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 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		ShapEPipeline
						UpperCamelCase_     =		["""prompt"""]
						UpperCamelCase_     =		["""prompt"""]
						UpperCamelCase_     =		[
						    """num_images_per_prompt""",
						    """num_inference_steps""",
						    """generator""",
						    """latents""",
						    """guidance_scale""",
						    """frame_size""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase_     =		False


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												return 32


						@property
						def        __A       (       self	:				Any						):





												'''simple docstring'''





												return 32


						@property
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												return self.time_input_dim * 4


						@property
						def        __A       (       self	:				Any						):





												'''simple docstring'''





												return 8


						@property
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)
												return tokenizer


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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(UpperCamelCase__						)


						@property
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												torch.manual_seed(0						)

												SCREAMING_SNAKE_CASE							:    Any							=							{
												    '''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,
												}

												SCREAMING_SNAKE_CASE							:    List[str]							=							PriorTransformer(**UpperCamelCase__						)
												return model


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												torch.manual_seed(0						)

												SCREAMING_SNAKE_CASE							:    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,
												    ),
												}
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							ShapERenderer(**UpperCamelCase__						)
												return model


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.dummy_prior
												SCREAMING_SNAKE_CASE							:    Dict							=							self.dummy_text_encoder
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.dummy_tokenizer
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.dummy_renderer

												SCREAMING_SNAKE_CASE							:    int							=							HeunDiscreteScheduler(
												    beta_schedule='''exp'''    ,		num_train_timesteps=1024    ,		prediction_type='''sample'''    ,		use_karras_sigmas=UpperCamelCase__    ,		clip_sample=UpperCamelCase__    ,		clip_sample_range=1.0    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							{
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Dict=0						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''cpu'''

												SCREAMING_SNAKE_CASE							:    Any							=							self.get_dummy_components()

												SCREAMING_SNAKE_CASE							:    Dict							=							self.pipeline_class(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							pipe.to(UpperCamelCase__						)

												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipe(**self.get_dummy_inputs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    Any							=							output.images[0]
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							image[0, -3:, -3:, -1]

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

												SCREAMING_SNAKE_CASE							:    Optional[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        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							torch_device == '''cpu'''
												SCREAMING_SNAKE_CASE							:    int							=							True

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



						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.pipeline_class(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							pipe.to(UpperCamelCase__						)
												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Any							=							1
												SCREAMING_SNAKE_CASE							:    Dict							=							2

												SCREAMING_SNAKE_CASE							:    Any							=							self.get_dummy_inputs(UpperCamelCase__						)

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

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipe(**UpperCamelCase__    ,		num_images_per_prompt=UpperCamelCase__						)[0]

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






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


						def        __A       (       self	:				str						):





												'''simple docstring'''





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



						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy'''						)
												SCREAMING_SNAKE_CASE							:    int							=							ShapEPipeline.from_pretrained('''openai/shap-e'''						)
												SCREAMING_SNAKE_CASE							:    int							=							pipe.to(UpperCamelCase__						)
												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.Generator(device=UpperCamelCase__						).manual_seed(0						)

												SCREAMING_SNAKE_CASE							:    int							=							pipe(
												    '''a shark'''    ,		generator=UpperCamelCase__    ,		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(UpperCamelCase__    ,		UpperCamelCase__						)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





