"""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 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    BigBirdConfig
     _UpperCamelCase							:    jnp.dtype	      =	jnp.floataa
     _UpperCamelCase							:    bool	      =	True






     def 					__A						(	self						):
       super().setup()
       _lowerCAmelCase		: int						    =  nn.Dense(5       ,  dtype=self.dtype						)






     def __call__(	self       ,  *a__       ,  **a__						):
       _lowerCAmelCase		: Union[str, Any]						    =  super().__call__(*a__       ,  **a__						)
       _lowerCAmelCase		: Any						    =  self.cls(outputs[2]						)
       return outputs[:2] + (cls_out,)







class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    int	      =	FlaxBigBirdForNaturalQuestionsModule







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

  _lowerCAmelCase		: str						    =  partial(_lowerCamelCase    ,reduction=jnp.mean			)
  _lowerCAmelCase		: List[Any]						    =  cross_entropy(_lowerCamelCase    ,_lowerCamelCase			)
  _lowerCAmelCase		: Tuple						    =  cross_entropy(_lowerCamelCase    ,_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  cross_entropy(_lowerCamelCase    ,_lowerCamelCase			)
  return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class 		__A							:
     _UpperCamelCase							:    str	      =	"google/bigbird-roberta-base"
     _UpperCamelCase							:    int	      =	3_000
     _UpperCamelCase							:    int	      =	10_500

     _UpperCamelCase							:    int	      =	128
     _UpperCamelCase							:    int	      =	3

     _UpperCamelCase							:    int	      =	1
     _UpperCamelCase							:    int	      =	5

     # tx_args
     _UpperCamelCase							:    float	      =	3E-5
     _UpperCamelCase							:    float	      =	0.0
     _UpperCamelCase							:    int	      =	20_000
     _UpperCamelCase							:    float	      =	0.0_0_9_5

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






     def 					__A						(	self						):
       os.makedirs(self.base_dir       ,  exist_ok=a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  os.path.join(self.base_dir       ,  self.save_dir						)
       _lowerCAmelCase		: int						    =  self.batch_size_per_device * jax.device_count()







@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    int	      =	4_096  # no dynamic padding on TPUs






     def __call__(	self       ,  a__						):
       _lowerCAmelCase		: Dict						    =  self.collate_fn(a__						)
       _lowerCAmelCase		: Dict						    =  jax.tree_util.tree_map(a__       ,  a__						)
       return batch






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  self.fetch_inputs(features["""input_ids"""]						)
       _lowerCAmelCase		: int						    =  {
           """input_ids""": jnp.array(a__       ,  dtype=jnp.intaa						),
           """attention_mask""": jnp.array(a__       ,  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 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  [self._fetch_inputs(a__						) for ids in input_ids]
       return zip(*a__						)






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







def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: Any=None			) ->   Any:
  if seed is not None:
    _lowerCAmelCase		: Any						    =  dataset.shuffle(seed=_lowerCamelCase			)
  for i in range(len(_lowerCamelCase			) // batch_size			):
    _lowerCAmelCase		: int						    =  dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(_lowerCamelCase			)





@partial(jax.pmap    ,axis_name="""batch"""			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: int    ,**_lowerCamelCase		: int			) ->   str:
  def loss_fn(_lowerCamelCase		: Tuple			):
    _lowerCAmelCase		: List[Any]						    =  model_inputs.pop("""start_labels"""			)
    _lowerCAmelCase		: List[str]						    =  model_inputs.pop("""end_labels"""			)
    _lowerCAmelCase		: List[Any]						    =  model_inputs.pop("""pooled_labels"""			)

    _lowerCAmelCase		: List[Any]						    =  state.apply_fn(**_lowerCamelCase    ,params=_lowerCamelCase    ,dropout_rng=_lowerCamelCase    ,train=_lowerCamelCase			)
    _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  outputs

    return state.loss_fn(
        _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,)

  _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  jax.random.split(_lowerCamelCase			)
  _lowerCAmelCase		: Tuple						    =  jax.value_and_grad(_lowerCamelCase			)
  _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  grad_fn(state.params			)
  _lowerCAmelCase		: Tuple						    =  jax.lax.pmean({"""loss""": loss}    ,axis_name="""batch"""			)
  _lowerCAmelCase		: Dict						    =  jax.lax.pmean(_lowerCamelCase    ,"""batch"""			)

  _lowerCAmelCase		: Tuple						    =  state.apply_gradients(grads=_lowerCamelCase			)
  return state, metrics, new_drp_rng





@partial(jax.pmap    ,axis_name="""batch"""			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,**_lowerCamelCase		: List[str]			) ->   List[str]:
  _lowerCAmelCase		: Tuple						    =  model_inputs.pop("""start_labels"""			)
  _lowerCAmelCase		: Optional[Any]						    =  model_inputs.pop("""end_labels"""			)
  _lowerCAmelCase		: int						    =  model_inputs.pop("""pooled_labels"""			)

