

'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

_lowercase      :      List[str]            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      Tuple					):




						'''simple docstring'''






						if isinstance(lowercase__				,       (list, tuple)					) and isinstance(videos[0]				,       (list, tuple)					) and is_valid_image(videos[0][0]					):
												return videos

						elif isinstance(lowercase__				,       (list, tuple)					) and is_valid_image(videos[0]					):
												return [videos]

						elif is_valid_image(lowercase__					):
												return [[videos]]

						raise ValueError(f"""Could not make batched video from {videos}"""					)






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Tuple  		=	["pixel_values"]




				def __init__(		self      :			int				,   lowerCAmelCase      :			List[Any] = True				,   lowerCAmelCase      :			List[Any] = None				,   lowerCAmelCase      :			Union[str, Any] = PILImageResampling.BILINEAR				,   lowerCAmelCase      :			Dict = True				,   lowerCAmelCase      :			str = None				,   lowerCAmelCase      :			str = True				,   lowerCAmelCase      :			List[str] = 1 / 255				,   lowerCAmelCase      :			Optional[int] = True				,   lowerCAmelCase      :			Optional[int] = True				,   lowerCAmelCase      :			Dict = None				,   lowerCAmelCase      :			Optional[int] = None				,   **lowerCAmelCase      :			Optional[Any]				,   )->					Optional[int]:

										"""simple docstring"""




										super().__init__(**lowerCAmelCase				)
										UpperCAmelCase					    =     size if size is not None else {'shortest_edge': 256}
										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   default_to_square=lowerCAmelCase				)
										UpperCAmelCase					    =     crop_size if crop_size is not None else {'height': 224, 'width': 224}
										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   param_name='''crop_size'''				)

										UpperCAmelCase					    =     do_resize
										UpperCAmelCase					    =     size
										UpperCAmelCase					    =     do_center_crop
										UpperCAmelCase					    =     crop_size
										UpperCAmelCase					    =     resample
										UpperCAmelCase					    =     do_rescale
										UpperCAmelCase					    =     rescale_factor
										UpperCAmelCase					    =     offset
										UpperCAmelCase					    =     do_normalize
										UpperCAmelCase					    =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase					    =     image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			int = PILImageResampling.BILINEAR				,   lowerCAmelCase      :			Dict = None				,   **lowerCAmelCase      :			List[Any]				,   )->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   default_to_square=lowerCAmelCase				)
										if "shortest_edge" in size:
																UpperCAmelCase					    =     get_resize_output_image_size(lowerCAmelCase				,   size['''shortest_edge''']				,   default_to_square=lowerCAmelCase				)
										elif "height" in size and "width" in size:
																UpperCAmelCase					    =     (size['height'], size['width'])
										else:
																raise ValueError(F"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""				)
										return resize(lowerCAmelCase				,   size=lowerCAmelCase				,   resample=lowerCAmelCase				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Any = None				,   **lowerCAmelCase      :			int				,   )->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				)
										if "height" not in size or "width" not in size:
																raise ValueError(F"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}"""				)
										return center_crop(lowerCAmelCase				,   size=(size['''height'''], size['''width'''])				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			List[str] = True				,   lowerCAmelCase      :			Union[str, Any] = None				,   **lowerCAmelCase      :			Optional[Any]				,   )->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     image.astype(np.floataa				)
										if offset:
																UpperCAmelCase					    =     image - (scale / 2)
										return rescale(lowerCAmelCase				,   scale=lowerCAmelCase				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Dict = None				,   **lowerCAmelCase      :			int				,   )->					List[str]:

										"""simple docstring"""




										return normalize(lowerCAmelCase				,   mean=lowerCAmelCase				,   std=lowerCAmelCase				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[Any] = None				,   lowerCAmelCase      :			Optional[Any] = None				,   lowerCAmelCase      :			int = None				,   lowerCAmelCase      :			List[str] = None				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			Any = None				,   lowerCAmelCase      :			Dict = None				,   lowerCAmelCase      :			Tuple = None				,   lowerCAmelCase      :			Dict = None				,   lowerCAmelCase      :			List[str] = None				,   lowerCAmelCase      :			int = ChannelDimension.FIRST				,   )->					int:

										"""simple docstring"""




										if do_resize and size is None or resample is None:
																raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

										if do_center_crop and crop_size is None:
																raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

										if offset and not do_rescale:
																raise ValueError('''For offset, do_rescale must also be set to True.'''				)

										# All transformations expect numpy arrays.
										UpperCAmelCase					    =     to_numpy_array(lowerCAmelCase				)

										if do_resize:
																UpperCAmelCase					    =     self.resize(image=lowerCAmelCase				,   size=lowerCAmelCase				,   resample=lowerCAmelCase				)

										if do_center_crop:
																UpperCAmelCase					    =     self.center_crop(lowerCAmelCase				,   size=lowerCAmelCase				)

										if do_rescale:
																UpperCAmelCase					    =     self.rescale(image=lowerCAmelCase				,   scale=lowerCAmelCase				,   offset=lowerCAmelCase				)

										if do_normalize:
																UpperCAmelCase					    =     self.normalize(image=lowerCAmelCase				,   mean=lowerCAmelCase				,   std=lowerCAmelCase				)

										UpperCAmelCase					    =     to_channel_dimension_format(lowerCAmelCase				,   lowerCAmelCase				)
										return image





