




'''simple docstring'''







import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_MAPPING,
       SegformerForImageClassification,
       SegformerForSemanticSegmentation,
       SegformerModel,
   )
   from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import SegformerImageProcessor


class 			_a  (	__a ):
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.config_class(**self.inputs_dict				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''hidden_sizes'''				)				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''num_attention_heads'''				)				)
           self.parent.assertTrue(hasattr(lowercase   ,				'''num_encoder_blocks'''				)				)






class 			_a  :
       def __init__(	self						:	Optional[Any]   ,				lowercase						:	Any   ,				lowercase						:	Optional[Any]=13   ,				lowercase						:	Any=64   ,				lowercase						:	Any=3   ,				lowercase						:	int=4   ,				lowercase						:	Any=[2, 2, 2, 2]   ,				lowercase						:	Optional[int]=[8, 4, 2, 1]   ,				lowercase						:	int=[16, 32, 64, 128]   ,				lowercase						:	Dict=[1, 4, 8, 16]   ,				lowercase						:	List[Any]=[1, 2, 4, 8]   ,				lowercase						:	Optional[Any]=True   ,				lowercase						:	Tuple=True   ,				lowercase						:	Tuple="gelu"   ,				lowercase						:	Any=0.1   ,				lowercase						:	Optional[int]=0.1   ,				lowercase						:	Optional[Any]=0.02   ,				lowercase						:	Optional[int]=3   ,				lowercase						:	Optional[int]=None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		image_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		num_encoder_blocks
           UpperCAmelCase			       =		sr_ratios
           UpperCAmelCase			       =		depths
           UpperCAmelCase			       =		hidden_sizes
           UpperCAmelCase			       =		downsampling_rates
           UpperCAmelCase			       =		num_attention_heads
           UpperCAmelCase			       =		is_training
           UpperCAmelCase			       =		use_labels
           UpperCAmelCase			       =		hidden_act
           UpperCAmelCase			       =		hidden_dropout_prob
           UpperCAmelCase			       =		attention_probs_dropout_prob
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		num_labels
           UpperCAmelCase			       =		scope
       def     A      (	self						:	List[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.image_size, self.image_size]   ,				self.num_labels				)

           UpperCAmelCase			       =		self.get_config()
           return config, pixel_values, labels
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           return SegformerConfig(
               image_size=self.image_size   ,				num_channels=self.num_channels   ,				num_encoder_blocks=self.num_encoder_blocks   ,				depths=self.depths   ,				hidden_sizes=self.hidden_sizes   ,				num_attention_heads=self.num_attention_heads   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				initializer_range=self.initializer_range   ,				)
       def     A      (	self						:	Any   ,				lowercase						:	Optional[int]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerModel(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           UpperCAmelCase			       =		UpperCAmelCase			       =		self.image_size // (self.downsampling_rates[-1] * 2)
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Dict   ,				lowercase						:	Dict   ,				lowercase						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		SegformerForSemanticSegmentation(lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(
               result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
           UpperCAmelCase			       =		model(lowercase   ,				labels=lowercase				)
           self.parent.assertEqual(
               result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
           self.parent.assertGreater(result.loss   ,				0.0				)
       def     A      (	self						:	Dict   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Tuple   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		SegformerForSemanticSegmentation(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		torch.randint(0   ,				1   ,				(self.batch_size, self.image_size, self.image_size)				).to(lowercase				)
           UpperCAmelCase			       =		model(lowercase   ,				labels=lowercase				)
           self.parent.assertGreater(result.loss   ,				0.0				)







       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

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






@require_torch
class 			_a  (	__a							,							__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					(
           (
               SegformerModel,
               SegformerForSemanticSegmentation,
               SegformerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       __a				:     Dict   				=					(
           {
               """feature-extraction""": SegformerModel,
               """image-classification""": SegformerForImageClassification,
               """image-segmentation""": SegformerForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __a				:     Union[str, Any]   				=					True
       __a				:     str   				=					False
       __a				:     Dict   				=					False
       __a				:     int   				=					False
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerModelTester(self				)
           UpperCAmelCase			       =		SegformerConfigTester(self   ,				config_class=lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_segmentation(*lowercase				)
       @unittest.skip('''SegFormer does not use inputs_embeds'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass
       @unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase			       =		['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,				lowercase				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		True

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		False
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()
               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)
               UpperCAmelCase			       =		outputs.attentions