  _lowerCAmelCase		: Tuple						    =  state.apply_fn(**_lowerCamelCase    ,params=state.params    ,train=_lowerCamelCase			)
  _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  outputs

  _lowerCAmelCase		: Tuple						    =  state.loss_fn(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
  _lowerCAmelCase		: Tuple						    =  jax.lax.pmean({"""loss""": loss}    ,axis_name="""batch"""			)
  return metrics
class 		__A							(				train_state.TrainState	):
     _UpperCamelCase							:    Callable	      =	struct.field(pytree_node=SCREAMING_SNAKE_CASE_	)







@dataclass
class 		__A							:
     _UpperCamelCase							:    Args
     _UpperCamelCase							:    Callable
     _UpperCamelCase							:    Callable
     _UpperCamelCase							:    Callable
     _UpperCamelCase							:    Callable
     _UpperCamelCase							:    wandb
     _UpperCamelCase							:    Callable	      =	None






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       _lowerCAmelCase		: str						    =  model.params
       _lowerCAmelCase		: str						    =  TrainState.create(
           apply_fn=model.__call__       ,  params=a__       ,  tx=a__       ,  loss_fn=a__       ,  )
       if ckpt_dir is not None:
         _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  restore_checkpoint(a__       ,  a__						)
         _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		: str						    =  build_tx(**a__						)
         _lowerCAmelCase		: str						    =  train_state.TrainState(
             step=a__       ,  apply_fn=model.__call__       ,  params=a__       ,  tx=a__       ,  opt_state=a__       ,  )
         _lowerCAmelCase		: int						    =  args
         _lowerCAmelCase		: str						    =  data_collator
         _lowerCAmelCase		: Optional[Any]						    =  lr
         _lowerCAmelCase		: int						    =  params
       _lowerCAmelCase		: Any						    =  jax_utils.replicate(a__						)
       return state






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.args
       _lowerCAmelCase		: Union[str, Any]						    =  len(a__						) // args.batch_size

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

             _lowerCAmelCase		: str						    =  self.evaluate(a__       ,  a__						)
             _lowerCAmelCase		: List[Any]						    =  {
                 """step""": state_step.item(),
                 """eval_loss""": eval_loss.item(),
                 """tr_loss""": tr_loss,
                 """lr""": lr.item(),
             }
             tqdm.write(str(a__						)						)
             self.logger.log(a__       ,  commit=a__						)

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






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase		: Dict						    =  get_batched_dataset(a__       ,  self.args.batch_size						)
       _lowerCAmelCase		: List[Any]						    =  len(a__						) // self.args.batch_size
       _lowerCAmelCase		: str						    =  jnp.array(0       ,  dtype=jnp.floataa						)
       _lowerCAmelCase		: List[str]						    =  0
       for batch in tqdm(a__       ,  total=a__       ,  desc="""Evaluating ... """						):
         _lowerCAmelCase		: Any						    =  self.data_collator(a__						)
         _lowerCAmelCase		: Tuple						    =  self.val_step_fn(a__       ,  **a__						)
         running_loss += jax_utils.unreplicate(metrics["""loss"""]						)
         i += 1
       return running_loss / i






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







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

  with open(os.path.join(_lowerCamelCase    ,"""opt_state.msgpack"""			)    ,"""rb"""			) as f:
    _lowerCAmelCase		: Optional[int]						    =  from_bytes(state.opt_state    ,f.read()			)

  _lowerCAmelCase		: int						    =  joblib.load(os.path.join(_lowerCamelCase    ,"""args.joblib"""			)			)
  _lowerCAmelCase		: List[Any]						    =  joblib.load(os.path.join(_lowerCamelCase    ,"""data_collator.joblib"""			)			)

  with open(os.path.join(_lowerCamelCase    ,"""training_state.json"""			)    ,"""r"""			) as f:
    _lowerCAmelCase		: Tuple						    =  json.load(_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  training_state["""step"""]

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





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






def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: Optional[int]			) ->   str:
  def weight_decay_mask(_lowerCamelCase		: List[Any]			):
    _lowerCAmelCase		: List[str]						    =  traverse_util.flatten_dict(_lowerCamelCase			)
    _lowerCAmelCase		: int						    =  {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
    return traverse_util.unflatten_dict(_lowerCamelCase			)

  _lowerCAmelCase		: List[Any]						    =  scheduler_fn(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)

  _lowerCAmelCase		: Optional[Any]						    =  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, CLIPTextModelWithProjection, CLIPTokenizer

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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	ShapEPipeline
     _UpperCamelCase							:    Optional[Any]	      =	["prompt"]
     _UpperCamelCase							:    Tuple	      =	["prompt"]
     _UpperCamelCase							:    Dict	      =	[
         "num_images_per_prompt",
         "num_inference_steps",
         "generator",
         "latents",
         "guidance_scale",
         "frame_size",
         "output_type",
         "return_dict",
     ]
     _UpperCamelCase							:    str	      =	False






