


"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							UnCLIPImageVariationPipeline
		A    =							IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
		A    =							IMAGE_VARIATION_BATCH_PARAMS

		A    =							[
		    "generator",
		    "return_dict",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]
		A    =							False
		@property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					return 32
		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return 32
		@property
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					return self.time_input_dim
		@property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					return self.time_input_dim * 4
		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return 1_00
		@property
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
					return tokenizer
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:Optional[Any]					     =	CLIPTextConfig(
					    bos_token_id=0					,				eos_token_id=2					,				hidden_size=self.text_embedder_hidden_size					,				projection_dim=self.text_embedder_hidden_size					,				intermediate_size=37					,				layer_norm_eps=1E-05					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=10_00					,				)
					return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size					,				projection_dim=self.text_embedder_hidden_size					,				num_hidden_layers=5					,				num_attention_heads=4					,				image_size=32					,				intermediate_size=37					,				patch_size=1					,				)
					return CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					torch.manual_seed(0   )

					_UpperCamelCase					:Optional[int]					     =	{
					    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
					    '''time_embed_dim''': self.time_embed_dim,
					    '''cross_attention_dim''': self.cross_attention_dim,
					}

					_UpperCamelCase					:Optional[int]					     =	UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE__   )
					return model
		@property
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					torch.manual_seed(0   )

					_UpperCamelCase					:Tuple					     =	{
					    '''sample_size''': 32,
					    # RGB in channels
					    '''in_channels''': 3,
					    # Out channels is double in channels because predicts mean and variance
					    '''out_channels''': 6,
					    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
					    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
					    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
					    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    '''layers_per_block''': 1,
					    '''cross_attention_dim''': self.cross_attention_dim,
					    '''attention_head_dim''': 4,
					    '''resnet_time_scale_shift''': '''scale_shift''',
					    '''class_embed_type''': '''identity''',
					}

					_UpperCamelCase					:List[str]					     =	UNetaDConditionModel(**SCREAMING_SNAKE_CASE__   )
					return model
		@property
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					return {
					    "sample_size": 64,
					    "layers_per_block": 1,
					    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
					    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
					    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    "in_channels": 6,
					    "out_channels": 3,
					}
		@property
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					torch.manual_seed(0   )

					_UpperCamelCase					:Any					     =	UNetaDModel(**self.dummy_super_res_kwargs   )
					return model
		@property
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					torch.manual_seed(1   )

					_UpperCamelCase					:int					     =	UNetaDModel(**self.dummy_super_res_kwargs   )
					return model
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self.dummy_decoder
					_UpperCamelCase					:str					     =	self.dummy_text_proj
					_UpperCamelCase					:List[Any]					     =	self.dummy_text_encoder
					_UpperCamelCase					:Union[str, Any]					     =	self.dummy_tokenizer
					_UpperCamelCase					:Optional[Any]					     =	self.dummy_super_res_first
					_UpperCamelCase					:str					     =	self.dummy_super_res_last

					_UpperCamelCase					:Dict					     =	UnCLIPScheduler(
					    variance_type='''learned_range'''					,				prediction_type='''epsilon'''					,				num_train_timesteps=10_00					,				)

					_UpperCamelCase					:Any					     =	UnCLIPScheduler(
					    variance_type='''fixed_small_log'''					,				prediction_type='''epsilon'''					,				num_train_timesteps=10_00					,				)

					_UpperCamelCase					:str					     =	CLIPImageProcessor(crop_size=32					,				size=32   )

