import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)






class 		a__						(					A__					):

					def __init__(    self      :       Tuple,_A      :       Optional[int],_A      :       Any=13,_A      :       List[str]=7,_A      :       int=True,_A      :       Dict=True,_A      :       Dict=False,_A      :       List[Any]=True,_A      :       Any=99,_A      :       Optional[int]=32,_A      :       Any=5,_A      :       List[Any]=4,_A      :       Dict=64,_A      :       Optional[Any]="gelu",_A      :       Tuple=0.1,_A      :       Any=0.1,_A      :       List[Any]=512,_A      :       Dict=16,_A      :       Optional[Any]=2,_A      :       Union[str, Any]=0.02,_A      :       List[str]=3,_A      :       Optional[Any]=4,_A      :       Union[str, Any]=None,_A      :       Tuple=2,_A      :       List[str]=2,_A      :       str=2,_A      :       Dict=2,_A      :       Optional[Any]=4,_A      :       Union[str, Any]=1,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Dict				      =    is_training
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_input_mask
							SCREAMING_SNAKE_CASE_  :		int				      =    use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		str				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		str				      =    type_vocab_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_labels
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_choices
							SCREAMING_SNAKE_CASE_  :		Dict				      =    scope
							SCREAMING_SNAKE_CASE_  :		int				      =    q_groups
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    k_groups
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    v_groups
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    post_attention_groups
							SCREAMING_SNAKE_CASE_  :		int				      =    intermediate_groups
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    output_groups

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    random_attention_mask([self.batch_size, self.seq_length]    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    None
							SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		str				      =    None
							if self.use_labels:
									SCREAMING_SNAKE_CASE_  :		str				      =    ids_tensor([self.batch_size],self.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.num_labels    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ids_tensor([self.batch_size],self.num_choices    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							return SqueezeBertConfig(
							    embedding_size=self.hidden_size,vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,attention_probs_dropout_prob=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,q_groups=self.q_groups,k_groups=self.k_groups,v_groups=self.v_groups,post_attention_groups=self.post_attention_groups,intermediate_groups=self.intermediate_groups,output_groups=self.output_groups,)

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       int,_A      :       Optional[int],_A      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    SqueezeBertModel(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any,_A      :       Tuple,_A      :       str,_A      :       Any,_A      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    SqueezeBertForMaskedLM(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       Any,_A      :       Tuple,_A      :       int,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SqueezeBertForQuestionAnswering(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(
							    _A,attention_mask=_A,start_positions=_A,end_positions=_A    )
							self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length)    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       List[str],_A      :       Tuple,_A      :       List[Any],_A      :       List[str],_A      :       List[str],_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    SqueezeBertForSequenceClassification(_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Optional[int],_A      :       str,_A      :       List[Any],_A      :       List[str],_A      :       str,_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    SqueezeBertForTokenClassification(config=_A    )
							model.to(_A    )
							model.eval()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Tuple,_A      :       str,_A      :       Optional[Any],_A      :       int,_A      :       str,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.num_choices
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SqueezeBertForMultipleChoice(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    input_ids.unsqueeze(1    ).expand(-1,self.num_choices,-1    ).contiguous()
							SCREAMING_SNAKE_CASE_  :		str				      =    input_mask.unsqueeze(1    ).expand(-1,self.num_choices,-1    ).contiguous()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(
							    _A,attention_mask=_A,labels=_A,)
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)    )






					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.prepare_config_and_inputs()
							((SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_))  :		Dict				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(
					    (
					        SqueezeBertModel,
					        SqueezeBertForMaskedLM,
					        SqueezeBertForMultipleChoice,
					        SqueezeBertForQuestionAnswering,
					        SqueezeBertForSequenceClassification,
					        SqueezeBertForTokenClassification,
					    )
					    if is_torch_available()
					    else None
					)
					A	       =							(
					    {
					        'feature-extraction': SqueezeBertModel,
					        'fill-mask': SqueezeBertForMaskedLM,
					        'question-answering': SqueezeBertForQuestionAnswering,
					        'text-classification': SqueezeBertForSequenceClassification,
					        'token-classification': SqueezeBertForTokenClassification,
					        'zero-shot': SqueezeBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					A	       =							False
					A	       =							True
					A	       =							False

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    SqueezeBertModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    ConfigTester(self,config_class=_A,dim=37    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*_A    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_A    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*_A    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_A    )






					@slow
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    SqueezeBertModel.from_pretrained(_A    )
									self.assertIsNotNone(_A    )







