





'''simple docstring'''

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__lowerCAmelCase				:      Union[str, Any]						      =logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCAmelCase				:      List[str]						      ="\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"






@dataclass
class   UpperCAmelCase      (			__snake_case							):
 __lowercase  						=							42






class   UpperCAmelCase      (			__snake_case							):
 def __init__(							self :Any		,			lowercase_ :PriorTransformer		,			lowercase_ :CLIPVisionModel		,			lowercase_ :CLIPImageProcessor		,			lowercase_ :HeunDiscreteScheduler		,			lowercase_ :ShapERenderer		,			)->				str:
     super().__init__()

     self.register_modules(
         prior=__lowerCamelCase		,			image_encoder=__lowerCamelCase		,			image_processor=__lowerCamelCase		,			scheduler=__lowerCamelCase		,			renderer=__lowerCamelCase		,			)
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Optional[int]		,			lowercase_ :Tuple		,			lowercase_ :Dict		,			lowercase_ :int		,			lowercase_ :str		,			lowercase_ :str				)->				int:







     if latents is None:
         A__							=     randn_tensor(__lowerCamelCase		,			generator=__lowerCamelCase		,			device=__lowerCamelCase		,			dtype=__lowerCamelCase				)
     else:
         if latents.shape != shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"				)
         A__							=     latents.to(__lowerCamelCase				)

     A__							=     latents * scheduler.init_noise_sigma
     return latents
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Dict=0				)->				Optional[int]:







     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError("Please install accelerate via `pip install accelerate`"				)

     A__							=     torch.device(F"cuda:{gpu_id}"				)

     A__							=     [self.image_encoder, self.prior]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(__lowerCamelCase		,			__lowerCamelCase				)
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				int:







     if self.device != torch.device("meta"				) or not hasattr(self.image_encoder		,			"_hf_hook"				):
         return self.device
     for module in self.image_encoder.modules():
         if (
             hasattr(__lowerCamelCase		,			"_hf_hook"				)
             and hasattr(module._hf_hook		,			"execution_device"				)
             and module._hf_hook.execution_device is not None
         ):
             return torch.device(module._hf_hook.execution_device				)
     return self.device
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :int		,			lowercase_ :List[str]		,			lowercase_ :int		,			lowercase_ :Union[str, Any]		,			)->				List[str]:







     if isinstance(__lowerCamelCase		,			__lowerCamelCase				) and isinstance(image[0]		,			torch.Tensor				):
         A__							=     torch.cat(__lowerCamelCase		,			axis=0				) if image[0].ndim == 4 else torch.stack(__lowerCamelCase		,			axis=0				)

     if not isinstance(__lowerCamelCase		,			torch.Tensor				):
         A__							=     self.image_processor(__lowerCamelCase		,			return_tensors="pt"				).pixel_values[0].unsqueeze(0				)

     A__							=     image.to(dtype=self.image_encoder.dtype		,			device=__lowerCamelCase				)

     A__							=     self.image_encoder(__lowerCamelCase				)["last_hidden_state"]
     A__							=     image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

     A__							=     image_embeds.repeat_interleave(__lowerCamelCase		,			dim=0				)

     if do_classifier_free_guidance:
         A__							=     torch.zeros_like(__lowerCamelCase				)

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         A__							=     torch.cat([negative_image_embeds, image_embeds]				)

     return image_embeds



 @torch.no_grad()
 @replace_example_docstring(__lowerCamelCase				)
 def __call__(							self :str		,			lowercase_ :Union[PIL.Image.Image, List[PIL.Image.Image]]		,			lowercase_ :int = 1		,			lowercase_ :int = 25		,			lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowercase_ :Optional[torch.FloatTensor] = None		,			lowercase_ :float = 4.0		,			lowercase_ :int = 64		,			lowercase_ :Optional[str] = "pil"		,			lowercase_ :bool = True		,			)->				Tuple:







     if isinstance(__lowerCamelCase		,			PIL.Image.Image				):
         A__							=     1
     elif isinstance(__lowerCamelCase		,			torch.Tensor				):
         A__							=     image.shape[0]
     elif isinstance(__lowerCamelCase		,			__lowerCamelCase				) and isinstance(image[0]		,			(torch.Tensor, PIL.Image.Image)				):
         A__							=     len(__lowerCamelCase				)
     else:
         raise ValueError(
             F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCamelCase				)}"				)

     A__							=     self._execution_device

     A__							=     batch_size * num_images_per_prompt

     A__							=     guidance_scale > 1.0
     A__							=     self._encode_image(__lowerCamelCase		,			__lowerCamelCase		,			__lowerCamelCase		,			__lowerCamelCase				)

     # prior

     self.scheduler.set_timesteps(__lowerCamelCase		,			device=__lowerCamelCase				)
     A__							=     self.scheduler.timesteps

     A__							=     self.prior.config.num_embeddings
     A__							=     self.prior.config.embedding_dim