               UpperCAmelCase			       =		sum(self.model_tester.depths				)
               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()
               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)
               UpperCAmelCase			       =		outputs.attentions

               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # verify the first attentions (first block, first layer)
               UpperCAmelCase			       =		(self.model_tester.image_size // 4) ** 2
               UpperCAmelCase			       =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
               self.assertListEqual(
                   list(attentions[0].shape[-3:]				)   ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,				)

               # verify the last attentions (last block, last layer)
               UpperCAmelCase			       =		(self.model_tester.image_size // 32) ** 2
               UpperCAmelCase			       =		(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
               self.assertListEqual(
                   list(attentions[-1].shape[-3:]				)   ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,				)
               UpperCAmelCase			       =		len(lowercase				)

               # Check attention is always last and order is fine
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		True
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()
               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)

               self.assertEqual(out_len + 1   ,				len(lowercase				)				)

               UpperCAmelCase			       =		outputs.attentions

               self.assertEqual(len(lowercase				)   ,				lowercase				)
               # verify the first attentions (first block, first layer)
               UpperCAmelCase			       =		(self.model_tester.image_size // 4) ** 2
               UpperCAmelCase			       =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]				)   ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           def check_hidden_states_output(lowercase						:	Dict   ,				lowercase						:	List[str]   ,				lowercase						:	Optional[Any]				):
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.eval()

               with torch.no_grad():
                   UpperCAmelCase			       =		model(**self._prepare_for_class(lowercase   ,				lowercase				)				)

               UpperCAmelCase			       =		outputs.hidden_states

               UpperCAmelCase			       =		self.model_tester.num_encoder_blocks
               self.assertEqual(len(lowercase				)   ,				lowercase				)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]				)   ,				[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]   ,				)

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		True
               check_hidden_states_output(lowercase   ,				lowercase   ,				lowercase				)

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

               check_hidden_states_output(lowercase   ,				lowercase   ,				lowercase				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowercase				):
                   continue

               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.train()
               UpperCAmelCase			       =		self._prepare_for_class(lowercase   ,				lowercase   ,				return_labels=lowercase				)
               UpperCAmelCase			       =		model(**lowercase				).loss
               loss.backward()
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           pass







       @slow
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase			       =		SegformerModel.from_pretrained(lowercase				)
               self.assertIsNotNone(lowercase				)






def 						snake_case_							():
    UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image







@require_torch
class 			_a  (	unittest.TestCase ):
       @slow
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerImageProcessor(
               image_scale=(512, 512)   ,				keep_ratio=lowercase   ,				align=lowercase   ,				do_random_crop=lowercase				)
           UpperCAmelCase			       =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''				).to(
               lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				)
           UpperCAmelCase			       =		encoded_inputs.pixel_values.to(lowercase				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)

           UpperCAmelCase			       =		torch.Size((1, model.config.num_labels, 128, 128)				)
           self.assertEqual(outputs.logits.shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor(
               [
                   [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                   [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                   [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
               ]				).to(lowercase				)
           self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,				lowercase   ,				atol=1E-4				)				)
       @slow
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerImageProcessor(
               image_scale=(512, 512)   ,				keep_ratio=lowercase   ,				align=lowercase   ,				do_random_crop=lowercase				)
           UpperCAmelCase			       =		SegformerForSemanticSegmentation.from_pretrained(
               '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''				).to(lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				)
           UpperCAmelCase			       =		encoded_inputs.pixel_values.to(lowercase				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)

           UpperCAmelCase			       =		torch.Size((1, model.config.num_labels, 128, 128)				)
           self.assertEqual(outputs.logits.shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor(
               [
                   [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                   [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                   [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
               ]				).to(lowercase				)
           self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,				lowercase   ,				atol=1E-1				)				)







       @slow
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		SegformerImageProcessor(
               image_scale=(512, 512)   ,				keep_ratio=lowercase   ,				align=lowercase   ,				do_random_crop=lowercase				)
           UpperCAmelCase			       =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''				).to(
               lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				)
           UpperCAmelCase			       =		encoded_inputs.pixel_values.to(lowercase				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)

           UpperCAmelCase			       =		outputs.logits.detach().cpu()

           UpperCAmelCase			       =		image_processor.post_process_semantic_segmentation(outputs=lowercase   ,				target_sizes=[(500, 300)]				)
           UpperCAmelCase			       =		torch.Size((500, 300)				)
           self.assertEqual(segmentation[0].shape   ,				lowercase				)

