



'''simple docstring'''



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    jnp.floataa
				lowerCAmelCase__					    =    True

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											super().setup()
											__lowercase       	=nn.Dense(5  ,       dtype=self.dtype)

				def __call__(							self  :  Tuple  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											__lowercase       	=super().__call__(*_lowerCAmelCase  ,       **_lowerCAmelCase)
											__lowercase       	=self.cls(outputs[2])
											return outputs[:2] + (cls_out,)





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    FlaxBigBirdForNaturalQuestionsModule



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							def cross_entropy(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=None					):
														__lowercase       	=logits.shape[-1]
														__lowercase       	=(labels[..., None] == jnp.arange(_lowerCAmelCase					)[None]).astype('f4'					)
														__lowercase       	=jax.nn.log_softmax(_lowerCAmelCase  ,   axis=-1					)
														__lowercase       	=-jnp.sum(labels * logits  ,   axis=-1					)
														if reduction is not None:
																					__lowercase       	=reduction(_lowerCAmelCase					)
														return loss

							__lowercase       	=partial(_lowerCAmelCase  ,   reduction=jnp.mean					)
							__lowercase       	=cross_entropy(_lowerCAmelCase  ,   _lowerCAmelCase					)
							__lowercase       	=cross_entropy(_lowerCAmelCase  ,   _lowerCAmelCase					)
							__lowercase       	=cross_entropy(_lowerCAmelCase  ,   _lowerCAmelCase					)
							return (start_loss + end_loss + pooled_loss) / 3





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    "google/bigbird-roberta-base"
				lowerCAmelCase__					    =    30_00
				lowerCAmelCase__					    =    1_05_00

				lowerCAmelCase__					    =    1_28
				lowerCAmelCase__					    =    3

				lowerCAmelCase__					    =    1
				lowerCAmelCase__					    =    5

				# tx_args
				lowerCAmelCase__					    =    3E-5
				lowerCAmelCase__					    =    0.0
				lowerCAmelCase__					    =    2_00_00
				lowerCAmelCase__					    =    0.00_95

				lowerCAmelCase__					    =    "bigbird-roberta-natural-questions"
				lowerCAmelCase__					    =    "training-expt"
				lowerCAmelCase__					    =    "data/nq-training.jsonl"
				lowerCAmelCase__					    =    "data/nq-validation.jsonl"

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											os.makedirs(self.base_dir  ,       exist_ok=_lowerCAmelCase)
											__lowercase       	=os.path.join(self.base_dir  ,       self.save_dir)
											__lowercase       	=self.batch_size_per_device * jax.device_count()





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    40_96  # no dynamic padding on TPUs

				def __call__(							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  int):







											'''simple docstring'''

											__lowercase       	=self.collate_fn(_lowerCAmelCase)
											__lowercase       	=jax.tree_util.tree_map(_lowerCAmelCase  ,       _lowerCAmelCase)
											return batch

				def 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  int):







											'''simple docstring'''

											__lowercase	,				__lowercase       	=self.fetch_inputs(features['input_ids'])
											__lowercase       	={
											    'input_ids': jnp.array(_lowerCAmelCase  ,       dtype=jnp.intaa),
											    'attention_mask': jnp.array(_lowerCAmelCase  ,       dtype=jnp.intaa),
											    'start_labels': jnp.array(features['start_token']  ,       dtype=jnp.intaa),
											    'end_labels': jnp.array(features['end_token']  ,       dtype=jnp.intaa),
											    'pooled_labels': jnp.array(features['category']  ,       dtype=jnp.intaa),
											}
											return batch

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  list):







											'''simple docstring'''

											__lowercase       	=[self._fetch_inputs(_lowerCAmelCase) for ids in input_ids]
											return zip(*_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  list):







											'''simple docstring'''

											__lowercase       	=[1 for _ in range(len(_lowerCAmelCase))]
											while len(_lowerCAmelCase) < self.max_length:
																		input_ids.append(self.pad_id)
																		attention_mask.append(0)
											return input_ids, attention_mask



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=None					):



							"""simple docstring"""



							if seed is not None:
														__lowercase       	=dataset.shuffle(seed=_lowerCAmelCase					)
							for i in range(len(_lowerCAmelCase					) // batch_size					):
														__lowercase       	=dataset[i * batch_size : (i + 1) * batch_size]
														yield dict(_lowerCAmelCase					)