					_UpperCamelCase					:Union[str, Any]					     =	self.dummy_image_encoder

					return {
					    "decoder": decoder,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "text_proj": text_proj,
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder,
					    "super_res_first": super_res_first,
					    "super_res_last": super_res_last,
					    "decoder_scheduler": decoder_scheduler,
					    "super_res_scheduler": super_res_scheduler,
					}
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=True   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	floats_tensor((1, 3, 32, 32)					,				rng=random.Random(SCREAMING_SNAKE_CASE__   )   ).to(SCREAMING_SNAKE_CASE__   )
					if str(SCREAMING_SNAKE_CASE__   ).startswith('''mps'''   ):
								_UpperCamelCase					:int					     =	torch.manual_seed(SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Dict					     =	torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )

					if pil_image:
								_UpperCamelCase					:Any					     =	input_image * 0.5 + 0.5
								_UpperCamelCase					:str					     =	input_image.clamp(0					,				1   )
								_UpperCamelCase					:str					     =	input_image.cpu().permute(0					,				2					,				3					,				1   ).float().numpy()
								_UpperCamelCase					:Optional[int]					     =	DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE__   )[0]

					return {
					    "image": input_image,
					    "generator": generator,
					    "decoder_num_inference_steps": 2,
					    "super_res_num_inference_steps": 2,
					    "output_type": "np",
					}
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	'''cpu'''

					_UpperCamelCase					:Tuple					     =	self.get_dummy_components()

					_UpperCamelCase					:Dict					     =	self.pipeline_class(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[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__					,				pil_image=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	pipe(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	output.images

					_UpperCamelCase					:Dict					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	pipe(
					    **SCREAMING_SNAKE_CASE__					,				return_dict=SCREAMING_SNAKE_CASE__					,				)[0]

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

					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:Union[str, Any]					     =	np.array(
					    [
					        0.9_9_9_7,
					        0.0_0_0_2,
					        0.9_9_9_7,
					        0.9_9_9_7,
					        0.9_9_6_9,
					        0.0_0_2_3,
					        0.9_9_9_7,
					        0.9_9_6_9,
					        0.9_9_7_0,
					    ]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	'''cpu'''

					_UpperCamelCase					:Optional[int]					     =	self.get_dummy_components()

					_UpperCamelCase					:Any					     =	self.pipeline_class(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	pipe.to(SCREAMING_SNAKE_CASE__   )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	pipe(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	output.images

					_UpperCamelCase					:int					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	pipe(
					    **SCREAMING_SNAKE_CASE__					,				return_dict=SCREAMING_SNAKE_CASE__					,				)[0]

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

					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:int					     =	np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	'''cpu'''

					_UpperCamelCase					:Any					     =	self.get_dummy_components()

					_UpperCamelCase					:Any					     =	self.pipeline_class(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	pipe.to(SCREAMING_SNAKE_CASE__   )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	[
					    pipeline_inputs['''image'''],
					    pipeline_inputs['''image'''],
					]

					_UpperCamelCase					:Optional[Any]					     =	pipe(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	output.images

					_UpperCamelCase					:Optional[int]					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	[
					    tuple_pipeline_inputs['''image'''],
					    tuple_pipeline_inputs['''image'''],
					]

					_UpperCamelCase					:Optional[Any]					     =	pipe(
					    **SCREAMING_SNAKE_CASE__					,				return_dict=SCREAMING_SNAKE_CASE__					,				)[0]

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

					assert image.shape == (2, 64, 64, 3)

					_UpperCamelCase					:Optional[int]					     =	np.array(
					    [
					        0.9_9_9_7,
					        0.9_9_8_9,
					        0.0_0_0_8,
					        0.0_0_2_1,
					        0.9_9_6_0,
					        0.0_0_1_8,
					        0.0_0_1_4,
					        0.0_0_0_2,
					        0.9_9_3_3,
					    ]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	torch.device('''cpu'''   )






					class      A:

							"""simple docstring"""





							A    =							1


					_UpperCamelCase					:Union[str, Any]					     =	self.get_dummy_components()

					_UpperCamelCase					:Optional[int]					     =	self.pipeline_class(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	pipe.to(SCREAMING_SNAKE_CASE__   )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(0   )
					_UpperCamelCase					:Union[str, Any]					     =	pipe.decoder.dtype
					_UpperCamelCase					:Union[str, Any]					     =	1

