


'''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_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					BigBirdConfig
    lowerCamelCase  :					jnp.dtype							     =			jnp.floataa
    lowerCamelCase  :					bool							     =			True







    def       lowercase_				(							self	)				->     List[str]:
         super().setup()
         __lowerCamelCase   :	str								=		nn.Dense(5			,     dtype=self.dtype	)







    def __call__(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase   :	Optional[int]								=		super().__call__(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		self.cls(outputs[2]	)
         return outputs[:2] + (cls_out,)







class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[int]							     =			FlaxBigBirdForNaturalQuestionsModule







def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]							)     ->		Tuple:
     def cross_entropy(UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				List[str]=None							):
          __lowerCamelCase   :	Union[str, Any]								=		logits.shape[-1]
          __lowerCamelCase   :	Any								=		(labels[..., None] == jnp.arange(UpperCAmelCase_							)[None]).astype('f4'							)
          __lowerCamelCase   :	Dict								=		jax.nn.log_softmax(UpperCAmelCase_      ,    axis=-1							)
          __lowerCamelCase   :	Optional[int]								=		-jnp.sum(labels * logits      ,    axis=-1							)
          if reduction is not None:
               __lowerCamelCase   :	str								=		reduction(UpperCAmelCase_							)
          return loss

     __lowerCamelCase   :	List[str]								=		partial(UpperCAmelCase_      ,    reduction=jnp.mean							)
     __lowerCamelCase   :	List[str]								=		cross_entropy(UpperCAmelCase_      ,    UpperCAmelCase_							)
     __lowerCamelCase   :	str								=		cross_entropy(UpperCAmelCase_      ,    UpperCAmelCase_							)
     __lowerCamelCase   :	Tuple								=		cross_entropy(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return (start_loss + end_loss + pooled_loss) / 3





@dataclass
class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					str							     =			"google/bigbird-roberta-base"
    lowerCamelCase  :					int							     =			3_0_0_0
    lowerCamelCase  :					int							     =			1_0_5_0_0

    lowerCamelCase  :					int							     =			1_2_8
    lowerCamelCase  :					int							     =			3

    lowerCamelCase  :					int							     =			1
    lowerCamelCase  :					int							     =			5

    # tx_args
    lowerCamelCase  :					float							     =			3e-5
    lowerCamelCase  :					float							     =			0.0
    lowerCamelCase  :					int							     =			2_0_0_0_0
    lowerCamelCase  :					float							     =			0.0095

    lowerCamelCase  :					str							     =			"bigbird-roberta-natural-questions"
    lowerCamelCase  :					str							     =			"training-expt"
    lowerCamelCase  :					str							     =			"data/nq-training.jsonl"
    lowerCamelCase  :					str							     =			"data/nq-validation.jsonl"







    def       lowercase_				(							self	)				->     Dict:
         os.makedirs(self.base_dir			,     exist_ok=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		os.path.join(self.base_dir			,     self.save_dir	)
         __lowerCamelCase   :	Dict								=		self.batch_size_per_device * jax.device_count()







@dataclass
class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					int
    lowerCamelCase  :					int							     =			4_0_9_6  # no dynamic padding on TPUs







    def __call__(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.collate_fn(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		jax.tree_util.tree_map(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         return batch







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.fetch_inputs(features['input_ids']	)
         __lowerCamelCase   :	Dict								=		{
             'input_ids': jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	),
             'attention_mask': jnp.array(SCREAMING_SNAKE_CASE_			,     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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	Optional[Any]								=		[self._fetch_inputs(SCREAMING_SNAKE_CASE_	) for ids in input_ids]
         return zip(*SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         __lowerCamelCase   :	Tuple								=		[1 for _ in range(len(SCREAMING_SNAKE_CASE_	)	)]
         while len(SCREAMING_SNAKE_CASE_	) < self.max_length:
              input_ids.append(self.pad_id	)
              attention_mask.append(0	)
         return input_ids, attention_mask







def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				List[str]=None							)     ->		Tuple:
     if seed is not None:
          __lowerCamelCase   :	Union[str, Any]								=		dataset.shuffle(seed=UpperCAmelCase_							)
     for i in range(len(UpperCAmelCase_							) // batch_size							):
          __lowerCamelCase   :	List[Any]								=		dataset[i * batch_size : (i + 1) * batch_size]
          yield dict(UpperCAmelCase_							)



@partial(jax.pmap      ,    axis_name='batch'							)
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Optional[int]      ,    **UpperCAmelCase_    :				List[Any]							)     ->		Dict:
     def loss_fn(UpperCAmelCase_    :				Any							):
          __lowerCamelCase   :	Union[str, Any]								=		model_inputs.pop('start_labels'							)
          __lowerCamelCase   :	int								=		model_inputs.pop('end_labels'							)
          __lowerCamelCase   :	int								=		model_inputs.pop('pooled_labels'							)

