
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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 				lowerCamelCase__							(       __lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  ShapEImgaImgPipeline
	_A			=  ['image']
	_A			=  ['image']
	_A			=  [
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	_A			=  False







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				Dict:
								return 3_2







	@property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Tuple:
								return 3_2







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Tuple:
								return self.time_input_dim * 4







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Optional[int]:
								return 8







	@property
	def      _lowerCamelCase       (      self	:Dict		)	->				List[Any]:
								torch.manual_seed(0		)
								__UpperCamelCase			:     Tuple         =      CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size  ,     image_size=6_4  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1  ,     )

								__UpperCamelCase			:     str         =      CLIPVisionModel(a		)
								return model







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      CLIPImageProcessor(
								    crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.48145466, 0.4578275, 0.40821073]  ,     image_std=[0.26862954, 0.26130258, 0.27577711]  ,     resample=3  ,     size=2_2_4  ,     )

								return image_processor







	@property
	def      _lowerCamelCase       (      self	:Dict		)	->				Optional[Any]:
								torch.manual_seed(0		)

								__UpperCamelCase			:     Tuple         =      {
								    "num_attention_heads": 2,
								    "attention_head_dim": 1_6,
								    "embedding_dim": self.time_input_dim,
								    "num_embeddings": 3_2,
								    "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",
								    "embedding_proj_norm_type": "layer",
								    "encoder_hid_proj_type": None,
								    "added_emb_type": None,
								}

								__UpperCamelCase			:     Union[str, Any]         =      PriorTransformer(**a		)
								return model







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Tuple:
								torch.manual_seed(0		)

								__UpperCamelCase			:     Optional[Any]         =      {
								    "param_shapes": (
								        (self.renderer_dim, 9_3),
								        (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": 1_2,
								    "background": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__UpperCamelCase			:     Any         =      ShapERenderer(**a		)
								return model







	def      _lowerCamelCase       (      self	:int		)	->				Dict:
								__UpperCamelCase			:     Optional[int]         =      self.dummy_prior
								__UpperCamelCase			:     Tuple         =      self.dummy_image_encoder
								__UpperCamelCase			:     List[str]         =      self.dummy_image_processor
								__UpperCamelCase			:     Optional[Any]         =      self.dummy_renderer

								__UpperCamelCase			:     str         =      HeunDiscreteScheduler(
								    beta_schedule="exp"  ,     num_train_timesteps=1_0_2_4  ,     prediction_type="sample"  ,     use_karras_sigmas=a  ,     clip_sample=a  ,     clip_sample_range=1.0  ,     )
								__UpperCamelCase			:     List[Any]         =      {
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "image_processor": image_processor,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components







	def      _lowerCamelCase       (      self	:int  ,     a	:List[str]  ,     a	:List[Any]=0		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      floats_tensor((1, 3, 6_4, 6_4)  ,     rng=random.Random(a		)		).to(a		)

								if str(a		).startswith("mps"		):
															__UpperCamelCase			:     str         =      torch.manual_seed(a		)
								else:
															__UpperCamelCase			:     str         =      torch.Generator(device=a		).manual_seed(a		)
								__UpperCamelCase			:     Any         =      {
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 3_2,
								    "output_type": "np",
								}
								return inputs







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      "cpu"

								__UpperCamelCase			:     Optional[Any]         =      self.get_dummy_components()

								__UpperCamelCase			:     Union[str, Any]         =      self.pipeline_class(**a		)
								__UpperCamelCase			:     List[Any]         =      pipe.to(a		)

								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase			:     List[str]         =      pipe(**self.get_dummy_inputs(a		)		)
								__UpperCamelCase			:     int         =      output.images[0]
								__UpperCamelCase			:     List[str]         =      image[0, -3:, -3:, -1]

								assert image.shape == (2_0, 3_2, 3_2, 3)

								__UpperCamelCase			:     Optional[int]         =      np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]		)

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







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







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     List[Any]         =      torch_device == "cpu"
								__UpperCamelCase			:     Any         =      True
								self._test_inference_batch_single_identical(
								    batch_size=2  ,     test_max_difference=a  ,     relax_max_difference=a  ,     )







	def      _lowerCamelCase       (      self	:List[str]		)	->				Any:
								__UpperCamelCase			:     Dict         =      self.get_dummy_components()
								__UpperCamelCase			:     Optional[Any]         =      self.pipeline_class(**a		)
								__UpperCamelCase			:     Any         =      pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase			:     Union[str, Any]         =      1
								__UpperCamelCase			:     Optional[Any]         =      2

								__UpperCamelCase			:     List[Any]         =      self.get_dummy_inputs(a		)

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

								__UpperCamelCase			:     Any         =      pipe(**a  ,     num_images_per_prompt=a		)[0]

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




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




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Any		)	->				Optional[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								__UpperCamelCase			:     List[str]         =      load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"		)
								__UpperCamelCase			:     Dict         =      load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/shap_e/test_shap_e_img2img_out.npy"		)
								__UpperCamelCase			:     List[str]         =      ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"		)
								__UpperCamelCase			:     int         =      pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase			:     Tuple         =      torch.Generator(device=a		).manual_seed(0		)

								__UpperCamelCase			:     Union[str, Any]         =      pipe(
								    a  ,     generator=a  ,     guidance_scale=3.0  ,     num_inference_steps=6_4  ,     frame_size=6_4  ,     output_type="np"  ,     ).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(a  ,     a		)

from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
		from .deepspeed import (
		    DeepSpeedEngineWrapper,
		    DeepSpeedOptimizerWrapper,
		    DeepSpeedSchedulerWrapper,
		    DummyOptim,
		    DummyScheduler,
		    HfDeepSpeedConfig,
		)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers