

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Optional[int]										=				ShapEImgaImgPipeline
							_UpperCAmelCase   :  List[str]										=				['''image''']
							_UpperCAmelCase   :  int										=				['''image''']
							_UpperCAmelCase   :  Tuple										=				[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							_UpperCAmelCase   :  Union[str, Any]										=				False
							@property
							def    lowerCAmelCase					(		self		:      List[Any]):
								return 3_2
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return 3_2
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return self.time_input_dim * 4
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return 8
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(0)
								__lowerCamelCase    :    List[str] 	=			CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size       ,image_size=6_4       ,projection_dim=self.text_embedder_hidden_size       ,intermediate_size=3_7       ,num_attention_heads=4       ,num_channels=3       ,num_hidden_layers=5       ,patch_size=1       ,)

								__lowerCamelCase    :    Optional[int] 	=			CLIPVisionModel(SCREAMING_SNAKE_CASE__)
								return model
							@property
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Dict 	=			CLIPImageProcessor(
								    crop_size=2_2_4       ,do_center_crop=SCREAMING_SNAKE_CASE__       ,do_normalize=SCREAMING_SNAKE_CASE__       ,do_resize=SCREAMING_SNAKE_CASE__       ,image_mean=[0.48145466, 0.4578275, 0.40821073]       ,image_std=[0.26862954, 0.26130258, 0.27577711]       ,resample=3       ,size=2_2_4       ,)

								return image_processor
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(0)

								__lowerCamelCase    :    Any 	=			{
								    'num_attention_heads': 2,
								    'attention_head_dim': 1_6,
								    'embedding_dim': self.time_input_dim,
								    'num_embeddings': 3_2,
								    'embedding_proj_dim': self.text_embedder_hidden_size,
								    'time_embed_dim': self.time_embed_dim,
								    'num_layers': 1,
								    'clip_embed_dim': self.time_input_dim * 2,
								    'additional_embeddings': 0,
								    'time_embed_act_fn': 'gelu',
								    'norm_in_type': 'layer',
								    'embedding_proj_norm_type': 'layer',
								    'encoder_hid_proj_type': None,
								    'added_emb_type': None,
								}

								__lowerCamelCase    :    List[str] 	=			PriorTransformer(**SCREAMING_SNAKE_CASE__)
								return model
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(0)

								__lowerCamelCase    :    Optional[int] 	=			{
								    'param_shapes': (
								        (self.renderer_dim, 9_3),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    'd_latent': self.time_input_dim,
								    'd_hidden': self.renderer_dim,
								    'n_output': 1_2,
								    'background': (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__lowerCamelCase    :    int 	=			ShapERenderer(**SCREAMING_SNAKE_CASE__)
								return model
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[int] 	=			self.dummy_prior
								__lowerCamelCase    :    Optional[int] 	=			self.dummy_image_encoder
								__lowerCamelCase    :    Optional[int] 	=			self.dummy_image_processor
								__lowerCamelCase    :    Optional[Any] 	=			self.dummy_renderer

								__lowerCamelCase    :    int 	=			HeunDiscreteScheduler(
								    beta_schedule='exp'       ,num_train_timesteps=1_0_2_4       ,prediction_type='sample'       ,use_karras_sigmas=SCREAMING_SNAKE_CASE__       ,clip_sample=SCREAMING_SNAKE_CASE__       ,clip_sample_range=1.0       ,)
								__lowerCamelCase    :    Tuple 	=			{
								    'prior': prior,
								    'image_encoder': image_encoder,
								    'image_processor': image_processor,
								    'renderer': renderer,
								    'scheduler': scheduler,
								}

								return components
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str=0):
								__lowerCamelCase    :    List[str] 	=			floats_tensor((1, 3, 6_4, 6_4)       ,rng=random.Random(SCREAMING_SNAKE_CASE__)).to(SCREAMING_SNAKE_CASE__)

								if str(SCREAMING_SNAKE_CASE__).startswith('mps'):
									__lowerCamelCase    :    List[str] 	=			torch.manual_seed(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase    :    Tuple 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			{
								    'image': input_image,
								    'generator': generator,
								    'num_inference_steps': 1,
								    'frame_size': 3_2,
								    'output_type': 'np',
								}
								return inputs
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Dict 	=			'cpu'

								__lowerCamelCase    :    str 	=			self.get_dummy_components()

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

								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__))
								__lowerCamelCase    :    Optional[Any] 	=			output.images[0]
								__lowerCamelCase    :    str 	=			image[0, -3:, -3:, -1]

								assert image.shape == (2_0, 3_2, 3_2, 3)

								__lowerCamelCase    :    Optional[Any] 	=			np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2])
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    int 	=			torch_device == 'cpu'
								__lowerCamelCase    :    Dict 	=			True
								self._test_inference_batch_single_identical(
								    batch_size=2       ,test_max_difference=SCREAMING_SNAKE_CASE__       ,relax_max_difference=SCREAMING_SNAKE_CASE__       ,)


							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    List[Any] 	=			self.get_dummy_components()
								__lowerCamelCase    :    Optional[Any] 	=			self.pipeline_class(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			1
								__lowerCamelCase    :    str 	=			2

								__lowerCamelCase    :    Optional[int] 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)

								for key in inputs.keys():
									if key in self.batch_params:
										__lowerCamelCase    :    List[Any] 	=			batch_size * [inputs[key]]

								__lowerCamelCase    :    int 	=			pipe(**SCREAMING_SNAKE_CASE__       ,num_images_per_prompt=SCREAMING_SNAKE_CASE__)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      int):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    List[Any] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
								__lowerCamelCase    :    Optional[int] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/shap_e/test_shap_e_img2img_out.npy')
								__lowerCamelCase    :    Optional[int] 	=			ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
								__lowerCamelCase    :    Any 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(0)

								__lowerCamelCase    :    Tuple 	=			pipe(
								    SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,guidance_scale=3.0       ,num_inference_steps=6_4       ,frame_size=6_4       ,output_type='np'       ,).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)







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()




