

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      :					int					    =      '''true'''





def      A__					(			__A  :						Any     ,				__A  :						Union[str, Any]=82     ,				__A  :						Optional[Any]=16			)					->List[Any]:
      set_seed(42			)
      __A    =RegressionModel()
      __A    =deepcopy(__A			)
      __A    =RegressionDataset(length=__A			)
      __A    =DataLoader(__A     ,				batch_size=__A			)
      model.to(accelerator.device			)
      __A    ,		__A    =accelerator.prepare(__A     ,				__A			)
      return model, ddp_model, dataloader





def      A__					(			__A  :						Accelerator     ,				__A  :						Dict=False			)					->List[Any]:
      __A    =AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''			)
      __A    =load_dataset('''glue'''     ,				'''mrpc'''     ,				split='''validation'''			)

      def tokenize_function(__A  :						str			):
            __A    =tokenizer(examples['''sentence1''']     ,				examples['''sentence2''']     ,				truncation=__A     ,				max_length=__A			)
            return outputs

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

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

      def collate_fn(__A  :						Optional[Any]			):
            if use_longest:
                  return tokenizer.pad(__A     ,				padding='''longest'''     ,				return_tensors='''pt'''			)
            return tokenizer.pad(__A     ,				padding='''max_length'''     ,				max_length=1_28     ,				return_tensors='''pt'''			)

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





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





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]     ,				__A  :						List[Any]			)					->str:
      __A    =[]
      for batch in dataloader:
            __A    ,		__A    =batch.values()
            with torch.no_grad():
                  __A    =model(__A			)
                  __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(__A			)
            targs.append(__A			)
      __A    ,		__A    =torch.cat(__A			), torch.cat(__A			)
      return logits, targs





def      A__					(			__A  :						Accelerator     ,				__A  :						int=82     ,				__A  :						str=False     ,				__A  :						List[Any]=False     ,				__A  :						str=16			)					->Any:
      __A    ,		__A    ,		__A    =get_basic_setup(__A     ,				__A     ,				__A			)
      __A    ,		__A    =generate_predictions(__A     ,				__A     ,				__A			)
      assert (
          len(__A			) == num_samples
      ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__A			)}'''





def      A__					(			__A  :						bool = False     ,				__A  :						bool = False			)					->str:
      __A    =evaluate.load('''glue'''     ,				'''mrpc'''			)
      __A    ,		__A    =get_mrpc_setup(__A     ,				__A			)
      # First do baseline
      __A    ,		__A    ,		__A    =setup['''no''']
      model.to(__A			)
      model.eval()
      for batch in dataloader:
            batch.to(__A			)
            with torch.inference_mode():
                  __A    =model(**__A			)
            __A    =outputs.logits.argmax(dim=-1			)
            metric.add_batch(predictions=__A     ,				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(**__A			)
            __A    =outputs.logits.argmax(dim=-1			)
            __A    =batch['''labels''']
            __A    ,		__A    =accelerator.gather_for_metrics((preds, references)			)
            metric.add_batch(predictions=__A     ,				references=__A			)
      __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__					(			)					->List[Any]:
      __A    =Accelerator(split_batches=__A     ,				dispatch_batches=__A			)
      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(__A     ,				__A			)
                        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=__A     ,				dispatch_batches=__A			)
                  if accelerator.is_local_main_process:
                        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''			)
                  test_torch_metrics(__A     ,				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(__A     ,				5_12			)
      accelerator.state._reset_state()





def      A__					(			__A  :						Dict			)					->Optional[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
   main()





import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





@slow
@require_torch_gpu
class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-1


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_dpmpp_2m'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe(
                 [prompt] ,   generator=lowercase__ ,   guidance_scale=7.5 ,   num_inference_steps=1_5 ,   output_type='''np''' ,   use_karras_sigmas=lowercase__ ,   )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array(
                 [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2


