

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_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  int										=				UnCLIPImageVariationPipeline
							_UpperCAmelCase   :  Tuple										=				IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
							_UpperCAmelCase   :  int										=				IMAGE_VARIATION_BATCH_PARAMS

							_UpperCAmelCase   :  Dict										=				[
							    '''generator''',
							    '''return_dict''',
							    '''decoder_num_inference_steps''',
							    '''super_res_num_inference_steps''',
							]
							_UpperCAmelCase   :  str										=				False
							@property
							def    lowerCAmelCase					(		self		:      List[Any]):
								return 3_2
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return 3_2
							@property
							def    lowerCAmelCase					(		self		:      List[str]):
								return self.time_input_dim
							@property
							def    lowerCAmelCase					(		self		:      Dict):
								return self.time_input_dim * 4
							@property
							def    lowerCAmelCase					(		self		:      Dict):
								return 1_0_0
							@property
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    List[Any] 	=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
								return tokenizer
							@property
							def    lowerCAmelCase					(		self		:      Tuple):
								torch.manual_seed(0)
								__lowerCamelCase    :    int 	=			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=3_7       ,layer_norm_eps=1E-05       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1_0_0_0       ,)
								return CLIPTextModelWithProjection(lowercase_)
							@property
							def    lowerCAmelCase					(		self		:      str):
								torch.manual_seed(0)
								__lowerCamelCase    :    List[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=3_2       ,intermediate_size=3_7       ,patch_size=1       ,)
								return CLIPVisionModelWithProjection(lowercase_)
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(0)

								__lowerCamelCase    :    List[str] 	=			{
								    'clip_embeddings_dim': self.text_embedder_hidden_size,
								    'time_embed_dim': self.time_embed_dim,
								    'cross_attention_dim': self.cross_attention_dim,
								}

								__lowerCamelCase    :    List[Any] 	=			UnCLIPTextProjModel(**lowercase_)
								return model
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(0)

								__lowerCamelCase    :    Tuple 	=			{
								    'sample_size': 3_2,
								    # 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',
								}

								__lowerCamelCase    :    Optional[Any] 	=			UNetaDConditionModel(**lowercase_)
								return model
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return {
								    "sample_size": 6_4,
								    "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    lowerCAmelCase					(		self		:      Dict):
								torch.manual_seed(0)

								__lowerCamelCase    :    Union[str, Any] 	=			UNetaDModel(**self.dummy_super_res_kwargs)
								return model
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(1)

								__lowerCamelCase    :    Optional[Any] 	=			UNetaDModel(**self.dummy_super_res_kwargs)
								return model
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Tuple 	=			self.dummy_decoder
								__lowerCamelCase    :    Optional[int] 	=			self.dummy_text_proj
								__lowerCamelCase    :    List[str] 	=			self.dummy_text_encoder
								__lowerCamelCase    :    str 	=			self.dummy_tokenizer
								__lowerCamelCase    :    int 	=			self.dummy_super_res_first
								__lowerCamelCase    :    List[str] 	=			self.dummy_super_res_last

								__lowerCamelCase    :    Any 	=			UnCLIPScheduler(
								    variance_type='learned_range'       ,prediction_type='epsilon'       ,num_train_timesteps=1_0_0_0       ,)

								__lowerCamelCase    :    Tuple 	=			UnCLIPScheduler(
								    variance_type='fixed_small_log'       ,prediction_type='epsilon'       ,num_train_timesteps=1_0_0_0       ,)

								__lowerCamelCase    :    str 	=			CLIPImageProcessor(crop_size=3_2       ,size=3_2)

								__lowerCamelCase    :    Tuple 	=			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    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      List[str]=0       ,SCREAMING_SNAKE_CASE__		:      Dict=True):
								__lowerCamelCase    :    str 	=			floats_tensor((1, 3, 3_2, 3_2)       ,rng=random.Random(lowercase_)).to(lowercase_)
								if str(lowercase_).startswith('mps'):
									__lowerCamelCase    :    Optional[int] 	=			torch.manual_seed(lowercase_)
								else:
									__lowerCamelCase    :    Optional[int] 	=			torch.Generator(device=lowercase_).manual_seed(lowercase_)

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

								return {
								    "image": input_image,
								    "generator": generator,
								    "decoder_num_inference_steps": 2,
								    "super_res_num_inference_steps": 2,
								    "output_type": "np",
								}
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    str 	=			'cpu'

