


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      =   """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""







@dataclass
class lowerCAmelCase_						(			lowerCAmelCase_	):

 '''simple docstring'''

 __snake_case												=  42







class lowerCAmelCase_						(			lowerCAmelCase_	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       super().__init__()

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       if latents is None:
             snake_case_	=				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}''' )
             snake_case_	=				latents.to(__lowerCAmelCase )

       snake_case_	=				latents * scheduler.init_noise_sigma
       return latents



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=0 ):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('''Please install accelerate via `pip install accelerate`''' )

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

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

       if not isinstance(__lowerCAmelCase		,				torch.Tensor ):
             snake_case_	=				self.image_processor(__lowerCAmelCase		,				return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )

       snake_case_	=				image.to(dtype=self.image_encoder.dtype		,				device=__lowerCAmelCase )

       snake_case_	=				self.image_encoder(__lowerCAmelCase )['''last_hidden_state''']
       snake_case_	=				image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       snake_case_	=				image_embeds.repeat_interleave(__lowerCAmelCase		,				dim=0 )

       if do_classifier_free_guidance:
             snake_case_	=				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
             snake_case_	=				torch.cat([negative_image_embeds, image_embeds] )

       return image_embeds





 @torch.no_grad()
 @replace_example_docstring(__lowerCAmelCase )
 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = 1		,				_UpperCAmelCase = 25		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 4.0		,				_UpperCAmelCase = 64		,				_UpperCAmelCase = "pil"		,				_UpperCAmelCase = True		,				):

       if isinstance(__lowerCAmelCase		,				PIL.Image.Image ):
             snake_case_	=				1
       elif isinstance(__lowerCAmelCase		,				torch.Tensor ):
             snake_case_	=				image.shape[0]
       elif isinstance(__lowerCAmelCase		,				__lowerCAmelCase ) and isinstance(image[0]		,				(torch.Tensor, PIL.Image.Image) ):
             snake_case_	=				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 )}''' )

       snake_case_	=				self._execution_device

       snake_case_	=				batch_size * num_images_per_prompt

       snake_case_	=				guidance_scale > 1.0
       snake_case_	=				self._encode_image(__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase )

       # prior

       self.scheduler.set_timesteps(__lowerCAmelCase		,				device=__lowerCAmelCase )
       snake_case_	=				self.scheduler.timesteps

       snake_case_	=				self.prior.config.num_embeddings
       snake_case_	=				self.prior.config.embedding_dim

       snake_case_	=				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
       snake_case_	=				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
             snake_case_	=				torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
             snake_case_	=				self.scheduler.scale_model_input(__lowerCAmelCase		,				__lowerCAmelCase )

             snake_case_	=				self.prior(
                 __lowerCAmelCase		,				timestep=__lowerCAmelCase		,				proj_embedding=__lowerCAmelCase		,				).predicted_image_embedding

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

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

             snake_case_	=				self.scheduler.step(
                 __lowerCAmelCase		,				timestep=__lowerCAmelCase		,				sample=__lowerCAmelCase		,				).prev_sample

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

       snake_case_	=				[]
       for i, latent in enumerate(__lowerCAmelCase ):
             print()
             snake_case_	=				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 )

       snake_case_	=				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}''' )

       snake_case_	=				images.cpu().numpy()

       if output_type == "pil":
             snake_case_	=				[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 )



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase      =   get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''</s>'''
       snake_case_	=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase )		,				_UpperCAmelCase )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase )		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]		,				'''<pad>''' )
       self.assertEqual(vocab_keys[1]		,				'''</s>''' )
       self.assertEqual(vocab_keys[-1]		,				'''v''' )
       self.assertEqual(len(_UpperCAmelCase )		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       self.assertEqual(self.get_tokenizer().vocab_size		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
           ''' </s> <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       snake_case_	=				'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       snake_case_	=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_61_03
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 1_03
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 10_24
       snake_case_	=				'''To ensure a smooth flow of bank resolutions.'''
       snake_case_	=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 1_50, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 10_24)
       assert batch.attention_mask.shape == (2, 10_24)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 @slow
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				{'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase		,				model_name='''google/bigbird-pegasus-large-arxiv'''		,				revision='''ba85d0851d708441f91440d509690f1ab6353415'''		,				)







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase		,				offset=0		,				mask_token_sent=_UpperCAmelCase		,				mask_token='''[MASK]''' )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
           ''' <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 10_00, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 40_96)
       assert batch.attention_mask.shape == (2, 40_96)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				(
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       snake_case_	=				self._large_tokenizer(_UpperCAmelCase ).input_ids

       self.assertListEqual(
           _UpperCAmelCase		,				[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]		,				)