     @property
     def 					__A						(	self						):
       return 32






     @property
     def 					__A						(	self						):
       return 32






     @property
     def 					__A						(	self						):
       return self.time_input_dim * 4






     @property
     def 					__A						(	self						):
       return 8






     @property
     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
       return tokenizer






     @property
     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: List[Any]						    =  CLIPTextConfig(
           bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=self.text_embedder_hidden_size       ,  projection_dim=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
       return CLIPTextModelWithProjection(a__						)






     @property
     def 					__A						(	self						):
       torch.manual_seed(0						)

       _lowerCAmelCase		: Tuple						    =  {
           """num_attention_heads""": 2,
           """attention_head_dim""": 16,
           """embedding_dim""": self.time_input_dim,
           """num_embeddings""": 32,
           """embedding_proj_dim""": self.text_embedder_hidden_size,
           """time_embed_dim""": self.time_embed_dim,
           """num_layers""": 1,
           """clip_embed_dim""": self.time_input_dim * 2,
           """additional_embeddings""": 0,
           """time_embed_act_fn""": """gelu""",
           """norm_in_type""": """layer""",
           """encoder_hid_proj_type""": None,
           """added_emb_type""": None,
       }

       _lowerCAmelCase		: Any						    =  PriorTransformer(**a__						)
       return model






     @property
     def 					__A						(	self						):
       torch.manual_seed(0						)

       _lowerCAmelCase		: Tuple						    =  {
           """param_shapes""": (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           """d_latent""": self.time_input_dim,
           """d_hidden""": self.renderer_dim,
           """n_output""": 12,
           """background""": (
               0.1,
               0.1,
               0.1,
           ),
       }
       _lowerCAmelCase		: Dict						    =  ShapERenderer(**a__						)
       return model






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.dummy_prior
       _lowerCAmelCase		: Any						    =  self.dummy_text_encoder
       _lowerCAmelCase		: List[Any]						    =  self.dummy_tokenizer
       _lowerCAmelCase		: Dict						    =  self.dummy_renderer

       _lowerCAmelCase		: List[Any]						    =  HeunDiscreteScheduler(
           beta_schedule="""exp"""       ,  num_train_timesteps=1024       ,  prediction_type="""sample"""       ,  use_karras_sigmas=a__       ,  clip_sample=a__       ,  clip_sample_range=1.0       ,  )
       _lowerCAmelCase		: List[Any]						    =  {
           """prior""": prior,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """renderer""": renderer,
           """scheduler""": scheduler,
       }

       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: List[str]						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Union[str, Any]						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Dict						    =  {
           """prompt""": """horse""",
           """generator""": generator,
           """num_inference_steps""": 1,
           """frame_size""": 32,
           """output_type""": """np""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: List[Any]						    =  self.get_dummy_components()

       _lowerCAmelCase		: str						    =  self.pipeline_class(**a__						)
       _lowerCAmelCase		: List[Any]						    =  pipe.to(a__						)

       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Optional[int]						    =  pipe(**self.get_dummy_inputs(a__						)						)
       _lowerCAmelCase		: List[str]						    =  output.images[0]
       _lowerCAmelCase		: Optional[Any]						    =  image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       _lowerCAmelCase		: Union[str, Any]						    =  np.array(
           [
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
           ]						)

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






     def 					__A						(	self						):
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  torch_device == """cpu"""
       _lowerCAmelCase		: Dict						    =  True

       self._test_inference_batch_single_identical(
           batch_size=2       ,  test_max_difference=a__       ,  relax_max_difference=a__       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[Any]						    =  self.pipeline_class(**a__						)
       _lowerCAmelCase		: int						    =  pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: str						    =  1
       _lowerCAmelCase		: Optional[Any]						    =  2

       _lowerCAmelCase		: List[Any]						    =  self.get_dummy_inputs(a__						)

       for key in inputs.keys():
         if key in self.batch_params:
           _lowerCAmelCase		: str						    =  batch_size * [inputs[key]]

       _lowerCAmelCase		: Tuple						    =  pipe(**a__       ,  num_images_per_prompt=a__						)[0]

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







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






     def 					__A						(	self						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/shap_e/test_shap_e_np_out.npy"""						)
       _lowerCAmelCase		: Union[str, Any]						    =  ShapEPipeline.from_pretrained("""openai/shap-e"""						)
       _lowerCAmelCase		: Tuple						    =  pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Optional[int]						    =  torch.Generator(device=a__						).manual_seed(0						)

       _lowerCAmelCase		: Any						    =  pipe(
           """a shark"""       ,  generator=a__       ,  guidance_scale=1_5.0       ,  num_inference_steps=64       ,  frame_size=64       ,  output_type="""np"""       ,  ).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(a__       ,  a__						)