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

								__lowerCamelCase    :    Optional[Any] 	=			self.pipeline_class(**lowercase_)
								__lowerCamelCase    :    str 	=			pipe.to(lowercase_)

								pipe.set_progress_bar_config(disable=lowercase_)

								__lowerCamelCase    :    int 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)

								__lowerCamelCase    :    List[str] 	=			pipe(**lowercase_)
								__lowerCamelCase    :    str 	=			output.images

								__lowerCamelCase    :    List[Any] 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)

								__lowerCamelCase    :    Optional[int] 	=			pipe(
								    **lowercase_       ,return_dict=lowercase_       ,)[0]

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

								assert image.shape == (1, 6_4, 6_4, 3)

								__lowerCamelCase    :    Optional[Any] 	=			np.array(
								    [
								        0.9997,
								        0.0002,
								        0.9997,
								        0.9997,
								        0.9969,
								        0.0023,
								        0.9997,
								        0.9969,
								        0.9970,
								    ])

								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    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			'cpu'

								__lowerCamelCase    :    str 	=			self.get_dummy_components()

								__lowerCamelCase    :    Optional[int] 	=			self.pipeline_class(**lowercase_)
								__lowerCamelCase    :    Tuple 	=			pipe.to(lowercase_)

								pipe.set_progress_bar_config(disable=lowercase_)

								__lowerCamelCase    :    Union[str, Any] 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)

								__lowerCamelCase    :    List[str] 	=			pipe(**lowercase_)
								__lowerCamelCase    :    List[str] 	=			output.images

								__lowerCamelCase    :    str 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)

								__lowerCamelCase    :    Optional[int] 	=			pipe(
								    **lowercase_       ,return_dict=lowercase_       ,)[0]

								__lowerCamelCase    :    str 	=			image[0, -3:, -3:, -1]
								__lowerCamelCase    :    Any 	=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								__lowerCamelCase    :    str 	=			np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])

								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    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Any 	=			'cpu'

								__lowerCamelCase    :    Any 	=			self.get_dummy_components()

								__lowerCamelCase    :    Union[str, Any] 	=			self.pipeline_class(**lowercase_)
								__lowerCamelCase    :    Union[str, Any] 	=			pipe.to(lowercase_)

								pipe.set_progress_bar_config(disable=lowercase_)

								__lowerCamelCase    :    str 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)
								__lowerCamelCase    :    Optional[Any] 	=			[
								    pipeline_inputs['image'],
								    pipeline_inputs['image'],
								]

								__lowerCamelCase    :    List[Any] 	=			pipe(**lowercase_)
								__lowerCamelCase    :    List[Any] 	=			output.images

								__lowerCamelCase    :    int 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)
								__lowerCamelCase    :    List[str] 	=			[
								    tuple_pipeline_inputs['image'],
								    tuple_pipeline_inputs['image'],
								]

								__lowerCamelCase    :    List[Any] 	=			pipe(
								    **lowercase_       ,return_dict=lowercase_       ,)[0]

								__lowerCamelCase    :    Tuple 	=			image[0, -3:, -3:, -1]
								__lowerCamelCase    :    Union[str, Any] 	=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (2, 6_4, 6_4, 3)

								__lowerCamelCase    :    Union[str, Any] 	=			np.array(
								    [
								        0.9997,
								        0.9989,
								        0.0008,
								        0.0021,
								        0.9960,
								        0.0018,
								        0.0014,
								        0.0002,
								        0.9933,
								    ])

								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    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Any 	=			torch.device('cpu')


								class 	A_		:
															_UpperCAmelCase   :  List[Any]										=				1
								__lowerCamelCase    :    str 	=			self.get_dummy_components()

								__lowerCamelCase    :    Union[str, Any] 	=			self.pipeline_class(**lowercase_)
								__lowerCamelCase    :    Any 	=			pipe.to(lowercase_)

								pipe.set_progress_bar_config(disable=lowercase_)

								__lowerCamelCase    :    Union[str, Any] 	=			torch.Generator(device=lowercase_).manual_seed(0)
								__lowerCamelCase    :    Optional[int] 	=			pipe.decoder.dtype
								__lowerCamelCase    :    Tuple 	=			1

