


'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__snake_case											=os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
		from run_translation import main  # noqa


set_seed(42)
__snake_case											='''sshleifer/student_marian_en_ro_6_1'''
__snake_case											='''sshleifer/tiny-mbart'''
@require_torch
class        lowercase     (							_a ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_=False   ,       UpperCamelCase_=None   ,       UpperCamelCase_=True   ,       UpperCamelCase_=True   ,       UpperCamelCase_=True   ,       UpperCamelCase_=True   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.run_trainer(
							    eval_steps=1   ,       max_len=12   ,       model_name=snake_case_   ,       num_train_epochs=1   ,       distributed=snake_case_   ,       extra_args_str=snake_case_   ,       predict_with_generate=snake_case_   ,       do_train=snake_case_   ,       do_eval=snake_case_   ,       do_predict=snake_case_   ,       )
							UpperCamelCase__       :int							 =			TrainerState.load_from_json(os.path.join(snake_case_   ,       '''trainer_state.json'''							)							).log_history

							if not do_eval:
												return

							UpperCamelCase__       :Tuple							 =			[log for log in logs if """eval_loss""" in log.keys()]

							UpperCamelCase__       :List[Any]							 =			eval_metrics[0]
							if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												UpperCamelCase__       :Dict							 =			eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']   ,       snake_case_							)
												assert not math.isnan(float(last_step_stats['''eval_loss''']							)							), "eval_loss must not be `nan`"




		@require_torch_non_multi_gpu
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick()




		@require_torch_multi_gpu
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=snake_case_							)




		@require_torch_multi_gpu
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=snake_case_							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=snake_case_   ,       extra_args_str='''--sharded_ddp simple'''							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=snake_case_   ,       extra_args_str='''--sharded_ddp simple --fp16'''							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=snake_case_   ,       extra_args_str='''--sharded_ddp zero_dp_2'''   ,       predict_with_generate=snake_case_							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(
							    distributed=snake_case_   ,       extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''   ,       predict_with_generate=snake_case_							)




		@require_apex
		@require_torch_gpu
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=snake_case_   ,       extra_args_str='''--fp16 --fp16_backend=apex'''							)
							# test 2nd time - was getting eval_loss': nan'
							# to reproduce the problem set distributed=False
							self.run_seqaseq_quick(distributed=snake_case_   ,       extra_args_str='''--fp16 --fp16_backend=apex'''							)




		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']							)
		@require_torch_multi_gpu
		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			{
							    # test with the default log_level - should be info and thus log info once
							    """base""": {"""extra_args_str""": """""", """n_matches""": 1},
							    # test with low log_level and log_level_replica - should be noisy on all processes
							    # now the info string should appear twice on 2 processes
							    """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
							    # test with high log_level and low log_level_replica
							    # now the info string should appear once only on the replica
							    """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
							    # test with high log_level and log_level_replica - should be quiet on all processes
							    """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
							}

							UpperCamelCase__       :Optional[int]							 =			experiments[experiment_id]
							UpperCamelCase__       :Optional[int]							 =			{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
							UpperCamelCase__       :Union[str, Any]							 =			"""Running training"""
							with CaptureStderr() as cl:
												self.run_seqaseq_quick(**snake_case_   ,       extra_args_str=data['''extra_args_str''']							)
							UpperCamelCase__       :str							 =			len(re.findall(snake_case_   ,       cl.err							)							)
							self.assertEqual(snake_case_   ,       data['''n_matches''']							)




		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.run_trainer(
							    eval_steps=2   ,       max_len=128   ,       model_name=snake_case_   ,       learning_rate=3e-4   ,       num_train_epochs=10   ,       distributed=snake_case_   ,       )

							# Check metrics
							UpperCamelCase__       :Dict							 =			TrainerState.load_from_json(os.path.join(snake_case_   ,       '''trainer_state.json'''							)							).log_history
							UpperCamelCase__       :List[str]							 =			[log for log in logs if """eval_loss""" in log.keys()]
							UpperCamelCase__       :List[str]							 =			eval_metrics[0]
							UpperCamelCase__       :Any							 =			eval_metrics[-1]

							assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
							assert isinstance(last_step_stats['''eval_bleu''']   ,       snake_case_							)

							# test if do_predict saves generations and metrics
							UpperCamelCase__       :Optional[int]							 =			os.listdir(snake_case_							)
							UpperCamelCase__       :List[str]							 =			{os.path.basename(snake_case_							) for p in contents}
							assert "generated_predictions.txt" in contents
							assert "predict_results.json" in contents




		@slow
		@require_bitsandbytes
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							from transformers.training_args import OptimizerNames

							def train_and_return_metrics(UpperCamelCase_							) -> Tuple[int, float]:
												UpperCamelCase__       :Dict							 =			"""--skip_memory_metrics 0"""

												UpperCamelCase__       :Optional[int]							 =			self.run_trainer(
												    max_len=128   ,       model_name=snake_case_   ,       learning_rate=3e-4   ,       num_train_epochs=1   ,       optim=snake_case_   ,       distributed=snake_case_   ,       extra_args_str=snake_case_   ,       do_eval=snake_case_   ,       do_predict=snake_case_   ,       n_gpus_to_use=1   ,       )

												# Check metrics
												UpperCamelCase__       :Optional[Any]							 =			TrainerState.load_from_json(Path(snake_case_   ,       '''trainer_state.json'''							)							).log_history
												UpperCamelCase__       :Optional[int]							 =			int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20							)
												UpperCamelCase__       :Tuple							 =			int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20							)

