

"""simple docstring"""


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      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = ShapEImgaImgPipeline
     _SCREAMING_SNAKE_CASE						:Tuple       = ["""image"""]
     _SCREAMING_SNAKE_CASE						:Any       = ["""image"""]
     _SCREAMING_SNAKE_CASE						:int       = [
         """num_images_per_prompt""",
         """num_inference_steps""",
         """generator""",
         """latents""",
         """guidance_scale""",
         """frame_size""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE						:Tuple       = False

     @property
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return 32

     @property
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           return 32

     @property
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           return self.time_input_dim * 4

     @property
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           return 8

     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           torch.manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size	,    image_size=64	,    projection_dim=self.text_embedder_hidden_size	,    intermediate_size=37	,    num_attention_heads=4	,    num_channels=3	,    num_hidden_layers=5	,    patch_size=1	,    )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	CLIPVisionModel(_a  )
           return model

     @property
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	CLIPImageProcessor(
               crop_size=224	,    do_center_crop=_a	,    do_normalize=_a	,    do_resize=_a	,    image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,    image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,    resample=3	,    size=224	,    )

           return image_processor

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


           """simple docstring"""


           torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{
               """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""",
               """embedding_proj_norm_type""": """layer""",
               """encoder_hid_proj_type""": None,
               """added_emb_type""": None,
           }

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	PriorTransformer(**_a  )
           return model

     @property
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
               """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__   :      List[str]      		=	ShapERenderer(**_a  )
           return model

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.dummy_prior
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.dummy_image_encoder
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_image_processor
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_renderer

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	HeunDiscreteScheduler(
               beta_schedule="""exp"""	,    num_train_timesteps=1_024	,    prediction_type="""sample"""	,    use_karras_sigmas=_a	,    clip_sample=_a	,    clip_sample_range=1.0	,    )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{
               """prior""": prior,
               """image_encoder""": image_encoder,
               """image_processor""": image_processor,
               """renderer""": renderer,
               """scheduler""": scheduler,
           }

           return components

     def 					_a			(						self	,    _a	,    _a=0  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(_a  )  ).to(_a  )

           if str(_a  ).startswith("""mps"""  ):
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.manual_seed(_a  )
           else:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.Generator(device=_a  ).manual_seed(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{
               """image""": input_image,
               """generator""": generator,
               """num_inference_steps""": 1,
               """frame_size""": 32,
               """output_type""": """np""",
           }
           return inputs

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.get_dummy_components()

           SCREAMING_SNAKE_CASE__   :      int      		=	self.pipeline_class(**_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe.to(_a  )

           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	pipe(**self.get_dummy_inputs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	output.images[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image[0, -3:, -3:, -1]

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

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	np.array(
               [
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
               ]  )

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

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


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

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	torch_device == """cpu"""
           SCREAMING_SNAKE_CASE__   :      Dict      		=	True
           self._test_inference_batch_single_identical(
               batch_size=2	,    test_max_difference=_a	,    relax_max_difference=_a	,    )





     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_dummy_components()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.pipeline_class(**_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	pipe.to(_a  )
           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	1
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	2

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_dummy_inputs(_a  )

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

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe(**_a	,    num_images_per_prompt=_a  )[0]

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





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






     '''simple docstring'''

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


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





     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/shap_e/test_shap_e_img2img_out.npy"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	pipe.to(_a  )
           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.Generator(device=_a  ).manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe(
               _a	,    generator=_a	,    guidance_scale=3.0	,    num_inference_steps=64	,    frame_size=64	,    output_type="""np"""	,    ).images[0]

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

           assert_mean_pixel_difference(_a	,    _a  )





"""simple docstring"""


from typing import TYPE_CHECKING

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


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  a					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