@require_sentencepiece
@require_tokenizers
@require_torch
class 		a__						(					unittest.TestCase					):

					@slow
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model(_A    )[0]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.Size((1, 3)    )
							self.assertEqual(output.shape,_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.tensor([[0.6401, -0.0349, -0.6041]]    )
							self.assertTrue(torch.allclose(_A,_A,atol=1E-4    )    )






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


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

_snake_case										=       "\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 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     )						->							List[Any]:
           super().__init__()

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

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							str:
           if latents is None:
                 _A							:     str			=						randn_tensor(_a		,     generator=_a		,     device=_a		,     dtype=_a				)
           else:
                 if latents.shape != shape:
                       raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
                 _A							:     Union[str, Any]			=						latents.to(_a				)

           _A							:     int			=						latents * scheduler.init_noise_sigma
           return latents

     def 					a__	(  self		,     _a=0				)						->							Optional[Any]:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

           _A							:     str			=						torch.device(F'''cuda:{gpu_id}'''				)

           _A							:     Any			=						[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(_a		,     _a				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           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(_a		,     """_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 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     )						->							Tuple:
           if isinstance(_a		,     _a				) and isinstance(image[0]		,     torch.Tensor				):
                 _A							:     int			=						torch.cat(_a		,     axis=0				) if image[0].ndim == 4 else torch.stack(_a		,     axis=0				)

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

           _A							:     int			=						image.to(dtype=self.image_encoder.dtype		,     device=_a				)

           _A							:     List[Any]			=						self.image_encoder(_a				)["""last_hidden_state"""]
           _A							:     List[Any]			=						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           _A							:     Dict			=						image_embeds.repeat_interleave(_a		,     dim=0				)

           if do_classifier_free_guidance:
                 _A							:     str			=						torch.zeros_like(_a				)

                 # 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							:     List[str]			=						torch.cat([negative_image_embeds, image_embeds]				)

           return image_embeds



     @torch.no_grad()
     @replace_example_docstring(_a				)
     def __call__(  self		,     _a		,     _a = 1		,     _a = 25		,     _a = None		,     _a = None		,     _a = 4.0		,     _a = 64		,     _a = "pil"		,     _a = True		,     )						->							Union[str, Any]:

           if isinstance(_a		,     PIL.Image.Image				):
                 _A							:     List[Any]			=						1
           elif isinstance(_a		,     torch.Tensor				):
                 _A							:     Any			=						image.shape[0]
           elif isinstance(_a		,     _a				) and isinstance(image[0]		,     (torch.Tensor, PIL.Image.Image)				):
                 _A							:     Union[str, Any]			=						len(_a				)
           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(_a				)}'''				)

           _A							:     Optional[int]			=						self._execution_device

           _A							:     Tuple			=						batch_size * num_images_per_prompt

           _A							:     List[Any]			=						guidance_scale > 1.0
           _A							:     Optional[Any]			=						self._encode_image(_a		,     _a		,     _a		,     _a				)

           # prior

           self.scheduler.set_timesteps(_a		,     device=_a				)
           _A							:     Optional[int]			=						self.scheduler.timesteps

           _A							:     List[str]			=						self.prior.config.num_embeddings
           _A							:     int			=						self.prior.config.embedding_dim

           _A							:     Optional[Any]			=						self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)		,     image_embeds.dtype		,     _a		,     _a		,     _a		,     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							:     List[Any]			=						latents.reshape(latents.shape[0]		,     _a		,     _a				)

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

                 _A							:     Tuple			=						self.prior(
                     _a		,     timestep=_a		,     proj_embedding=_a		,     ).predicted_image_embedding

                 # remove the variance
                 _A			,   _A							:     Optional[Any]			=						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							:     Dict			=						noise_pred.chunk(2				)
                       _A							:     Tuple			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                 _A							:     int			=						self.scheduler.step(
                     _a		,     timestep=_a		,     sample=_a		,     ).prev_sample

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

           _A							:     List[str]			=						[]
           for i, latent in enumerate(_a				):
                 print()
                 _A							:     List[str]			=						self.renderer.decode(
                     latent[None, :]		,     _a		,     size=_a		,     ray_batch_size=4096		,     n_coarse_samples=64		,     n_fine_samples=128		,     )

                 images.append(_a				)

           _A							:     List[Any]			=						torch.stack(_a				)

           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							:     List[str]			=						images.cpu().numpy()

           if output_type == "pil":
                 _A							:     List[Any]			=						[self.numpy_to_pil(_a				) 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=_a				)