     A__							=     self.prepare_latents(
         (batch_size, num_embeddings * embedding_dim)		,			image_embeds.dtype		,			__lowerCamelCase		,			__lowerCamelCase		,			__lowerCamelCase		,			self.scheduler		,			)

     # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
     A__							=     latents.reshape(latents.shape[0]		,			__lowerCamelCase		,			__lowerCamelCase				)

     for i, t in enumerate(self.progress_bar(__lowerCamelCase				)				):
         # expand the latents if we are doing classifier free guidance
         A__							=     torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
         A__							=     self.scheduler.scale_model_input(__lowerCamelCase		,			__lowerCamelCase				)

         A__							=     self.prior(
             __lowerCamelCase		,			timestep=__lowerCamelCase		,			proj_embedding=__lowerCamelCase		,			).predicted_image_embedding

         # remove the variance
         A__,      A__							=     noise_pred.split(
             scaled_model_input.shape[2]		,			dim=2				)  # batch_size, num_embeddings, embedding_dim

         if do_classifier_free_guidance is not None:
             A__,      A__							=     noise_pred.chunk(2				)
             A__							=     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

         A__							=     self.scheduler.step(
             __lowerCamelCase		,			timestep=__lowerCamelCase		,			sample=__lowerCamelCase		,			).prev_sample

     if output_type == "latent":
         return ShapEPipelineOutput(images=__lowerCamelCase				)

     A__							=     []
     for i, latent in enumerate(__lowerCamelCase				):
         print()
         A__							=     self.renderer.decode(
             latent[None, :]		,			__lowerCamelCase		,			size=__lowerCamelCase		,			ray_batch_size=40_96		,			n_coarse_samples=64		,			n_fine_samples=1_28		,			)

         images.append(__lowerCamelCase				)

     A__							=     torch.stack(__lowerCamelCase				)

     if output_type not in ["np", "pil"]:
         raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}"				)

     A__							=     images.cpu().numpy()

     if output_type == "pil":
         A__							=     [self.numpy_to_pil(__lowerCamelCase				) for image in images]

     # Offload last model to CPU
     if hasattr(self		,			"final_offload_hook"				) and self.final_offload_hook is not None:
         self.final_offload_hook.offload()

     if not return_dict:
         return (images,)

     return ShapEPipelineOutput(images=__lowerCamelCase				)







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__lowercase					:





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[int]=3       ,							__lowerCamelCase    :       Union[str, Any]=3_2       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       Dict=1_0       ,							__lowerCamelCase    :       Optional[int]=[1_0, 2_0, 3_0, 4_0]       ,							__lowerCamelCase    :       Optional[int]=[1, 1, 2, 1]       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       str=True       ,							__lowerCamelCase    :       str="relu"       ,							__lowerCamelCase    :       str=3       ,							__lowerCamelCase    :       int=None       ,							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  embeddings_size
         UpperCAmelCase      					=  hidden_sizes
         UpperCAmelCase      					=  depths
         UpperCAmelCase      					=  is_training
         UpperCAmelCase      					=  use_labels
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  num_labels
         UpperCAmelCase      					=  scope
         UpperCAmelCase      					=  len(__lowerCamelCase							)





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






         """simple docstring"""







         UpperCAmelCase      					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCAmelCase      					=  None
         if self.use_labels:
            UpperCAmelCase      					=  ids_tensor([self.batch_size]       ,							self.num_labels							)

         UpperCAmelCase      					=  self.get_config()

         return config, pixel_values, labels





      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return ResNetConfig(
             num_channels=self.num_channels       ,							embeddings_size=self.embeddings_size       ,							hidden_sizes=self.hidden_sizes       ,							depths=self.depths       ,							hidden_act=self.hidden_act       ,							num_labels=self.num_labels       ,							image_size=self.image_size       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Tuple							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModel(config=__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       int							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  TFResNetForImageClassification(__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.prepare_config_and_inputs()
         UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase      					=  config_and_inputs
         UpperCAmelCase      					=  {"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_tf
class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      UpperCamelCase										=							(
          {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(self       ,							config_class=__lowerCamelCase       ,							has_text_modality=__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         return





      @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         pass





      @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         pass





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






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  inspect.signature(model.call							)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase      					=  [*signature.parameters.keys()]

            UpperCAmelCase      					=  ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]							)     -> List[str]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       str       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

            UpperCAmelCase      					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase      					=  self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase							)       ,							expected_num_stages + 1							)

            # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]							)       ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,							)

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase      					=  ["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               UpperCAmelCase      					=  layer_type
               UpperCAmelCase      					=  True
               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase      					=  True

               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





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






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> List[str]:






         """simple docstring"""







         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  TFResNetModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)

def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_tf
@require_vision
class 	__lowercase					(	unittest.TestCase	):





      @cached_property
      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return (
             AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             if is_vision_available()
             else None
         )





      @slow
      def 							_lowercase					(		self    :       Dict							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

         UpperCAmelCase      					=  self.default_image_processor
         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""tf"""							)

         # forward pass
         UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,							__lowerCamelCase       ,							atol=1e-4							)							)



