




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						:   Any		=   "true"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   int=82						,							_lowerCAmelCase							:   Any=16	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Accelerator						,							_lowerCAmelCase							:   Tuple=False	):




 """simple docstring"""







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

 def tokenize_function(_lowerCAmelCase							:   Tuple	):
  UpperCAmelCase__							 =						tokenizer(examples["sentence1"]						,							examples["sentence2"]						,							truncation=_lowerCAmelCase						,							max_length=_lowerCAmelCase	)
  return outputs

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

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

 def collate_fn(_lowerCAmelCase							:   List[str]	):
  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 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Accelerator						,							_lowerCAmelCase							:   str=82						,							_lowerCAmelCase							:   Optional[int]=False						,							_lowerCAmelCase							:   Any=False						,							_lowerCAmelCase							:   Union[str, Any]=16	):




 """simple docstring"""







 UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						get_basic_setup(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
 UpperCAmelCase__ , UpperCAmelCase__							 =						generate_predictions(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
 assert (
     len(_lowerCAmelCase	) == num_samples
 ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowerCAmelCase	)}'''



def 						lowerCAmelCase		(		_lowerCAmelCase							:   bool = False						,							_lowerCAmelCase							:   bool = False	):




 """simple docstring"""







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

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




 """simple docstring"""







 UpperCAmelCase__							 =						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]:
   UpperCAmelCase__							 =						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**"	)
 UpperCAmelCase__							 =						Accelerator()
 test_torch_metrics(_lowerCAmelCase						,							512	)
 accelerator.state._reset_state()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







 main()


if __name__ == "__main__":
  main()











import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		IFInpaintingSuperResolutionPipeline
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}		)
      UpperCAmelCase_        =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       return self._get_superresolution_dummy_components()
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]=0      )     ->			int:
       if str(lowerCamelCase      ).startswith("mps"      ):
        UpperCAmelCase__							 =						torch.manual_seed(lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

       UpperCAmelCase__							 =						floats_tensor((1, 3, 16, 16) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						{
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_image,
           "mask_image": mask_image,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }

       return inputs
      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available() ,      reason="XFormers attention is only available with CUDA and `xformers` installed" ,      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Dict:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda" ,      reason="float16 requires CUDA"      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1e-1      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_save_load_local()







      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2 ,      )





