
"""simple docstring"""







import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase__   					=		getLogger(__name__)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = 8       ,  _UpperCamelCase = 1024       ,  _UpperCamelCase="val"       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False       ,  _UpperCamelCase="summarization"       ,  _UpperCamelCase=None       ,  _UpperCamelCase=1       ,  _UpperCamelCase = None       ,  _UpperCamelCase=""       ,  **_UpperCamelCase       ,  ):
	__lowerCAmelCase			:						List[Any]									=      str(_UpperCamelCase		)
	assert local_rank is not None
	torch.distributed.init_process_group(backend='nccl'       ,  rank=_UpperCamelCase		)

	__lowerCAmelCase			:						List[str]									=      Path(_UpperCamelCase		)
	__lowerCAmelCase			:						Any									=      save_dir.joinpath(F"rank_{local_rank}_output.json"		)
	torch.cuda.set_device(_UpperCamelCase		)
	__lowerCAmelCase			:						Any									=      AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).cuda()
	if fpaa:
		__lowerCAmelCase			:						Union[str, Any]									=      model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(_UpperCamelCase       ,  _UpperCamelCase		)  # update config with task specific params
	__lowerCAmelCase			:						str									=      generate_kwargs.pop('num_beams'       ,  model.config.num_beams		)  # AttributeError risk?
	if num_return_sequences > num_beams:
		__lowerCAmelCase			:						Optional[int]									=      num_return_sequences

	__lowerCAmelCase			:						Dict									=      AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	if max_source_length is None:
		__lowerCAmelCase			:						int									=      tokenizer.model_max_length
	if prefix is None:
		__lowerCAmelCase			:						Any									=      prefix or getattr(model.config       ,  'prefix'       ,  ''		) or ''
	__lowerCAmelCase			:						str									=      SeqaSeqDataset(
	    _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  max_target_length=1024       ,  type_path=_UpperCamelCase       ,  n_obs=_UpperCamelCase       ,  prefix=_UpperCamelCase       ,  **_UpperCamelCase       ,  )
	# I set shuffle=True for a more accurate progress bar.
	# If all the longest samples are first, the prog bar estimate is too high at the beginning.
	__lowerCAmelCase			:						Tuple									=      ds.make_sortish_sampler(_UpperCamelCase       ,  distributed=_UpperCamelCase       ,  add_extra_examples=_UpperCamelCase       ,  shuffle=_UpperCamelCase		)
	__lowerCAmelCase			:						Dict									=      DataLoader(_UpperCamelCase       ,  sampler=_UpperCamelCase       ,  batch_size=_UpperCamelCase       ,  collate_fn=ds.collate_fn		)
	__lowerCAmelCase			:						Tuple									=      []
	for batch in tqdm(_UpperCamelCase		):
		__lowerCAmelCase			:						List[Any]									=      model.generate(
		    input_ids=batch['input_ids'].to(model.device		)       ,  attention_mask=batch['attention_mask'].to(model.device		)       ,  num_return_sequences=_UpperCamelCase       ,  num_beams=_UpperCamelCase       ,  **_UpperCamelCase       ,  )
		__lowerCAmelCase			:						Union[str, Any]									=      tokenizer.batch_decode(_UpperCamelCase       ,  skip_special_tokens=_UpperCamelCase       ,  clean_up_tokenization_spaces=_UpperCamelCase		)
		__lowerCAmelCase			:						Any									=      batch['ids']
		if num_return_sequences > 1:
			__lowerCAmelCase			:						List[Any]									=      chunks(_UpperCamelCase       ,  _UpperCamelCase		)  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_UpperCamelCase		):
			results.append({'pred': pred, 'id': ids[i].item()}		)
	save_json(_UpperCamelCase       ,  _UpperCamelCase		)
	return results, sampler.num_replicas
def     __lowerCAmelCase				():
	__lowerCAmelCase			:						str									=      argparse.ArgumentParser(
	    epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'		)
	parser.add_argument('--data_dir'       ,  type=_UpperCamelCase       ,  help='like cnn_dm/test.source'		)
	parser.add_argument(
	    '--model_name'       ,  type=_UpperCamelCase       ,  help='like facebook/bart-large-cnn,t5-base, etc.'       ,  default='sshleifer/distilbart-xsum-12-3'       ,  )
	parser.add_argument('--save_dir'       ,  type=_UpperCamelCase       ,  help='where to save'       ,  default='tmp_gen'		)
	parser.add_argument('--max_source_length'       ,  type=_UpperCamelCase       ,  default=_UpperCamelCase		)
	parser.add_argument(
	    '--type_path'       ,  type=_UpperCamelCase       ,  default='test'       ,  help='which subset to evaluate typically train/val/test'		)
	parser.add_argument('--task'       ,  type=_UpperCamelCase       ,  default='summarization'       ,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'       ,  type=_UpperCamelCase       ,  default=8       ,  required=_UpperCamelCase       ,  help='batch size'		)
	parser.add_argument(
	    '--local_rank'       ,  type=_UpperCamelCase       ,  default=-1       ,  required=_UpperCamelCase       ,  help='should be passed by distributed.launch'		)