@partial(jax.pmap  ,   axis_name='batch'					)
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   **_lowerCAmelCase					):



							"""simple docstring"""



							def loss_fn(_lowerCAmelCase					):
														__lowercase       	=model_inputs.pop('start_labels'					)
														__lowercase       	=model_inputs.pop('end_labels'					)
														__lowercase       	=model_inputs.pop('pooled_labels'					)

														__lowercase       	=state.apply_fn(**_lowerCAmelCase  ,   params=_lowerCAmelCase  ,   dropout_rng=_lowerCAmelCase  ,   train=_lowerCAmelCase					)
														__lowercase	,				__lowercase	,				__lowercase       	=outputs

														return state.loss_fn(
														    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   )

							__lowercase	,				__lowercase       	=jax.random.split(_lowerCAmelCase					)
							__lowercase       	=jax.value_and_grad(_lowerCAmelCase					)
							__lowercase	,				__lowercase       	=grad_fn(state.params					)
							__lowercase       	=jax.lax.pmean({'loss': loss}  ,   axis_name='batch'					)
							__lowercase       	=jax.lax.pmean(_lowerCAmelCase  ,   'batch'					)

							__lowercase       	=state.apply_gradients(grads=_lowerCAmelCase					)
							return state, metrics, new_drp_rng



@partial(jax.pmap  ,   axis_name='batch'					)
def   _A       (   _lowerCAmelCase  ,   **_lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=model_inputs.pop('start_labels'					)
							__lowercase       	=model_inputs.pop('end_labels'					)
							__lowercase       	=model_inputs.pop('pooled_labels'					)

							__lowercase       	=state.apply_fn(**_lowerCAmelCase  ,   params=state.params  ,   train=_lowerCAmelCase					)
							__lowercase	,				__lowercase	,				__lowercase       	=outputs

							__lowercase       	=state.loss_fn(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							__lowercase       	=jax.lax.pmean({'loss': loss}  ,   axis_name='batch'					)
							return metrics





class _UpperCamelCase		(     train_state.TrainState		):





				'''simple docstring'''





				lowerCAmelCase__					    =    struct.field(pytree_node=A		)





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    None

				def 				__lowerCamelCase     (							self  :  Any  ,       _lowerCAmelCase  :  Tuple  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  str=None):







											'''simple docstring'''

											__lowercase       	=model.params
											__lowercase       	=TrainState.create(
											    apply_fn=model.__call__  ,       params=_lowerCAmelCase  ,       tx=_lowerCAmelCase  ,       loss_fn=_lowerCAmelCase  ,       )
											if ckpt_dir is not None:
																		__lowercase	,				__lowercase	,				__lowercase	,				__lowercase	,				__lowercase       	=restore_checkpoint(_lowerCAmelCase  ,       _lowerCAmelCase)
																		__lowercase       	={
																		    'lr': args.lr,
																		    'init_lr': args.init_lr,
																		    'warmup_steps': args.warmup_steps,
																		    'num_train_steps': num_train_steps,
																		    'weight_decay': args.weight_decay,
																		}
																		__lowercase	,				__lowercase       	=build_tx(**_lowerCAmelCase)
																		__lowercase       	=train_state.TrainState(
																		    step=_lowerCAmelCase  ,       apply_fn=model.__call__  ,       params=_lowerCAmelCase  ,       tx=_lowerCAmelCase  ,       opt_state=_lowerCAmelCase  ,       )
																		__lowercase       	=args
																		__lowercase       	=data_collator
																		__lowercase       	=lr
																		__lowercase       	=params
											__lowercase       	=jax_utils.replicate(_lowerCAmelCase)
											return state

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  Tuple  ,       _lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.args
											__lowercase       	=len(_lowerCAmelCase) // args.batch_size

