


"""simple docstring"""






import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							ConsistencyModelPipeline
		A    =							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
		A    =							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

		# Override required_optional_params to remove num_images_per_prompt
		A    =							frozenset(
		    [
		        "num_inference_steps",
		        "generator",
		        "latents",
		        "output_type",
		        "return_dict",
		        "callback",
		        "callback_steps",
		    ]		)
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	UNetaDModel.from_pretrained(
					    '''diffusers/consistency-models-test'''					,				subfolder='''test_unet'''					,				)
					return unet
		@property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(
					    '''diffusers/consistency-models-test'''					,				subfolder='''test_unet_class_cond'''					,				)
					return unet
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=False   )      ->	Optional[Any]:

					"""simple docstring"""



					if class_cond:
								_UpperCamelCase					:List[str]					     =	self.dummy_cond_unet
					else:
								_UpperCamelCase					:Any					     =	self.dummy_uncond_unet

					# Default to CM multistep sampler
					_UpperCamelCase					:int					     =	CMStochasticIterativeScheduler(
					    num_train_timesteps=40					,				sigma_min=0.0_0_2					,				sigma_max=8_0.0					,				)

					_UpperCamelCase					:Dict					     =	{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					}

					return components
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=0   )      ->	List[Any]:

					"""simple docstring"""



					if str(SCREAMING_SNAKE_CASE__   ).startswith('''mps'''   ):
								_UpperCamelCase					:Optional[Any]					     =	torch.manual_seed(SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Optional[int]					     =	torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	{
					    '''batch_size''': 1,
					    '''num_inference_steps''': None,
					    '''timesteps''': [22, 0],
					    '''generator''': generator,
					    '''output_type''': '''np''',
					}

					return inputs
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					_UpperCamelCase					:Dict					     =	self.get_dummy_components()
					_UpperCamelCase					:Dict					     =	ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 32, 32, 3)

					_UpperCamelCase					:Tuple					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					_UpperCamelCase					:Optional[Any]					     =	self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	0
					_UpperCamelCase					:Optional[int]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 32, 32, 3)

					_UpperCamelCase					:Optional[Any]					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:Tuple					     =	np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					_UpperCamelCase					:List[str]					     =	self.get_dummy_components()
					_UpperCamelCase					:Any					     =	ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	1
					_UpperCamelCase					:Union[str, Any]					     =	None
					_UpperCamelCase					:str					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 32, 32, 3)

					_UpperCamelCase					:Tuple					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:Dict					     =	np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3





		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					_UpperCamelCase					:Optional[Any]					     =	self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	1
					_UpperCamelCase					:Optional[int]					     =	None
					_UpperCamelCase					:Optional[Any]					     =	0
					_UpperCamelCase					:Optional[int]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 32, 32, 3)

					_UpperCamelCase					:List[str]					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:Any					     =	np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3






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

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__="cpu"					,				SCREAMING_SNAKE_CASE__=torch.floataa					,				SCREAMING_SNAKE_CASE__=(1, 3, 64, 64)   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	torch.manual_seed(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	{
					    '''num_inference_steps''': None,
					    '''timesteps''': [22, 0],
					    '''class_labels''': 0,
					    '''generator''': generator,
					    '''output_type''': '''np''',
					}

					if get_fixed_latents:
								_UpperCamelCase					:Optional[Any]					     =	self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__					,				shape=SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:List[str]					     =	latents

					return inputs
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__="cpu"					,				SCREAMING_SNAKE_CASE__=torch.floataa					,				SCREAMING_SNAKE_CASE__=(1, 3, 64, 64)   )      ->	Optional[int]:

					"""simple docstring"""



					if type(SCREAMING_SNAKE_CASE__   ) == str:
								_UpperCamelCase					:List[str]					     =	torch.device(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__   )
					return latents
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	UNetaDModel.from_pretrained('''diffusers/consistency_models'''					,				subfolder='''diffusers_cd_imagenet64_l2'''   )
					_UpperCamelCase					:int					     =	CMStochasticIterativeScheduler(
					    num_train_timesteps=40					,				sigma_min=0.0_0_2					,				sigma_max=8_0.0					,				)
					_UpperCamelCase					:int					     =	ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(torch_device=SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	self.get_inputs()
					_UpperCamelCase					:List[Any]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:List[Any]					     =	image[0, -3:, -3:, -1]

					_UpperCamelCase					:Dict					     =	np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	UNetaDModel.from_pretrained('''diffusers/consistency_models'''					,				subfolder='''diffusers_cd_imagenet64_l2'''   )
					_UpperCamelCase					:Optional[int]					     =	CMStochasticIterativeScheduler(
					    num_train_timesteps=40					,				sigma_min=0.0_0_2					,				sigma_max=8_0.0					,				)
					_UpperCamelCase					:Optional[Any]					     =	ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(torch_device=SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	self.get_inputs()
					_UpperCamelCase					:Dict					     =	1
					_UpperCamelCase					:Optional[Any]					     =	None
					_UpperCamelCase					:Optional[Any]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:Union[str, Any]					     =	image[0, -3:, -3:, -1]