	parser.add_argument(
	    '--n_obs'       ,  type=_UpperCamelCase       ,  default=_UpperCamelCase       ,  required=_UpperCamelCase       ,  help='How many observations. Defaults to all.'		)
	parser.add_argument(
	    '--num_return_sequences'       ,  type=_UpperCamelCase       ,  default=1       ,  required=_UpperCamelCase       ,  help='How many sequences to return'		)
	parser.add_argument(
	    '--sync_timeout'       ,  type=_UpperCamelCase       ,  default=600       ,  required=_UpperCamelCase       ,  help='How long should master process wait for other processes to finish.'       ,  )
	parser.add_argument('--src_lang'       ,  type=_UpperCamelCase       ,  default=_UpperCamelCase       ,  required=_UpperCamelCase		)
	parser.add_argument('--tgt_lang'       ,  type=_UpperCamelCase       ,  default=_UpperCamelCase       ,  required=_UpperCamelCase		)
	parser.add_argument(
	    '--prefix'       ,  type=_UpperCamelCase       ,  required=_UpperCamelCase       ,  default=_UpperCamelCase       ,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--fp16'       ,  action='store_true'		)
	parser.add_argument('--debug'       ,  action='store_true'		)
	__lowerCAmelCase			:						Optional[int]									=      time.time()
	__lowerCAmelCase			,   __lowerCAmelCase			:						str									=      parser.parse_known_args()
	__lowerCAmelCase			:						Any									=      parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if generate_kwargs and args.local_rank <= 0:
		print(F"parsed the following generate kwargs: {generate_kwargs}"		)
	__lowerCAmelCase			:						List[Any]									=      Path(args.save_dir + '_tmp'		)
	Path(_UpperCamelCase		).mkdir(exist_ok=_UpperCamelCase		)  # this handles locking.
	__lowerCAmelCase			:						List[str]									=      list(json_save_dir.glob('rank_*.json'		)		)
	if intermediate_files:
		raise ValueError(F"Found files at {json_save_dir} please move or remove them."		)
		# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
	__lowerCAmelCase			:						Optional[int]									=      {}
	if args.src_lang is not None:
		__lowerCAmelCase			:						Tuple									=      args.src_lang
	if args.tgt_lang is not None:
		__lowerCAmelCase			:						Tuple									=      args.tgt_lang

	Path(args.save_dir		).mkdir(exist_ok=_UpperCamelCase		)
	__lowerCAmelCase			,   __lowerCAmelCase			:						str									=      eval_data_dir(
	    args.data_dir       ,  _UpperCamelCase       ,  args.model_name       ,  type_path=args.type_path       ,  bs=args.bs       ,  fpaa=args.fpaa       ,  task=args.task       ,  local_rank=args.local_rank       ,  n_obs=args.n_obs       ,  max_source_length=args.max_source_length       ,  num_return_sequences=args.num_return_sequences       ,  prefix=args.prefix       ,  dataset_kwargs=_UpperCamelCase       ,  **_UpperCamelCase       ,  )

	if args.local_rank <= 0:
		__lowerCAmelCase			:						Optional[Any]									=      Path(args.save_dir		)
		save_dir.mkdir(exist_ok=_UpperCamelCase		)
		__lowerCAmelCase			:						List[str]									=      gather_results_from_each_node(_UpperCamelCase       ,  _UpperCamelCase       ,  args.sync_timeout		)
		__lowerCAmelCase			:						Optional[Any]									=      combine_partial_results(_UpperCamelCase		)
		if args.num_return_sequences > 1:
			__lowerCAmelCase			:						int									=      save_dir.joinpath('pseudolabel_results.json'		)
			print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"		)
			save_json(_UpperCamelCase       ,  _UpperCamelCase		)
			return
		__lowerCAmelCase			:						Optional[int]									=      Path(args.data_dir		).joinpath(args.type_path + '.target'		)
		with open(_UpperCamelCase		) as f:
			__lowerCAmelCase			:						Union[str, Any]									=      [x.rstrip() for x in f.readlines()][: len(_UpperCamelCase		)]

