



"""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							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			ShapEPipeline
	SCREAMING_SNAKE_CASE					   =			['prompt']
	SCREAMING_SNAKE_CASE					   =			['prompt']
	SCREAMING_SNAKE_CASE					   =			[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	SCREAMING_SNAKE_CASE					   =			False

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> int:

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Union[str, Any]:

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Any:

				"""simple docstring"""

				return self.time_input_dim * 4

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Optional[int]:

				"""simple docstring"""

				return 8

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
				return tokenizer

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> List[str]:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= 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(_lowercase)

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> List[Any]:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase   :						int				= {
				    """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(**_lowercase)
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Any:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase   :						Union[str, Any]				= {
				    """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   :						str				= ShapERenderer(**_lowercase)
				return model

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.dummy_prior
				__lowerCAmelCase   :						List[str]				= self.dummy_text_encoder
				__lowerCAmelCase   :						Optional[Any]				= self.dummy_tokenizer
				__lowerCAmelCase   :						Optional[Any]				= self.dummy_renderer

				__lowerCAmelCase   :						Any				= HeunDiscreteScheduler(
				    beta_schedule="exp" ,    num_train_timesteps=1024 ,    prediction_type="sample" ,    use_karras_sigmas=_lowercase ,    clip_sample=_lowercase ,    clip_sample_range=1.0 ,    )
				__lowerCAmelCase   :						Union[str, Any]				= {
				    """prior""": prior,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """renderer""": renderer,
				    """scheduler""": scheduler,
				}

				return components

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	int=0)       -> int:

				"""simple docstring"""

				if str(_lowercase).startswith("mps"):
							__lowerCAmelCase   :						Optional[Any]				= torch.manual_seed(_lowercase)
				else:
							__lowerCAmelCase   :						Tuple				= torch.Generator(device=_lowercase).manual_seed(_lowercase)
				__lowerCAmelCase   :						Optional[Any]				= {
				    """prompt""": """horse""",
				    """generator""": generator,
				    """num_inference_steps""": 1,
				    """frame_size""": 32,
				    """output_type""": """np""",
				}
				return inputs

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= """cpu"""

				__lowerCAmelCase   :						List[str]				= self.get_dummy_components()

				__lowerCAmelCase   :						int				= self.pipeline_class(**_lowercase)
				__lowerCAmelCase   :						List[str]				= pipe.to(_lowercase)

				pipe.set_progress_bar_config(disable=_lowercase)

				__lowerCAmelCase   :						List[Any]				= pipe(**self.get_dummy_inputs(_lowercase))
				__lowerCAmelCase   :						List[Any]				= output.images[0]
				__lowerCAmelCase   :						Union[str, Any]				= image[0, -3:, -3:, -1]

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

				__lowerCAmelCase   :						Any				= np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ])

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> List[str]:

				"""simple docstring"""

				self._test_inference_batch_consistent(batch_sizes=[1, 2])

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= torch_device == """cpu"""
				__lowerCAmelCase   :						List[Any]				= True

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.get_dummy_components()
				__lowerCAmelCase   :						str				= self.pipeline_class(**_lowercase)
				__lowerCAmelCase   :						List[str]				= pipe.to(_lowercase)
				pipe.set_progress_bar_config(disable=_lowercase)

				__lowerCAmelCase   :						Dict				= 1
				__lowerCAmelCase   :						Optional[int]				= 2

				__lowerCAmelCase   :						Tuple				= self.get_dummy_inputs(_lowercase)

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

				__lowerCAmelCase   :						Tuple				= pipe(**_lowercase ,    num_images_per_prompt=_lowercase)[0]

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






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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Optional[Any]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/shap_e/test_shap_e_np_out.npy")
				__lowerCAmelCase   :						Any				= ShapEPipeline.from_pretrained("openai/shap-e")
				__lowerCAmelCase   :						Any				= pipe.to(_lowercase)
				pipe.set_progress_bar_config(disable=_lowercase)

				__lowerCAmelCase   :						Tuple				= torch.Generator(device=_lowercase).manual_seed(0)

				__lowerCAmelCase   :						int				= pipe(
				    "a shark" ,    generator=_lowercase ,    guidance_scale=15.0 ,    num_inference_steps=64 ,    frame_size=64 ,    output_type="np" ,    ).images[0]

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

				assert_mean_pixel_difference(_lowercase ,    _lowercase)




"""simple docstring"""






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__snake_case							:     str   =					pytest.mark.integration


@pytest.mark.parametrize("path"      ,["paws", "csv"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			inspect_dataset(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"      ,["accuracy"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			inspect_metric(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.parametrize(
    "path, config_name, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						str				= get_dataset_config_info(__snake_case      ,config_name=__snake_case			)
			assert info.config_name == config_name
			assert list(info.splits.keys()			) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			with pytest.raises(__snake_case			):
						get_dataset_config_info(__snake_case      ,config_name=__snake_case			)


@pytest.mark.parametrize(
    "path, expected"      ,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						Dict				= get_dataset_config_names(__snake_case			)
			assert expected in config_names


@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"      ,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						List[Any]				= get_dataset_infos(__snake_case			)
			assert list(infos.keys()			) == expected_configs
			__lowerCAmelCase   :						List[Any]				= expected_configs[0]
			assert expected_config in infos
			__lowerCAmelCase   :						Tuple				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits_in_first_config


@pytest.mark.parametrize(
    "path, expected_config, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Tuple				= get_dataset_infos(__snake_case			)
			assert expected_config in infos
			__lowerCAmelCase   :						int				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			with pytest.raises(__snake_case			):
						get_dataset_split_names(__snake_case      ,config_name=__snake_case			)