






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





       @property
       def      _a       (   self				):
         UpperCamelCase_:   str        =							1
         UpperCamelCase_:   int        =							3
         UpperCamelCase_:   List[Any]        =							(3_2, 3_2)

         UpperCamelCase_:   List[Any]        =							floats_tensor((batch_size, num_channels) + sizes    ,     rng=random.Random(0				)				).to(_lowerCamelCase				)
         return image





       @property
       def      _a       (   self				):
         torch.manual_seed(0				)
         UpperCamelCase_:   Dict        =							UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)    ,     layers_per_block=2    ,     sample_size=3_2    ,     in_channels=4    ,     out_channels=4    ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,     cross_attention_dim=3_2    ,     )
         return model





       @property
       def      _a       (   self				):
         torch.manual_seed(0				)
         UpperCamelCase_:   Optional[int]        =							AutoencoderKL(
             block_out_channels=[3_2, 6_4]    ,     in_channels=3    ,     out_channels=3    ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,     latent_channels=4    ,     )
         return model





       @property
       def      _a       (   self				):
         torch.manual_seed(0				)
         UpperCamelCase_:   Dict        =							RobertaSeriesConfig(
             hidden_size=3_2    ,     project_dim=3_2    ,     intermediate_size=3_7    ,     layer_norm_eps=1e-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=5_0_0_6    ,     )
         return RobertaSeriesModelWithTransformation(_lowerCamelCase				)





       @property
       def      _a       (   self				):
         def extract(*_lowerCamelCase    ,     **_lowerCamelCase				):




           class 							_lowerCAmelCase:





                  """simple docstring"""







                  def __init__(   self				):
                    UpperCamelCase_:   Union[str, Any]        =							torch.ones([0]				)





                  def      _a       (   self    ,     _lowerCamelCase				):
                    self.pixel_values.to(_lowerCamelCase				)
                    return self







           return Out()

         return extract





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							'cpu'  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase_:   Optional[int]        =							self.dummy_cond_unet
         UpperCamelCase_:   List[str]        =							PNDMScheduler(skip_prk_steps=_lowerCamelCase				)
         UpperCamelCase_:   str        =							self.dummy_vae
         UpperCamelCase_:   Tuple        =							self.dummy_text_encoder
         UpperCamelCase_:   Optional[Any]        =							XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'				)
         UpperCamelCase_:   int        =							7_7

         UpperCamelCase_:   str        =							self.dummy_image.to(_lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							init_image / 2 + 0.5

         # make sure here that pndm scheduler skips prk
         UpperCamelCase_:   Dict        =							AltDiffusionImgaImgPipeline(
             unet=_lowerCamelCase    ,     scheduler=_lowerCamelCase    ,     vae=_lowerCamelCase    ,     text_encoder=_lowerCamelCase    ,     tokenizer=_lowerCamelCase    ,     safety_checker=_lowerCamelCase    ,     feature_extractor=self.dummy_extractor    ,     )
         UpperCamelCase_:   List[Any]        =							VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,     do_normalize=_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							alt_pipe.to(_lowerCamelCase				)
         alt_pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							'A painting of a squirrel eating a burger'
         UpperCamelCase_:   Dict        =							torch.Generator(device=_lowerCamelCase				).manual_seed(0				)
         UpperCamelCase_:   List[str]        =							alt_pipe(
             [prompt]    ,     generator=_lowerCamelCase    ,     guidance_scale=6.0    ,     num_inference_steps=2    ,     output_type='np'    ,     image=_lowerCamelCase    ,     )

         UpperCamelCase_:   Dict        =							output.images

         UpperCamelCase_:   Any        =							torch.Generator(device=_lowerCamelCase				).manual_seed(0				)
         UpperCamelCase_:   Tuple        =							alt_pipe(
             [prompt]    ,     generator=_lowerCamelCase    ,     guidance_scale=6.0    ,     num_inference_steps=2    ,     output_type='np'    ,     image=_lowerCamelCase    ,     return_dict=_lowerCamelCase    ,     )[0]

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

         assert image.shape == (1, 3_2, 3_2, 3)
         UpperCamelCase_:   int        =							np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 5e-3
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 5e-3