											__lowercase       	=jax.random.PRNGKey(0)
											__lowercase       	=jax.random.split(_lowerCAmelCase  ,       jax.device_count())
											for epoch in range(args.max_epochs):
																		__lowercase       	=jnp.array(0  ,       dtype=jnp.floataa)
																		__lowercase       	=get_batched_dataset(_lowerCAmelCase  ,       args.batch_size  ,       seed=_lowerCAmelCase)
																		__lowercase       	=0
																		for batch in tqdm(_lowerCAmelCase  ,       total=_lowerCAmelCase  ,       desc=f"""Running EPOCH-{epoch}"""):
																									__lowercase       	=self.data_collator(_lowerCAmelCase)
																									__lowercase	,				__lowercase	,				__lowercase       	=self.train_step_fn(_lowerCAmelCase  ,       _lowerCAmelCase  ,       **_lowerCAmelCase)
																									running_loss += jax_utils.unreplicate(metrics['loss'])
																									i += 1
																									if i % args.logging_steps == 0:
																																__lowercase       	=jax_utils.unreplicate(state.step)
																																__lowercase       	=running_loss.item() / i
																																__lowercase       	=self.scheduler_fn(state_step - 1)

																																__lowercase       	=self.evaluate(_lowerCAmelCase  ,       _lowerCAmelCase)
																																__lowercase       	={
																																    'step': state_step.item(),
																																    'eval_loss': eval_loss.item(),
																																    'tr_loss': tr_loss,
																																    'lr': lr.item(),
																																}
																																tqdm.write(str(_lowerCAmelCase))
																																self.logger.log(_lowerCAmelCase  ,       commit=_lowerCAmelCase)

																									if i % args.save_steps == 0:
																																self.save_checkpoint(args.save_dir + f"""-e{epoch}-s{i}"""  ,       state=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict):







											'''simple docstring'''

											__lowercase       	=get_batched_dataset(_lowerCAmelCase  ,       self.args.batch_size)
											__lowercase       	=len(_lowerCAmelCase) // self.args.batch_size
											__lowercase       	=jnp.array(0  ,       dtype=jnp.floataa)
											__lowercase       	=0
											for batch in tqdm(_lowerCAmelCase  ,       total=_lowerCAmelCase  ,       desc='Evaluating ... '):
																		__lowercase       	=self.data_collator(_lowerCAmelCase)
																		__lowercase       	=self.val_step_fn(_lowerCAmelCase  ,       **_lowerCAmelCase)
																		running_loss += jax_utils.unreplicate(metrics['loss'])
																		i += 1
											return running_loss / i

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[Any]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=jax_utils.unreplicate(_lowerCAmelCase)
											print(f"""SAVING CHECKPOINT IN {save_dir}"""  ,       end=' ... ')
											self.model_save_fn(_lowerCAmelCase  ,       params=state.params)
											with open(os.path.join(_lowerCAmelCase  ,       'opt_state.msgpack')  ,       'wb') as f:
																		f.write(to_bytes(state.opt_state))
											joblib.dump(self.args  ,       os.path.join(_lowerCAmelCase  ,       'args.joblib'))
											joblib.dump(self.data_collator  ,       os.path.join(_lowerCAmelCase  ,       'data_collator.joblib'))
											with open(os.path.join(_lowerCAmelCase  ,       'training_state.json')  ,       'w') as f:
																		json.dump({'step': state.step.item()}  ,       _lowerCAmelCase)
											print('DONE')



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							print(f"""RESTORING CHECKPOINT FROM {save_dir}"""  ,   end=' ... '					)
							with open(os.path.join(_lowerCAmelCase  ,   'flax_model.msgpack'					)  ,   'rb'					) as f:
														__lowercase       	=from_bytes(state.params  ,   f.read()					)

							with open(os.path.join(_lowerCAmelCase  ,   'opt_state.msgpack'					)  ,   'rb'					) as f:
														__lowercase       	=from_bytes(state.opt_state  ,   f.read()					)

							__lowercase       	=joblib.load(os.path.join(_lowerCAmelCase  ,   'args.joblib'					)					)
							__lowercase       	=joblib.load(os.path.join(_lowerCAmelCase  ,   'data_collator.joblib'					)					)

							with open(os.path.join(_lowerCAmelCase  ,   'training_state.json'					)  ,   'r'					) as f:
														__lowercase       	=json.load(_lowerCAmelCase					)
							__lowercase       	=training_state['step']

							print('DONE'					)
							return params, opt_state, step, args, data_collator



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=num_train_steps - warmup_steps
							__lowercase       	=optax.linear_schedule(init_value=_lowerCAmelCase  ,   end_value=_lowerCAmelCase  ,   transition_steps=_lowerCAmelCase					)
							__lowercase       	=optax.linear_schedule(init_value=_lowerCAmelCase  ,   end_value=1e-7  ,   transition_steps=_lowerCAmelCase					)
							__lowercase       	=optax.join_schedules(schedules=[warmup_fn, decay_fn]  ,   boundaries=[warmup_steps]					)
							return lr