          __lowerCamelCase   :	int								=		state.apply_fn(**UpperCAmelCase_      ,    params=UpperCAmelCase_      ,    dropout_rng=UpperCAmelCase_      ,    train=UpperCAmelCase_							)
          __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		outputs

          return state.loss_fn(
              UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    )

     __lowerCamelCase				,       __lowerCamelCase   :	int								=		jax.random.split(UpperCAmelCase_							)
     __lowerCamelCase   :	List[Any]								=		jax.value_and_grad(UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		grad_fn(state.params							)
     __lowerCamelCase   :	int								=		jax.lax.pmean({'loss': loss}      ,    axis_name='batch'							)
     __lowerCamelCase   :	Dict								=		jax.lax.pmean(UpperCAmelCase_      ,    'batch'							)

     __lowerCamelCase   :	Optional[int]								=		state.apply_gradients(grads=UpperCAmelCase_							)
     return state, metrics, new_drp_rng



@partial(jax.pmap      ,    axis_name='batch'							)
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    **UpperCAmelCase_    :				int							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		model_inputs.pop('start_labels'							)
     __lowerCamelCase   :	List[Any]								=		model_inputs.pop('end_labels'							)
     __lowerCamelCase   :	Optional[Any]								=		model_inputs.pop('pooled_labels'							)

     __lowerCamelCase   :	int								=		state.apply_fn(**UpperCAmelCase_      ,    params=state.params      ,    train=UpperCAmelCase_							)
     __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		outputs

     __lowerCamelCase   :	List[str]								=		state.loss_fn(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[int]								=		jax.lax.pmean({'loss': loss}      ,    axis_name='batch'							)
     return metrics





class    UpperCAmelCase_  (train_state.TrainState  ):





    """simple docstring"""






    lowerCamelCase  :					Callable							     =			struct.field(pytree_node=_UpperCAmelCase  )







@dataclass
class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					Args
    lowerCamelCase  :					Callable
    lowerCamelCase  :					Callable
    lowerCamelCase  :					Callable
    lowerCamelCase  :					Callable
    lowerCamelCase  :					wandb
    lowerCamelCase  :					Callable							     =			None







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     Any:
         __lowerCamelCase   :	int								=		model.params
         __lowerCamelCase   :	List[Any]								=		TrainState.create(
             apply_fn=model.__call__			,     params=SCREAMING_SNAKE_CASE_			,     tx=SCREAMING_SNAKE_CASE_			,     loss_fn=SCREAMING_SNAKE_CASE_			,     )
         if ckpt_dir is not None:
              __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		restore_checkpoint(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Dict								=		{
                  'lr': args.lr,
                  'init_lr': args.init_lr,
                  'warmup_steps': args.warmup_steps,
                  'num_train_steps': num_train_steps,
                  'weight_decay': args.weight_decay,
              }
              __lowerCamelCase				,       __lowerCamelCase   :	Any								=		build_tx(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Any								=		train_state.TrainState(
                  step=SCREAMING_SNAKE_CASE_			,     apply_fn=model.__call__			,     params=SCREAMING_SNAKE_CASE_			,     tx=SCREAMING_SNAKE_CASE_			,     opt_state=SCREAMING_SNAKE_CASE_			,     )
              __lowerCamelCase   :	str								=		args
              __lowerCamelCase   :	Tuple								=		data_collator
              __lowerCamelCase   :	int								=		lr
              __lowerCamelCase   :	Optional[Any]								=		params
         __lowerCamelCase   :	Optional[Any]								=		jax_utils.replicate(SCREAMING_SNAKE_CASE_	)
         return state







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[Any]								=		self.args
         __lowerCamelCase   :	Optional[Any]								=		len(SCREAMING_SNAKE_CASE_	) // args.batch_size

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

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

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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     str:
         __lowerCamelCase   :	List[Any]								=		get_batched_dataset(SCREAMING_SNAKE_CASE_			,     self.args.batch_size	)
         __lowerCamelCase   :	int								=		len(SCREAMING_SNAKE_CASE_	) // self.args.batch_size
         __lowerCamelCase   :	Any								=		jnp.array(0			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		0
         for batch in tqdm(SCREAMING_SNAKE_CASE_			,     total=SCREAMING_SNAKE_CASE_			,     desc='Evaluating ... '	):
              __lowerCamelCase   :	Union[str, Any]								=		self.data_collator(SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Any								=		self.val_step_fn(SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)
              running_loss += jax_utils.unreplicate(metrics['loss']	)
              i += 1
         return running_loss / i