												UpperCamelCase__       :Optional[int]							 =			logs[0]["""train_loss"""]
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

							UpperCamelCase__       :List[Any]							 =			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
							UpperCamelCase__       :List[Any]							 =			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

							UpperCamelCase__       :Dict							 =			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							UpperCamelCase__       :Optional[Any]							 =			gpu_peak_mem_orig + gpu_alloc_mem_orig
							UpperCamelCase__       :Dict							 =			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							UpperCamelCase__       :Tuple							 =			gpu_total_mem_orig - gpu_total_mem_bnb

							# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
							# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
							# in 2 bytes and the diff in optim memory usage is derived as so:
							#
							# - normal 25*8=~200MB (8 bytes per param)
							# - bnb    25*2= ~50MB (2 bytes per param)
							#
							# Thus we should expect ~150MB total memory saved.
							#
							# Peak memory should be the same - the total should be different by about that same margin
							#
							# After leaving a small margin to accommodate for differences between gpus let's check
							# that we have at least 120MB in savings
							UpperCamelCase__       :int							 =			120

							# uncomment the following if this test starts failing - requires py38 for a new print feature
							# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
							# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
							# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
							# print(f"{gpu_alloc_mem_diff=}MB")
							# print(f"{gpu_peak_mem_diff=}MB")
							# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
							# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

							self.assertGreater(
							    snake_case_   ,       snake_case_   ,       '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
							    F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
							    F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''   ,       )

							self.assertGreater(
							    snake_case_   ,       snake_case_   ,       '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
							    F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
							    F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''   ,       )

							self.assertEqual(
							    snake_case_   ,       snake_case_   ,       F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = 3e-3   ,       UpperCamelCase_ = "adafactor"   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = 0   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = None   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
							UpperCamelCase__       :Union[str, Any]							 =			self.get_auto_remove_tmp_dir()
							UpperCamelCase__       :List[Any]							 =			F'''\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(snake_case_							)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(snake_case_							)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '''.split()

							UpperCamelCase__       :List[Any]							 =			F'''\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(snake_case_							)}\n        '''.split()

							UpperCamelCase__       :Dict							 =			"""
            --do_predict
        """.split()

							UpperCamelCase__       :List[Any]							 =			[]
							if do_train:
												args += args_train

							if do_eval:
												args += args_eval

							if do_predict:
												args += args_predict

							if predict_with_generate:
												args += "--predict_with_generate".split()

							if do_train:
												if optim == "adafactor":
																	args += "--adafactor".split()
												else:
																	args += F'''--optim {optim}'''.split()

							if extra_args_str is not None:
												args += extra_args_str.split()

							if distributed:
												if n_gpus_to_use is None:
																	UpperCamelCase__       :Any							 =			get_gpu_count()
												UpperCamelCase__       :Optional[int]							 =			get_torch_dist_unique_port()
												UpperCamelCase__       :List[str]							 =			F'''\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '''.split()
												UpperCamelCase__       :int							 =			[sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(snake_case_   ,       env=self.get_env()							)
							else:
												UpperCamelCase__       :str							 =			["""run_translation.py"""] + args
												with patch.object(snake_case_   ,       '''argv'''   ,       snake_case_							):
																	main()

							return output_dir



'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class        lowercase     (							A__     ,		A__     ,		A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    AltDiffusionPipeline
		_a   			=    TEXT_TO_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_BATCH_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			UNetaDConditionModel(
							    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,       cross_attention_dim=32   ,       )
							UpperCamelCase__       :int							 =			DDIMScheduler(
							    beta_start=0.00085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=UpperCamelCase_   ,       set_alpha_to_one=UpperCamelCase_   ,       )
							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			AutoencoderKL(
							    block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,       latent_channels=4   ,       )

							# TODO: address the non-deterministic text encoder (fails for save-load tests)
							# torch.manual_seed(0)
							# text_encoder_config = RobertaSeriesConfig(
							#     hidden_size=32,
							#     project_dim=32,
							#     intermediate_size=37,
							#     layer_norm_eps=1e-05,
							#     num_attention_heads=4,
							#     num_hidden_layers=5,
							#     vocab_size=5002,
							# )
							# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

							torch.manual_seed(0							)
							UpperCamelCase__       :Union[str, Any]							 =			CLIPTextConfig(
							    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=5002   ,       )
							UpperCamelCase__       :Optional[Any]							 =			CLIPTextModel(UpperCamelCase_							)

							UpperCamelCase__       :int							 =			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
							UpperCamelCase__       :Dict							 =			77

							UpperCamelCase__       :Dict							 =			{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :Dict							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :List[str]							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :Tuple							 =			self.get_dummy_components()
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :Union[str, Any]							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder

							UpperCamelCase__       :Any							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			'''A photo of an astronaut'''
							UpperCamelCase__       :Union[str, Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			output.images
							UpperCamelCase__       :List[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :int							 =			self.get_dummy_components()
							UpperCamelCase__       :Union[str, Any]							 =			PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :str							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder
							UpperCamelCase__       :Union[str, Any]							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :str							 =			output.images
							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]							)

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



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

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :int							 =			torch.manual_seed(0							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''							)

							UpperCamelCase__       :Union[str, Any]							 =			output.images

							UpperCamelCase__       :Optional[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :Union[str, Any]							 =			np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''							)

							UpperCamelCase__       :List[str]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=UpperCamelCase_   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :str							 =			torch.manual_seed(0							)

							UpperCamelCase__       :Optional[int]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''							)
							UpperCamelCase__       :Any							 =			output.images

							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :int							 =			np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]							)

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