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							def weight_decay_mask(_lowerCAmelCase					):
														__lowercase       	=traverse_util.flatten_dict(_lowerCAmelCase					)
														__lowercase       	={k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
														return traverse_util.unflatten_dict(_lowerCAmelCase					)

							__lowercase       	=scheduler_fn(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							__lowercase       	=optax.adamw(learning_rate=_lowerCAmelCase  ,   weight_decay=_lowerCAmelCase  ,   mask=_lowerCAmelCase					)
							return tx, lr








'''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 _UpperCamelCase		(     A      ,       A      ,       A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    AltDiffusionPipeline
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_IMAGE_PARAMS

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											torch.manual_seed(0)
											__lowercase       	=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  ,       )
											__lowercase       	=DDIMScheduler(
											    beta_start=0.0_0085  ,       beta_end=0.012  ,       beta_schedule='scaled_linear'  ,       clip_sample=_lowerCAmelCase  ,       set_alpha_to_one=_lowerCAmelCase  ,       )
											torch.manual_seed(0)
											__lowercase       	=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  ,       )

											# 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)
											__lowercase       	=CLIPTextConfig(
											    bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=3_2  ,       projection_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_2  ,       )
											__lowercase       	=CLIPTextModel(_lowerCAmelCase)

											__lowercase       	=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
											__lowercase       	=7_7

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

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  Dict=0):







											'''simple docstring'''

											if str(_lowerCAmelCase).startswith('mps'):
																		__lowercase       	=torch.manual_seed(_lowerCAmelCase)
											else:
																		__lowercase       	=torch.Generator(device=_lowerCAmelCase).manual_seed(_lowerCAmelCase)
											__lowercase       	={
											    '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  :  List[str]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	='cpu'  # ensure determinism for the device-dependent torch.Generator

											__lowercase       	=self.get_dummy_components()
											torch.manual_seed(0)
											__lowercase       	=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  ,       vocab_size=5_0_0_2  ,       )
											# TODO: remove after fixing the non-deterministic text encoder
											__lowercase       	=RobertaSeriesModelWithTransformation(_lowerCAmelCase)
											__lowercase       	=text_encoder

											__lowercase       	=AltDiffusionPipeline(**_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=self.get_dummy_inputs(_lowerCAmelCase)
											__lowercase       	='A photo of an astronaut'
											__lowercase       	=alt_pipe(**_lowerCAmelCase)
											__lowercase       	=output.images
											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__lowercase       	=np.array(
											    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	='cpu'  # ensure determinism for the device-dependent torch.Generator

											__lowercase       	=self.get_dummy_components()
											__lowercase       	=PNDMScheduler(skip_prk_steps=_lowerCAmelCase)
											torch.manual_seed(0)
											__lowercase       	=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  ,       vocab_size=5_0_0_2  ,       )
											# TODO: remove after fixing the non-deterministic text encoder
											__lowercase       	=RobertaSeriesModelWithTransformation(_lowerCAmelCase)
											__lowercase       	=text_encoder
											__lowercase       	=AltDiffusionPipeline(**_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=self.get_dummy_inputs(_lowerCAmelCase)
											__lowercase       	=alt_pipe(**_lowerCAmelCase)
											__lowercase       	=output.images
											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__lowercase       	=np.array(
											    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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





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





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,       safety_checker=_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	='A painting of a squirrel eating a burger'
											__lowercase       	=torch.manual_seed(0)
											__lowercase       	=alt_pipe([prompt]  ,       generator=_lowerCAmelCase  ,       guidance_scale=6.0  ,       num_inference_steps=2_0  ,       output_type='np')

											__lowercase       	=output.images

											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowercase       	=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  :  Dict):







											'''simple docstring'''

											__lowercase       	=DDIMScheduler.from_pretrained('BAAI/AltDiffusion'  ,       subfolder='scheduler')

											__lowercase       	=AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,       scheduler=_lowerCAmelCase  ,       safety_checker=_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	='A painting of a squirrel eating a burger'
											__lowercase       	=torch.manual_seed(0)

											__lowercase       	=alt_pipe([prompt]  ,       generator=_lowerCAmelCase  ,       num_inference_steps=2  ,       output_type='numpy')
											__lowercase       	=output.images

											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowercase       	=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