					_UpperCamelCase					:Optional[Any]					     =	np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2
		@require_torch_a
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained('''diffusers/consistency_models'''					,				subfolder='''diffusers_cd_imagenet64_l2'''   )
					_UpperCamelCase					:List[str]					     =	CMStochasticIterativeScheduler(
					    num_train_timesteps=40					,				sigma_min=0.0_0_2					,				sigma_max=8_0.0					,				)
					_UpperCamelCase					:List[Any]					     =	ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(torch_device=SCREAMING_SNAKE_CASE__					,				torch_dtype=torch.floataa   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__   )
					# Ensure usage of flash attention in torch 2.0
					with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__					,				enable_math=SCREAMING_SNAKE_CASE__					,				enable_mem_efficient=SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:List[str]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:Union[str, Any]					     =	image[0, -3:, -3:, -1]

					_UpperCamelCase					:Any					     =	np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3





		@require_torch_a
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	UNetaDModel.from_pretrained('''diffusers/consistency_models'''					,				subfolder='''diffusers_cd_imagenet64_l2'''   )
					_UpperCamelCase					:Dict					     =	CMStochasticIterativeScheduler(
					    num_train_timesteps=40					,				sigma_min=0.0_0_2					,				sigma_max=8_0.0					,				)
					_UpperCamelCase					:str					     =	ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(torch_device=SCREAMING_SNAKE_CASE__					,				torch_dtype=torch.floataa   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	1
					_UpperCamelCase					:int					     =	None
					# Ensure usage of flash attention in torch 2.0
					with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__					,				enable_math=SCREAMING_SNAKE_CASE__					,				enable_mem_efficient=SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	pipe(**SCREAMING_SNAKE_CASE__   ).images
					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:Optional[int]					     =	image[0, -3:, -3:, -1]

					_UpperCamelCase					:Tuple					     =	np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3





"""simple docstring"""






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


UpperCamelCase__				:Dict			   =	"""true"""





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





def 				A_       ( snake_case__       ,					snake_case__=False		)					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
			_UpperCamelCase					:int					     =	load_dataset('''glue'''       ,					'''mrpc'''       ,					split='''validation'''		)

			def tokenize_function(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	tokenizer(examples['''sentence1''']       ,					examples['''sentence2''']       ,					truncation=snake_case__       ,					max_length=snake_case__		)
						return outputs

			with accelerator.main_process_first():
						_UpperCamelCase					:Optional[int]					     =	dataset.map(
						    snake_case__       ,					batched=snake_case__       ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,					)

			_UpperCamelCase					:Tuple					     =	tokenized_datasets.rename_column('''label'''       ,					'''labels'''		)

			def collate_fn(snake_case__		):
						if use_longest:
									return tokenizer.pad(snake_case__       ,					padding='''longest'''       ,					return_tensors='''pt'''		)
						return tokenizer.pad(snake_case__       ,					padding='''max_length'''       ,					max_length=1_28       ,					return_tensors='''pt'''		)

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





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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	[]
			for batch in dataloader:
						_UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	batch.values()
						with torch.no_grad():
									_UpperCamelCase					:List[Any]					     =	model(snake_case__		)
									_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	accelerator.gather_for_metrics((logit, target)		)
									logits_and_targets.append((logit, target)		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	[], []
			for logit, targ in logits_and_targets:
						logits.append(snake_case__		)
						targs.append(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	torch.cat(snake_case__		), torch.cat(snake_case__		)
			return logits, targs





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=False       ,					snake_case__=False       ,					snake_case__=16		)					->  Dict:
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	get_basic_setup(snake_case__       ,					snake_case__       ,					snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	generate_predictions(snake_case__       ,					snake_case__       ,					snake_case__		)
			assert (
			    len(snake_case__		) == num_samples
			), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__		)}"





def 				A_       ( snake_case__ = False       ,					snake_case__ = False		)					->  Optional[Any]:
			_UpperCamelCase					:Any					     =	evaluate.load('''glue'''       ,					'''mrpc'''		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	get_mrpc_setup(snake_case__       ,					snake_case__		)
			# First do baseline
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	setup['''no''']
			model.to(snake_case__		)
			model.eval()
			for batch in dataloader:
						batch.to(snake_case__		)
						with torch.inference_mode():
									_UpperCamelCase					:str					     =	model(**snake_case__		)
						_UpperCamelCase					:Dict					     =	outputs.logits.argmax(dim=-1		)
						metric.add_batch(predictions=snake_case__       ,					references=batch['''labels''']		)
			_UpperCamelCase					:int					     =	metric.compute()

			# Then do distributed
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	setup['''ddp''']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									_UpperCamelCase					:Dict					     =	model(**snake_case__		)
						_UpperCamelCase					:str					     =	outputs.logits.argmax(dim=-1		)
						_UpperCamelCase					:Union[str, Any]					     =	batch['''labels''']
						_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	accelerator.gather_for_metrics((preds, references)		)
						metric.add_batch(predictions=snake_case__       ,					references=snake_case__		)
			_UpperCamelCase					:Optional[Any]					     =	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_       ( )					->  Optional[Any]:
			_UpperCamelCase					:int					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=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(snake_case__       ,					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]:
									_UpperCamelCase					:Tuple					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=snake_case__		)
									if accelerator.is_local_main_process:
												print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"		)
									test_torch_metrics(snake_case__       ,					99		)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test last batch is not dropped when perfectly divisible**'''		)
			_UpperCamelCase					:Any					     =	Accelerator()
			test_torch_metrics(snake_case__       ,					5_12		)
			accelerator.state._reset_state()





def 				A_       ( snake_case__		)					->  Dict:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()