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         __lowerCamelCase   :	Dict								=		jax_utils.unreplicate(SCREAMING_SNAKE_CASE_	)
         print(f'SAVING CHECKPOINT IN {save_dir}'			,     end=' ... '	)
         self.model_save_fn(SCREAMING_SNAKE_CASE_			,     params=state.params	)
         with open(os.path.join(SCREAMING_SNAKE_CASE_			,     'opt_state.msgpack'	)			,     'wb'	) as f:
              f.write(to_bytes(state.opt_state	)	)
         joblib.dump(self.args			,     os.path.join(SCREAMING_SNAKE_CASE_			,     'args.joblib'	)	)
         joblib.dump(self.data_collator			,     os.path.join(SCREAMING_SNAKE_CASE_			,     'data_collator.joblib'	)	)
         with open(os.path.join(SCREAMING_SNAKE_CASE_			,     'training_state.json'	)			,     'w'	) as f:
              json.dump({'step': state.step.item()}			,     SCREAMING_SNAKE_CASE_	)
         print('DONE'	)







def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Any:
     print(F'RESTORING CHECKPOINT FROM {save_dir}'      ,    end=' ... '							)
     with open(os.path.join(UpperCAmelCase_      ,    'flax_model.msgpack'							)      ,    'rb'							) as f:
          __lowerCamelCase   :	Optional[Any]								=		from_bytes(state.params      ,    f.read()							)

     with open(os.path.join(UpperCAmelCase_      ,    'opt_state.msgpack'							)      ,    'rb'							) as f:
          __lowerCamelCase   :	Optional[int]								=		from_bytes(state.opt_state      ,    f.read()							)

     __lowerCamelCase   :	int								=		joblib.load(os.path.join(UpperCAmelCase_      ,    'args.joblib'							)							)
     __lowerCamelCase   :	Optional[Any]								=		joblib.load(os.path.join(UpperCAmelCase_      ,    'data_collator.joblib'							)							)

     with open(os.path.join(UpperCAmelCase_      ,    'training_state.json'							)      ,    'r'							) as f:
          __lowerCamelCase   :	Optional[Any]								=		json.load(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		training_state['step']

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Tuple							)     ->		Union[str, Any]:
     __lowerCamelCase   :	List[Any]								=		num_train_steps - warmup_steps
     __lowerCamelCase   :	Union[str, Any]								=		optax.linear_schedule(init_value=UpperCAmelCase_      ,    end_value=UpperCAmelCase_      ,    transition_steps=UpperCAmelCase_							)
     __lowerCamelCase   :	Dict								=		optax.linear_schedule(init_value=UpperCAmelCase_      ,    end_value=1e-7      ,    transition_steps=UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[int]								=		optax.join_schedules(schedules=[warmup_fn, decay_fn]      ,    boundaries=[warmup_steps]							)
     return lr



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Optional[int]							)     ->		str:
     def weight_decay_mask(UpperCAmelCase_    :				List[Any]							):
          __lowerCamelCase   :	List[str]								=		traverse_util.flatten_dict(UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
          return traverse_util.unflatten_dict(UpperCAmelCase_							)

     __lowerCamelCase   :	List[Any]								=		scheduler_fn(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		optax.adamw(learning_rate=UpperCAmelCase_      ,    weight_decay=UpperCAmelCase_      ,    mask=UpperCAmelCase_							)
     return tx, lr










'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			StableDiffusionInstructPixaPixPipeline
    lowerCamelCase  :					List[Any]							     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
    lowerCamelCase  :					List[str]							     =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase  :					Optional[int]							     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase  :					List[str]							     =			IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_				(							self	)				->     int:
         torch.manual_seed(0	)
         __lowerCamelCase   :	Any								=		UNetaDConditionModel(
             block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=8			,     out_channels=4			,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,     cross_attention_dim=32			,     )
         __lowerCamelCase   :	Union[str, Any]								=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
         torch.manual_seed(0	)
         __lowerCamelCase   :	Optional[Any]								=		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			,     )
         torch.manual_seed(0	)
         __lowerCamelCase   :	Optional[int]								=		CLIPTextConfig(
             bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1E-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     )
         __lowerCamelCase   :	int								=		CLIPTextModel(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         __lowerCamelCase   :	Optional[Any]								=		{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=0	)				->     Dict:
         __lowerCamelCase   :	int								=		floats_tensor((1, 3, 32, 32)			,     rng=random.Random(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		image.cpu().permute(0			,     2			,     3			,     1	)[0]
         __lowerCamelCase   :	int								=		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_	)	).convert('RGB'	)
         if str(SCREAMING_SNAKE_CASE_	).startswith('mps'	):
              __lowerCamelCase   :	Union[str, Any]								=		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
         else:
              __lowerCamelCase   :	Optional[int]								=		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'image_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Any								=		self.get_dummy_components()
         __lowerCamelCase   :	Tuple								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	List[str]								=		image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	Optional[int]								=		np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[str]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	List[Any]								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Union[str, Any]								=		'french fries'
         __lowerCamelCase   :	List[Any]								=		sd_pipe(**SCREAMING_SNAKE_CASE_			,     negative_prompt=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		output.images
         __lowerCamelCase   :	str								=		image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	Optional[Any]								=		np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	List[str]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	Any								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		[inputs['prompt']] * 2