		# Calculate metrics, save metrics,  and save _generations.txt
		__lowerCAmelCase			:						Optional[Any]									=      'translation' in args.task
		__lowerCAmelCase			:						Any									=      calculate_bleu if calc_bleu else calculate_rouge
		__lowerCAmelCase			:						Any									=      'bleu' if calc_bleu else 'rouge'
		__lowerCAmelCase			:						Dict									=      score_fn(_UpperCamelCase       ,  _UpperCamelCase		)
		__lowerCAmelCase			:						int									=      len(_UpperCamelCase		)
		__lowerCAmelCase			:						Tuple									=      time.time() - start_time
		__lowerCAmelCase			:						Tuple									=      round(runtime / metrics['n_obs']       ,  4		)
		__lowerCAmelCase			:						Optional[Any]									=      num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		__lowerCAmelCase			:						List[str]									=      save_dir.joinpath(F"{args.type_path}_{metric_name}.json"		)
		save_json(_UpperCamelCase       ,  _UpperCamelCase       ,  indent=_UpperCamelCase		)
		print(_UpperCamelCase		)
		write_txt_file(_UpperCamelCase       ,  save_dir.joinpath(F"{args.type_path}_generations.txt"		)		)
		if args.debug:
			write_txt_file(_UpperCamelCase       ,  save_dir.joinpath(F"{args.type_path}.target"		)		)
		else:
			shutil.rmtree(_UpperCamelCase		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[int]									=      []
	for partial_result in partial_results:
		records.extend(_UpperCamelCase		)
	__lowerCAmelCase			:						List[Any]									=      sorted(_UpperCamelCase       ,  key=lambda _UpperCamelCase		: x["id"]		)
	__lowerCAmelCase			:						Optional[int]									=      [x['pred'] for x in records]
	return preds
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	# WAIT FOR lots of .json files
	__lowerCAmelCase			:						Union[str, Any]									=      time.time()
	logger.info('waiting for all nodes to finish'		)
	__lowerCAmelCase			:						Tuple									=      None
	while (time.time() - start_wait) < timeout:
		__lowerCAmelCase			:						Dict									=      list(save_dir.glob('rank_*.json'		)		)
		if len(_UpperCamelCase		) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			__lowerCAmelCase			:						List[str]									=      lmap(_UpperCamelCase       ,  _UpperCamelCase		)
			return json_data
		except JSONDecodeError:
			continue
	else:
		raise TimeoutError('Rank 0 gave up on waiting for other processes'		)
	# Unreachable


if __name__ == "__main__":
							# Usage for MT:
							run_generate()

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class 			__A( a  ,   a  ,   unittest.TestCase			):
				snake_case_						   =    AutoencoderKL
				snake_case_						   =    '''sample'''
				snake_case_						   =    1E-2




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    4
								__a										=    3
								__a										=    (32, 32)

								__a										=    floats_tensor((batch_size, num_channels) + sizes				).to(_snake_case				)

								return {"sample": image}




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								return (3, 32, 32)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								return (3, 32, 32)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    {
								    '''block_out_channels''': [32, 64],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								__a										=    self.dummy_input
								return init_dict, inputs_dict




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								pass




				@unittest.skipIf(torch_device == '''mps'''		,     '''Gradient checkpointing skipped on MPS'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a							,     __a										=    self.prepare_init_args_and_inputs_for_common()
								__a										=    self.model_class(**_snake_case				)
								model.to(_snake_case				)

								assert not model.is_gradient_checkpointing and model.training

								__a										=    model(**_snake_case				).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model.zero_grad()

								__a										=    torch.randn_like(_snake_case				)
								__a										=    (out - labels).mean()
								loss.backward()

								# re-instantiate the model now enabling gradient checkpointing
								__a										=    self.model_class(**_snake_case				)
								# clone model
								model_a.load_state_dict(model.state_dict()				)
								model_a.to(_snake_case				)
								model_a.enable_gradient_checkpointing()

								assert model_a.is_gradient_checkpointing and model_a.training

								__a										=    model_a(**_snake_case				).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model_a.zero_grad()
								__a										=    (out_a - labels).mean()
								loss_a.backward()

								# compare the output and parameters gradients
								self.assertTrue((loss - loss_a).abs() < 1E-5				)
								__a										=    dict(model.named_parameters()				)
								__a										=    dict(model_a.named_parameters()				)
								for name, param in named_params.items():
												self.assertTrue(torch_all_close(param.grad.data		,     named_params_a[name].grad.data		,     atol=5E-5				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a							,     __a										=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''		,     output_loading_info=_snake_case				)
								self.assertIsNotNone(_snake_case				)
								self.assertEqual(len(loading_info['''missing_keys''']				)		,     0				)

								model.to(_snake_case				)
								__a										=    model(**self.dummy_input				)

								assert image is not None, "Make sure output is not None"




