
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 					lowercase__(							_a					,		unittest.TestCase				):



	"""simple docstring"""




	a     :List[str]       	=      ShapEImgaImgPipeline
	a     :Tuple       	=      ['image']
	a     :Union[str, Any]       	=      ['image']
	a     :List[str]       	=      [
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	a     :Tuple       	=      False
	@property
	def 		_lowercase    (			self    :     List[str]			)			->	Optional[int]:
					return 3_2
	@property
	def 		_lowercase    (			self    :     Dict			)			->	str:
					return 3_2
	@property
	def 		_lowercase    (			self    :     Dict			)			->	Any:
					return self.time_input_dim * 4
	@property
	def 		_lowercase    (			self    :     List[str]			)			->	Tuple:
					return 8
	@property
	def 		_lowercase    (			self    :     Optional[int]			)			->	Any:
					torch.manual_seed(0			)
					lowercase_	    =   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			,				)

					lowercase_	    =   CLIPVisionModel(_SCREAMING_SNAKE_CASE			)
					return model
	@property
	def 		_lowercase    (			self    :     List[Any]			)			->	Tuple:
					lowercase_	    =   CLIPImageProcessor(
					    crop_size=2_2_4			,				do_center_crop=_SCREAMING_SNAKE_CASE			,				do_normalize=_SCREAMING_SNAKE_CASE			,				do_resize=_SCREAMING_SNAKE_CASE			,				image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,				image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,				resample=3			,				size=2_2_4			,				)

					return image_processor
	@property
	def 		_lowercase    (			self    :     Any			)			->	Optional[Any]:
					torch.manual_seed(0			)

					lowercase_	    =   {
					    '''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,
					}

					lowercase_	    =   PriorTransformer(**_SCREAMING_SNAKE_CASE			)
					return model
	@property
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					torch.manual_seed(0			)

					lowercase_	    =   {
					    '''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,
					    ),
					}
					lowercase_	    =   ShapERenderer(**_SCREAMING_SNAKE_CASE			)
					return model
	def 		_lowercase    (			self    :     Any			)			->	Optional[Any]:
					lowercase_	    =   self.dummy_prior
					lowercase_	    =   self.dummy_image_encoder
					lowercase_	    =   self.dummy_image_processor
					lowercase_	    =   self.dummy_renderer

					lowercase_	    =   HeunDiscreteScheduler(
					    beta_schedule='''exp'''			,				num_train_timesteps=1_0_2_4			,				prediction_type='''sample'''			,				use_karras_sigmas=_SCREAMING_SNAKE_CASE			,				clip_sample=_SCREAMING_SNAKE_CASE			,				clip_sample_range=1.0			,				)
					lowercase_	    =   {
					    '''prior''': prior,
					    '''image_encoder''': image_encoder,
					    '''image_processor''': image_processor,
					    '''renderer''': renderer,
					    '''scheduler''': scheduler,
					}

					return components
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int=0			)			->	Any:
					lowercase_	    =   floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)

					if str(_SCREAMING_SNAKE_CASE			).startswith('''mps'''			):
									lowercase_	    =   torch.manual_seed(_SCREAMING_SNAKE_CASE			)
					else:
									lowercase_	    =   torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
					lowercase_	    =   {
					    '''image''': input_image,
					    '''generator''': generator,
					    '''num_inference_steps''': 1,
					    '''frame_size''': 3_2,
					    '''output_type''': '''np''',
					}
					return inputs
	def 		_lowercase    (			self    :     List[str]			)			->	Tuple:
					lowercase_	    =   '''cpu'''

					lowercase_	    =   self.get_dummy_components()

					lowercase_	    =   self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
					lowercase_	    =   pipe.to(_SCREAMING_SNAKE_CASE			)

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

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

					lowercase_	    =   np.array(
					    [
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					    ]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]			)
	def 		_lowercase    (			self    :     Optional[int]			)			->	Optional[Any]:
					lowercase_	    =   torch_device == '''cpu'''
					lowercase_	    =   True
					self._test_inference_batch_single_identical(
					    batch_size=2			,				test_max_difference=_SCREAMING_SNAKE_CASE			,				relax_max_difference=_SCREAMING_SNAKE_CASE			,				)




	def 		_lowercase    (			self    :     Dict			)			->	str:
					lowercase_	    =   self.get_dummy_components()
					lowercase_	    =   self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
					lowercase_	    =   pipe.to(_SCREAMING_SNAKE_CASE			)
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

					lowercase_	    =   1
					lowercase_	    =   2

					lowercase_	    =   self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)

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

					lowercase_	    =   pipe(**_SCREAMING_SNAKE_CASE			,				num_images_per_prompt=_SCREAMING_SNAKE_CASE			)[0]

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

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



	"""simple docstring"""




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




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''			)
					lowercase_	    =   load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/shap_e/test_shap_e_img2img_out.npy'''			)
					lowercase_	    =   ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''			)
					lowercase_	    =   pipe.to(_SCREAMING_SNAKE_CASE			)
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

					lowercase_	    =   torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(0			)

					lowercase_	    =   pipe(
					    _SCREAMING_SNAKE_CASE			,				generator=_SCREAMING_SNAKE_CASE			,				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(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)