       @unittest.skipIf(torch_device != 'cuda'    ,     'This test requires a GPU'				)
       def      _a       (   self				):
         UpperCamelCase_:   Any        =							self.dummy_cond_unet
         UpperCamelCase_:   List[Any]        =							PNDMScheduler(skip_prk_steps=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							self.dummy_vae
         UpperCamelCase_:   Tuple        =							self.dummy_text_encoder
         UpperCamelCase_:   str        =							XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'				)
         UpperCamelCase_:   Any        =							7_7

         UpperCamelCase_:   Optional[int]        =							self.dummy_image.to(_lowerCamelCase				)

         # put models in fp16
         UpperCamelCase_:   Dict        =							unet.half()
         UpperCamelCase_:   Union[str, Any]        =							vae.half()
         UpperCamelCase_:   int        =							bert.half()

         # make sure here that pndm scheduler skips prk
         UpperCamelCase_:   str        =							AltDiffusionImgaImgPipeline(
             unet=_lowerCamelCase    ,     scheduler=_lowerCamelCase    ,     vae=_lowerCamelCase    ,     text_encoder=_lowerCamelCase    ,     tokenizer=_lowerCamelCase    ,     safety_checker=_lowerCamelCase    ,     feature_extractor=self.dummy_extractor    ,     )
         UpperCamelCase_:   Dict        =							VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,     do_normalize=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							alt_pipe.to(_lowerCamelCase				)
         alt_pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   Any        =							'A painting of a squirrel eating a burger'
         UpperCamelCase_:   Dict        =							torch.manual_seed(0				)
         UpperCamelCase_:   Any        =							alt_pipe(
             [prompt]    ,     generator=_lowerCamelCase    ,     num_inference_steps=2    ,     output_type='np'    ,     image=_lowerCamelCase    ,     ).images

         assert image.shape == (1, 3_2, 3_2, 3)





       @unittest.skipIf(torch_device != 'cuda'    ,     'This test requires a GPU'				)
       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'				)
         # resize to resolution that is divisible by 8 but not 16 or 32
         UpperCamelCase_:   Union[str, Any]        =							init_image.resize((7_6_0, 5_0_4)				)

         UpperCamelCase_:   Optional[Any]        =							'BAAI/AltDiffusion'
         UpperCamelCase_:   int        =							AltDiffusionImgaImgPipeline.from_pretrained(
             _lowerCamelCase    ,     safety_checker=_lowerCamelCase    ,     )
         pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   Union[str, Any]        =							'A fantasy landscape, trending on artstation'

         UpperCamelCase_:   List[str]        =							torch.manual_seed(0				)
         UpperCamelCase_:   str        =							pipe(
             prompt=_lowerCamelCase    ,     image=_lowerCamelCase    ,     strength=0.7_5    ,     guidance_scale=7.5    ,     generator=_lowerCamelCase    ,     output_type='np'    ,     )
         UpperCamelCase_:   int        =							output.images[0]

         UpperCamelCase_:   Tuple        =							image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert image.shape == (5_0_4, 7_6_0, 3)
         UpperCamelCase_:   List[Any]        =							np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]				)

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







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





       """simple docstring"""







       def      _a       (   self				):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'				)
         UpperCamelCase_:   int        =							init_image.resize((7_6_8, 5_1_2)				)
         UpperCamelCase_:   Dict        =							load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'				)

         UpperCamelCase_:   List[str]        =							'BAAI/AltDiffusion'
         UpperCamelCase_:   str        =							AltDiffusionImgaImgPipeline.from_pretrained(
             _lowerCamelCase    ,     safety_checker=_lowerCamelCase    ,     )
         pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   List[Any]        =							'A fantasy landscape, trending on artstation'

         UpperCamelCase_:   Tuple        =							torch.manual_seed(0				)
         UpperCamelCase_:   Any        =							pipe(
             prompt=_lowerCamelCase    ,     image=_lowerCamelCase    ,     strength=0.7_5    ,     guidance_scale=7.5    ,     generator=_lowerCamelCase    ,     output_type='np'    ,     )
         UpperCamelCase_:   Tuple        =							output.images[0]

         assert image.shape == (5_1_2, 7_6_8, 3)
         # img2img is flaky across GPUs even in fp32, so using MAE here
         assert np.abs(expected_image - image				).max() < 1e-2



"""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()