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			List[str] = None				,   lowerCAmelCase      :			str = None				,   lowerCAmelCase      :			Union[str, Any] = None				,   lowerCAmelCase      :			Optional[Any] = None				,   lowerCAmelCase      :			Tuple = None				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			List[Any] = None				,   lowerCAmelCase      :			Tuple = None				,   lowerCAmelCase      :			Union[str, Any] = None				,   lowerCAmelCase      :			Any = None				,   lowerCAmelCase      :			List[Any] = None				,   lowerCAmelCase      :			List[str] = None				,   lowerCAmelCase      :			Dict = ChannelDimension.FIRST				,   **lowerCAmelCase      :			List[Any]				,   )->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase					    =     resample if resample is not None else self.resample
										UpperCAmelCase					    =     do_center_crop if do_center_crop is not None else self.do_center_crop
										UpperCAmelCase					    =     do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase					    =     rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase					    =     offset if offset is not None else self.offset
										UpperCAmelCase					    =     do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase					    =     image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase					    =     image_std if image_std is not None else self.image_std

										UpperCAmelCase					    =     size if size is not None else self.size
										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   default_to_square=lowerCAmelCase				)
										UpperCAmelCase					    =     crop_size if crop_size is not None else self.crop_size
										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   param_name='''crop_size'''				)

										if not valid_images(lowerCAmelCase				):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

										UpperCAmelCase					    =     make_batched(lowerCAmelCase				)

										UpperCAmelCase					    =     [
										    [
										        self._preprocess_image(
										            image=lowerCAmelCase				,   do_resize=lowerCAmelCase				,   size=lowerCAmelCase				,   resample=lowerCAmelCase				,   do_center_crop=lowerCAmelCase				,   crop_size=lowerCAmelCase				,   do_rescale=lowerCAmelCase				,   rescale_factor=lowerCAmelCase				,   offset=lowerCAmelCase				,   do_normalize=lowerCAmelCase				,   image_mean=lowerCAmelCase				,   image_std=lowerCAmelCase				,   data_format=lowerCAmelCase				,   )
										        for img in video
										    ]
										    for video in videos
										]

										UpperCAmelCase					    =     {'pixel_values': videos}
										return BatchFeature(data=lowerCAmelCase				,   tensor_type=lowerCAmelCase				)









'''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 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				Any  		=	ShapEPipeline
				__magic_name__    :				Tuple  		=	["prompt"]
				__magic_name__    :				Optional[int]  		=	["prompt"]
				__magic_name__    :				Dict  		=	[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				__magic_name__    :				Optional[int]  		=	False




				@property
				def 					a__(		self      :			Optional[Any]				)->					Dict:

										"""simple docstring"""




										return 32




				@property
				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										return 32




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

										"""simple docstring"""




										return self.time_input_dim * 4




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

										"""simple docstring"""




										return 8




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

										"""simple docstring"""




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




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

										"""simple docstring"""




										torch.manual_seed(0				)
										UpperCAmelCase					    =     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(lowerCAmelCase				)




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

										"""simple docstring"""




										torch.manual_seed(0				)

										UpperCAmelCase					    =     {
										    '''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,
										}

										UpperCAmelCase					    =     PriorTransformer(**lowerCAmelCase				)
										return model




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

										"""simple docstring"""




										torch.manual_seed(0				)

										UpperCAmelCase					    =     {
										    '''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,
										    ),
										}
										UpperCAmelCase					    =     ShapERenderer(**lowerCAmelCase				)
										return model




				def 					a__(		self      :			Any				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.dummy_prior
										UpperCAmelCase					    =     self.dummy_text_encoder
										UpperCAmelCase					    =     self.dummy_tokenizer
										UpperCAmelCase					    =     self.dummy_renderer

										UpperCAmelCase					    =     HeunDiscreteScheduler(
										    beta_schedule='''exp'''				,   num_train_timesteps=1024				,   prediction_type='''sample'''				,   use_karras_sigmas=lowerCAmelCase				,   clip_sample=lowerCAmelCase				,   clip_sample_range=1.0				,   )
										UpperCAmelCase					    =     {
										    '''prior''': prior,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''renderer''': renderer,
										    '''scheduler''': scheduler,
										}

										return components




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			List[Any]=0				)->					Optional[Any]:

										"""simple docstring"""




										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''num_inference_steps''': 1,
										    '''frame_size''': 32,
										    '''output_type''': '''np''',
										}
										return inputs




				def 					a__(		self      :			int				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     '''cpu'''

										UpperCAmelCase					    =     self.get_dummy_components()

										UpperCAmelCase					    =     self.pipeline_class(**lowerCAmelCase				)
										UpperCAmelCase					    =     pipe.to(lowerCAmelCase				)

										pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     pipe(**self.get_dummy_inputs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     output.images[0]
										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

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

										UpperCAmelCase					    =     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 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




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




				def 					a__(		self      :			Optional[int]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     torch_device == '''cpu'''
										UpperCAmelCase					    =     True

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





				def 					a__(		self      :			int				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_dummy_components()
										UpperCAmelCase					    =     self.pipeline_class(**lowerCAmelCase				)
										UpperCAmelCase					    =     pipe.to(lowerCAmelCase				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     2

										UpperCAmelCase					    =     self.get_dummy_inputs(lowerCAmelCase				)

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

										UpperCAmelCase					    =     pipe(**lowerCAmelCase				,   num_images_per_prompt=lowerCAmelCase				)[0]

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







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




				def 					a__(		self      :			Dict				)->					Union[str, Any]:

										"""simple docstring"""




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





				def 					a__(		self      :			Optional[Any]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/shap_e/test_shap_e_np_out.npy'''				)
										UpperCAmelCase					    =     ShapEPipeline.from_pretrained('''openai/shap-e'''				)
										UpperCAmelCase					    =     pipe.to(lowerCAmelCase				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(0				)

										UpperCAmelCase					    =     pipe(
										    '''a shark'''				,   generator=lowerCAmelCase				,   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(lowerCAmelCase				,   lowerCAmelCase				)