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
								__a										=    model.to(_snake_case				)
								model.eval()

								if torch_device == "mps":
												__a										=    torch.manual_seed(0				)
								else:
												__a										=    torch.Generator(device=_snake_case				).manual_seed(0				)

								__a										=    torch.randn(
								    1		,     model.config.in_channels		,     model.config.sample_size		,     model.config.sample_size		,     generator=torch.manual_seed(0				)		,     )
								__a										=    image.to(_snake_case				)
								with torch.no_grad():
												__a										=    model(_snake_case		,     sample_posterior=_snake_case		,     generator=_snake_case				).sample

								__a										=    output[0, -1, -3:, -3:].flatten().cpu()

								# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
								# the expected output slices are not the same for CPU and GPU.
								if torch_device == "mps":
												__a										=    torch.tensor(
												    [
												        -4.0_078E-01,
												        -3.8_323E-04,
												        -1.2_681E-01,
												        -1.1_462E-01,
												        2.0_095E-01,
												        1.0_893E-01,
												        -8.8_247E-02,
												        -3.0_361E-01,
												        -9.8_644E-03,
												    ]				)
								elif torch_device == "cpu":
												__a										=    torch.tensor(
												    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
								else:
												__a										=    torch.tensor(
												    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]				)

								self.assertTrue(torch_all_close(_snake_case		,     _snake_case		,     rtol=1E-2				)				)







@slow
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								return F"""gaussian_noise_s={seed}_shape={'_'.join([str(_snake_case				) for s in shape]				)}.npy"""




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0		,     _snake_case=(4, 3, 512, 512)		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




								__a										=    torch.floataa if fpaa else torch.floataa
								__a										=    torch.from_numpy(load_hf_numpy(self.get_file_format(_snake_case		,     _snake_case				)				)				).to(_snake_case				).to(_snake_case				)
								return image




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case="CompVis/stable-diffusion-v1-4"		,     _snake_case=False				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    '''fp16''' if fpaa else None
								__a										=    torch.floataa if fpaa else torch.floataa

								__a										=    AutoencoderKL.from_pretrained(
								    _snake_case		,     subfolder='''vae'''		,     torch_dtype=_snake_case		,     revision=_snake_case		,     )
								model.to(_snake_case				).eval()

								return model




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0				)    ->      Tuple:


								'''simple docstring'''




								if torch_device == "mps":
												return torch.manual_seed(_snake_case				)
								return torch.Generator(device=_snake_case				).manual_seed(_snake_case				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case		,     generator=_snake_case		,     sample_posterior=_snake_case				).sample

								assert sample.shape == image.shape

								__a										=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
								__a										=    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=3E-3				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
				        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
				        # fmt: on
				    ]				)
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     fpaa=_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case		,     generator=_snake_case		,     sample_posterior=_snake_case				).sample

								assert sample.shape == image.shape

								__a										=    sample[-1, -2:, :2, -2:].flatten().float().cpu()
								__a										=    torch.tensor(_snake_case				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-2				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case				).sample

								assert sample.shape == image.shape

								__a										=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
								__a										=    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=3E-3				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
				        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
				        # fmt: on
				    ]				)
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								__a										=    sample[-1, -2:, :2, -2:].flatten().cpu()
								__a										=    torch.tensor(_snake_case				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-3				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
				        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
				        # fmt: on
				    ]				)
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=_snake_case				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								__a										=    sample[-1, -2:, :2, -2:].flatten().float().cpu()
								__a										=    torch.tensor(_snake_case				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=5E-3				)




				@parameterized.expand([(13,), (16,), (27,)]				)
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()		,     reason='''xformers is not required when using PyTorch 2.0.'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=_snake_case				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-1				)




				@parameterized.expand([(13,), (16,), (37,)]				)
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()		,     reason='''xformers is not required when using PyTorch 2.0.'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-2				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
				        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
				        # fmt: on
				    ]				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model.encode(_snake_case				).latent_dist
												__a										=    dist.sample(generator=_snake_case				)

								assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

								__a										=    sample[0, -1, -3:, -3:].flatten().cpu()
								__a										=    torch.tensor(_snake_case				)

								__a										=    3E-3 if torch_device != '''mps''' else 1E-2
								assert torch_all_close(_snake_case		,     _snake_case		,     atol=_snake_case				)