					_UpperCamelCase					:Optional[int]					     =	(
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					_UpperCamelCase					:Union[str, Any]					     =	pipe.prepare_latents(
					    SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				latents=SCREAMING_SNAKE_CASE__					,				scheduler=DummyScheduler()   )

					_UpperCamelCase					:Optional[Any]					     =	(
					    batch_size,
					    pipe.super_res_first.config.in_channels // 2,
					    pipe.super_res_first.config.sample_size,
					    pipe.super_res_first.config.sample_size,
					)
					_UpperCamelCase					:str					     =	pipe.prepare_latents(
					    SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				latents=SCREAMING_SNAKE_CASE__					,				scheduler=DummyScheduler()   )

					_UpperCamelCase					:Any					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	pipe(
					    **SCREAMING_SNAKE_CASE__					,				decoder_latents=SCREAMING_SNAKE_CASE__					,				super_res_latents=SCREAMING_SNAKE_CASE__   ).images

					_UpperCamelCase					:Tuple					     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					,				pil_image=SCREAMING_SNAKE_CASE__   )
					# Don't pass image, instead pass embedding
					_UpperCamelCase					:Tuple					     =	pipeline_inputs.pop('''image'''   )
					_UpperCamelCase					:Union[str, Any]					     =	pipe.image_encoder(SCREAMING_SNAKE_CASE__   ).image_embeds

					_UpperCamelCase					:List[str]					     =	pipe(
					    **SCREAMING_SNAKE_CASE__					,				decoder_latents=SCREAMING_SNAKE_CASE__					,				super_res_latents=SCREAMING_SNAKE_CASE__					,				image_embeddings=SCREAMING_SNAKE_CASE__					,				).images

					# make sure passing text embeddings manually is identical
					assert np.abs(img_out_a - img_out_a   ).max() < 1E-4
		@skip_mps
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	torch_device == '''cpu'''

					# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
					_UpperCamelCase					:Any					     =	1E-2

					self._test_attention_slicing_forward_pass(
					    test_max_difference=SCREAMING_SNAKE_CASE__					,				expected_max_diff=SCREAMING_SNAKE_CASE__   )
		@skip_mps
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	torch_device == '''cpu'''
					_UpperCamelCase					:Optional[int]					     =	True
					_UpperCamelCase					:Tuple					     =	[
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					self._test_inference_batch_single_identical(
					    test_max_difference=SCREAMING_SNAKE_CASE__					,				relax_max_difference=SCREAMING_SNAKE_CASE__					,				additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE__					,				)
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	[
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					if torch_device == "mps":
								# TODO: MPS errors with larger batch sizes
								_UpperCamelCase					:Optional[Any]					     =	[2, 3]
								self._test_inference_batch_consistent(
								    batch_sizes=SCREAMING_SNAKE_CASE__					,				additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE__					,				)
					else:
								self._test_inference_batch_consistent(
								    additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE__   )
		@skip_mps
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					return super().test_dict_tuple_outputs_equivalent()
		@skip_mps
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return super().test_save_load_local()





		@skip_mps
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return super().test_save_load_optional_components()






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

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



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





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

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''   )
					_UpperCamelCase					:Tuple					     =	load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''   )

					_UpperCamelCase					:Dict					     =	UnCLIPImageVariationPipeline.from_pretrained(
					    '''kakaobrain/karlo-v1-alpha-image-variations'''					,				torch_dtype=torch.floataa   )
					_UpperCamelCase					:int					     =	pipeline.to(SCREAMING_SNAKE_CASE__   )
					pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:int					     =	torch.Generator(device='''cpu'''   ).manual_seed(0   )
					_UpperCamelCase					:Optional[Any]					     =	pipeline(
					    SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''np'''					,				)

					_UpperCamelCase					:Tuple					     =	output.images[0]

					assert image.shape == (2_56, 2_56, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				15   )





"""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()