								__lowerCamelCase    :    Tuple 	=			(
								    batch_size,
								    pipe.decoder.config.in_channels,
								    pipe.decoder.config.sample_size,
								    pipe.decoder.config.sample_size,
								)
								__lowerCamelCase    :    List[str] 	=			pipe.prepare_latents(
								    lowercase_       ,dtype=lowercase_       ,device=lowercase_       ,generator=lowercase_       ,latents=lowercase_       ,scheduler=DummyScheduler())

								__lowerCamelCase    :    Dict 	=			(
								    batch_size,
								    pipe.super_res_first.config.in_channels // 2,
								    pipe.super_res_first.config.sample_size,
								    pipe.super_res_first.config.sample_size,
								)
								__lowerCamelCase    :    List[str] 	=			pipe.prepare_latents(
								    lowercase_       ,dtype=lowercase_       ,device=lowercase_       ,generator=lowercase_       ,latents=lowercase_       ,scheduler=DummyScheduler())

								__lowerCamelCase    :    str 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)

								__lowerCamelCase    :    Optional[int] 	=			pipe(
								    **lowercase_       ,decoder_latents=lowercase_       ,super_res_latents=lowercase_).images

								__lowerCamelCase    :    List[Any] 	=			self.get_dummy_inputs(lowercase_       ,pil_image=lowercase_)
								# Don't pass image, instead pass embedding
								__lowerCamelCase    :    Any 	=			pipeline_inputs.pop('image')
								__lowerCamelCase    :    Tuple 	=			pipe.image_encoder(lowercase_).image_embeds

								__lowerCamelCase    :    Optional[Any] 	=			pipe(
								    **lowercase_       ,decoder_latents=lowercase_       ,super_res_latents=lowercase_       ,image_embeddings=lowercase_       ,).images

								# make sure passing text embeddings manually is identical
								assert np.abs(img_out_a - img_out_a).max() < 1E-4
							@skip_mps
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			torch_device == 'cpu'

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

								self._test_attention_slicing_forward_pass(
								    test_max_difference=lowercase_       ,expected_max_diff=lowercase_)
							@skip_mps
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			torch_device == 'cpu'
								__lowerCamelCase    :    Any 	=			True
								__lowerCamelCase    :    List[Any] 	=			[
								    'decoder_num_inference_steps',
								    'super_res_num_inference_steps',
								]

								self._test_inference_batch_single_identical(
								    test_max_difference=lowercase_       ,relax_max_difference=lowercase_       ,additional_params_copy_to_batched_inputs=lowercase_       ,)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			[
								    'decoder_num_inference_steps',
								    'super_res_num_inference_steps',
								]

								if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									__lowerCamelCase    :    Dict 	=			[2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=lowercase_       ,additional_params_copy_to_batched_inputs=lowercase_       ,)
								else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=lowercase_)
							@skip_mps
							def    lowerCAmelCase					(		self		:      str):
								return super().test_dict_tuple_outputs_equivalent()
							@skip_mps
							def    lowerCAmelCase					(		self		:      Any):
								return super().test_save_load_local()


							@skip_mps
							def    lowerCAmelCase					(		self		:      Any):
								return super().test_save_load_optional_components()


@slow
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


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

								__lowerCamelCase    :    Optional[int] 	=			UnCLIPImageVariationPipeline.from_pretrained(
								    'kakaobrain/karlo-v1-alpha-image-variations'       ,torch_dtype=torch.floataa)
								__lowerCamelCase    :    str 	=			pipeline.to(lowercase_)
								pipeline.set_progress_bar_config(disable=lowercase_)

								__lowerCamelCase    :    Any 	=			torch.Generator(device='cpu').manual_seed(0)
								__lowerCamelCase    :    Tuple 	=			pipeline(
								    lowercase_       ,generator=lowercase_       ,output_type='np'       ,)

								__lowerCamelCase    :    Optional[int] 	=			output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)

								assert_mean_pixel_difference(lowercase_       ,lowercase_       ,1_5)







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


a		="""true"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

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

	def collate_fn(lowerCamelCase__      ):
		if use_longest:
			return tokenizer.pad(lowerCamelCase__     ,			padding='longest'     ,			return_tensors='pt'      )
		return tokenizer.pad(lowerCamelCase__     ,			padding='max_length'     ,			max_length=1_2_8     ,			return_tensors='pt'      )

	return DataLoader(lowerCamelCase__     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=1_6      )






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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






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

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			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        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			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]:
			__lowerCamelCase    :    Optional[Any] 	=			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__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()




