE = False     )			->       Dict:


      '''simple docstring'''


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

      # Then do distributed
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						setup["""ddp"""]
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						outputs.logits.argmax(dim=-1     )
            SCREAMING_SNAKE_CASE		=						batch["""labels"""]
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.gather_for_metrics((preds, references)     )
            metric.add_batch(predictions=_SCREAMING_SNAKE_CASE			,						references=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						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        __lowercase       (				)			->       Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						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]:
                  SCREAMING_SNAKE_CASE		=						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**"""     )
      SCREAMING_SNAKE_CASE		=						Accelerator()
      test_torch_metrics(_SCREAMING_SNAKE_CASE			,						5_12     )
      accelerator.state._reset_state()



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Optional[int]:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()









import unittest

import numpy as np

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

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 DPTImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=7						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   Union[str, Any]=18						,lowerCamelCase__						:   Optional[int]=30						,lowerCamelCase__						:   int=400						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Union[str, Any]=None						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Optional[int]=[0.5, 0.5, 0.5]						,lowerCamelCase__						:   Any=[0.5, 0.5, 0.5]						,)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""height""": 18, """width""": 18}
           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						min_resolution
           SCREAMING_SNAKE_CASE		=						max_resolution
           SCREAMING_SNAKE_CASE		=						do_resize
           SCREAMING_SNAKE_CASE		=						size
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean
           SCREAMING_SNAKE_CASE		=						image_std






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Union[str, Any]:




           '''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,
           }

@require_torch
@require_vision
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      DPTImageProcessor if is_vision_available() else None






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DPTImageProcessingTester(self	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""image_mean"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""image_std"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_normalize"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_resize"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""size"""	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size						,{"""height""": 18, """width""": 18}	)

           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict						,size=42	)
           self.assertEqual(image_processor.size						,{"""height""": 42, """width""": 42}	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




           '''simple docstring'''


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

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

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


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

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

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[int]:




           '''simple docstring'''


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

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

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)