         __lowerCamelCase   :	Tuple								=		np.array(inputs['image']	).astype(np.floataa	) / 2_5_5.0
         __lowerCamelCase   :	List[str]								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		image / 2 + 0.5
         __lowerCamelCase   :	Optional[Any]								=		image.permute(0			,     3			,     1			,     2	)
         __lowerCamelCase   :	Dict								=		image.repeat(2			,     1			,     1			,     1	)

         __lowerCamelCase   :	int								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Tuple								=		image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	int								=		self.get_dummy_components()
         __lowerCamelCase   :	Union[str, Any]								=		EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5			,     beta_end=0.0_1_2			,     beta_schedule='scaled_linear'	)
         __lowerCamelCase   :	str								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Optional[Any]								=		image[0, -3:, -3:, -1]

         __lowerCamelCase   :	Tuple								=		[round(SCREAMING_SNAKE_CASE_			,     4	) for x in image_slice.flatten().tolist()]
         print(','.join([str(SCREAMING_SNAKE_CASE_	) for x in slice]	)	)

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	List[str]								=		np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	Tuple								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_			,     do_normalize=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	int								=		pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_			,     input_image_type='pt'	)	)[0]

         __lowerCamelCase   :	Optional[Any]								=		components['vae']
         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_			,     input_image_type='pt'	)

         for image_param in self.image_latents_params:
              if image_param in inputs.keys():
                   __lowerCamelCase   :	str								=		vae.encode(inputs[image_param]	).latent_dist.mode()

         __lowerCamelCase   :	str								=		pipe(**SCREAMING_SNAKE_CASE_	)[0]

         __lowerCamelCase   :	Optional[Any]								=		np.abs(out - out_latents_inputs	).max()
         self.assertLess(SCREAMING_SNAKE_CASE_			,     1E-4			,     'passing latents as image input generate different result from passing image'	)







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





    """simple docstring"""






    def       lowercase_				(							self	)				->     int:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0	)				->     str:
         __lowerCamelCase   :	str								=		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'	)
         __lowerCamelCase   :	Any								=		{
             'prompt': 'turn him into a cyborg',
             'image': image,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'image_guidance_scale': 1.0,
             'output_type': 'numpy',
         }
         return inputs







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	int								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[Any]								=		self.get_inputs()
         __lowerCamelCase   :	List[str]								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Optional[int]								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Any								=		np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]	)

         assert np.abs(expected_slice - image_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[Any]								=		self.get_inputs()
         __lowerCamelCase   :	Optional[int]								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	str								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Optional[Any]								=		np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]	)

         assert np.abs(expected_slice - image_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		DDIMScheduler.from_config(pipe.scheduler.config	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Union[str, Any]								=		self.get_inputs()
         __lowerCamelCase   :	str								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Dict								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]	)

         assert np.abs(expected_slice - image_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		0

         def callback_fn(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) -> None:
              __lowerCamelCase   :	List[Any]								=		True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                   __lowerCamelCase   :	Any								=		latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase   :	Union[str, Any]								=		latents[0, -3:, -3:, -1]
                   __lowerCamelCase   :	str								=		np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]	)

                   assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2
              elif step == 2:
                   __lowerCamelCase   :	Optional[int]								=		latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase   :	List[Any]								=		latents[0, -3:, -3:, -1]
                   __lowerCamelCase   :	Any								=		np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]	)

                   assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2

         __lowerCamelCase   :	int								=		False

         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[int]								=		self.get_inputs()
         pipe(**SCREAMING_SNAKE_CASE_			,     callback=SCREAMING_SNAKE_CASE_			,     callback_steps=1	)
         assert callback_fn.has_been_called
         assert number_of_steps == 3







    def       lowercase_				(							self	)				->     Union[str, Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	List[Any]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing(1	)
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase   :	List[str]								=		self.get_inputs()
         __lowerCamelCase   :	Tuple								=		pipe(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Optional[int]								=		self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         __lowerCamelCase   :	Union[str, Any]								=		inputs['image'].resize((5_04, 5_04)	)

         __lowerCamelCase   :	int								=		'timbrooks/instruct-pix2pix'
         __lowerCamelCase   :	Any								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     )
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Dict								=		pipe(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		output.images[0]

         __lowerCamelCase   :	Optional[int]								=		image[2_55:2_58, 3_83:3_86, -1]

         assert image.shape == (5_04, 5_04, 3)
         __lowerCamelCase   :	List[str]								=		np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]	)

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