           UpperCAmelCase			       =		image_processor.post_process_semantic_segmentation(outputs=lowercase				)
           UpperCAmelCase			       =		torch.Size((128, 128)				)
           self.assertEqual(segmentation[0].shape   ,				lowercase				)












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


__UpperCAmelCase								=						logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCAmelCase								=						'\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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[PIL.Image.Image, np.ndarray]





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Any:
							super().__init__()

							self.register_modules(
							    prior=_UpperCamelCase				,		image_encoder=_UpperCamelCase				,		image_processor=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		renderer=_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							if latents is None:
												UpperCAmelCase_    :      str            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )
							else:
												if latents.shape != shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
												UpperCAmelCase_    :      Tuple            =						latents.to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						latents * scheduler.init_noise_sigma
							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Union[str, Any]:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

							UpperCAmelCase_    :      int            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      int            =						[self.image_encoder, self.prior]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(_UpperCamelCase				,		_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							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(_UpperCamelCase				,		'_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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							str:
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		torch.Tensor   ):
												UpperCAmelCase_    :      int            =						torch.cat(_UpperCamelCase				,		axis=0   ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase				,		axis=0   )

							if not isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      Optional[int]            =						self.image_processor(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values[0].unsqueeze(0   )

							UpperCAmelCase_    :      Tuple            =						image.to(dtype=self.image_encoder.dtype				,		device=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.image_encoder(_UpperCamelCase   )['last_hidden_state']
							UpperCAmelCase_    :      Union[str, Any]            =						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

							UpperCAmelCase_    :      List[str]            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      Dict            =						torch.zeros_like(_UpperCamelCase   )

												# 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
												UpperCAmelCase_    :      Optional[int]            =						torch.cat([negative_image_embeds, image_embeds]   )

							return image_embeds



		@torch.no_grad()
		@replace_example_docstring(_UpperCamelCase   )
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_5				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 6_4				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							Union[str, Any]:

							if isinstance(_UpperCamelCase				,		PIL.Image.Image   ):
												UpperCAmelCase_    :      Tuple            =						1
							elif isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      str            =						image.shape[0]
							elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		(torch.Tensor, PIL.Image.Image)   ):
												UpperCAmelCase_    :      Optional[int]            =						len(_UpperCamelCase   )
							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(_UpperCamelCase   )}"   )

							UpperCAmelCase_    :      Tuple            =						self._execution_device

							UpperCAmelCase_    :      str            =						batch_size * num_images_per_prompt

							UpperCAmelCase_    :      str            =						guidance_scale > 1.0
							UpperCAmelCase_    :      str            =						self._encode_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							# prior

							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.scheduler.timesteps

							UpperCAmelCase_    :      int            =						self.prior.config.num_embeddings
							UpperCAmelCase_    :      Any            =						self.prior.config.embedding_dim

							UpperCAmelCase_    :      List[str]            =						self.prepare_latents(
							    (batch_size, num_embeddings * embedding_dim)				,		image_embeds.dtype				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		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
							UpperCAmelCase_    :      List[Any]            =						latents.reshape(latents.shape[0]				,		_UpperCamelCase				,		_UpperCamelCase   )

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

												UpperCAmelCase_    :      int            =						self.prior(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		proj_embedding=_UpperCamelCase				,		).predicted_image_embedding

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

												if do_classifier_free_guidance is not None:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_    :      List[Any]            =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

												UpperCAmelCase_    :      List[str]            =						self.scheduler.step(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		sample=_UpperCamelCase				,		).prev_sample

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

							UpperCAmelCase_    :      List[Any]            =						[]
							for i, latent in enumerate(_UpperCamelCase   ):
												print()
												UpperCAmelCase_    :      List[str]            =						self.renderer.decode(
												    latent[None, :]				,		_UpperCamelCase				,		size=_UpperCamelCase				,		ray_batch_size=4_0_9_6				,		n_coarse_samples=6_4				,		n_fine_samples=1_2_8				,		)

												images.append(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						torch.stack(_UpperCamelCase   )

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

							UpperCAmelCase_    :      Dict            =						images.cpu().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[str]            =						[self.numpy_to_pil(_UpperCamelCase   ) 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=_UpperCamelCase   )






