





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

UpperCAmelCase						:   Optional[int]			  =     '\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 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[int]					    =	42





class 						_A(					snake_case__   ):


				"""simple docstring"""


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

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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):






									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	:						Any       =				latents.to(_A     )

									__A	:						Optional[int]       =				latents * scheduler.init_noise_sigma
									return latents



				def 					UpperCAmelCase_		(			self     ,					_A=0     ):






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

									__A	:						Optional[int]       =				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 					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(_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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					):






									if isinstance(_A     ,					_A     ) and isinstance(image[0]     ,					torch.Tensor     ):
														__A	:						Dict       =				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	:						str       =				image.to(dtype=self.image_encoder.dtype     ,					device=_A     )

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

									__A	:						Any       =				image_embeds.repeat_interleave(_A     ,					dim=0     )

									if do_classifier_free_guidance:
														__A	:						Optional[int]       =				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	:						Union[str, Any]       =				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     ,					):






									if isinstance(_A     ,					PIL.Image.Image     ):
														__A	:						Any       =				1
									elif isinstance(_A     ,					torch.Tensor     ):
														__A	:						Optional[Any]       =				image.shape[0]
									elif isinstance(_A     ,					_A     ) and isinstance(image[0]     ,					(torch.Tensor, PIL.Image.Image)     ):
														__A	:						List[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	:						List[Any]       =				self._execution_device

									__A	:						List[str]       =				batch_size * num_images_per_prompt

									__A	:						int       =				guidance_scale > 1.0
									__A	:						Optional[int]       =				self._encode_image(_A     ,					_A     ,					_A     ,					_A     )

									# prior

									self.scheduler.set_timesteps(_A     ,					device=_A     )
									__A	:						Any       =				self.scheduler.timesteps

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

									__A	:						Dict       =				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	:						Tuple       =				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	:						Dict       =				torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
														__A	:						Dict       =				self.scheduler.scale_model_input(_A     ,					_A     )

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

														# remove the variance
														__A	:						List[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	:						Union[str, Any]       =				noise_pred.chunk(2     )
																			__A	:						List[Any]       =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

														__A	:						List[str]       =				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	:						Tuple       =				self.renderer.decode(
														    latent[None, :]     ,					_A     ,					size=_A     ,					ray_batch_size=4096     ,					n_coarse_samples=64     ,					n_fine_samples=128     ,					)

														images.append(_A     )

									__A	:						Optional[int]       =				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[Any]       =				images.cpu().numpy()

									if output_type == "pil":
														__A	:						Union[str, 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     )








"""simple docstring"""






import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      lowerCamelCase__   (     A     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							None
					__a	       =							BloomTokenizerFast
					__a	       =							BloomTokenizerFast
					__a	       =							True
					__a	       =							False
					__a	       =							"""tokenizer_file"""
					__a	       =							{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												super().setUp()
												__UpperCAmelCase   :						Any      							=					BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""  )
												tokenizer.save_pretrained(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Any ,       **UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												kwargs.update(self.special_tokens_map  )
												return BloomTokenizerFast.from_pretrained(self.tmpdirname ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.get_rust_tokenizer()

												__UpperCAmelCase   :						Optional[Any]      							=					["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
												__UpperCAmelCase   :						int      							=					[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

												__UpperCAmelCase   :						Dict      							=					tokenizer.batch_encode_plus(UpperCamelCase  )["""input_ids"""]
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					tokenizer.batch_decode(UpperCamelCase  )
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Any=6  ):
												'''simple docstring'''






												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																										__UpperCAmelCase   :						Optional[int]      							=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase ,       **UpperCamelCase  )
																										# tokenizer_r.pad_token = None # Hotfixing padding = None
																										# Simple input
																										__UpperCAmelCase   :						Dict      							=					"""This is a simple input"""
																										__UpperCAmelCase   :						str      							=					["""This is a simple input 1""", """This is a simple input 2"""]
																										__UpperCAmelCase   :						List[str]      							=					("""This is a simple input""", """This is a pair""")
																										__UpperCAmelCase   :						Dict      							=					[
																										    ("""This is a simple input 1""", """This is a simple input 2"""),
																										    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																										]

																										# Simple input tests
																										try:
																																	tokenizer_r.encode(UpperCamelCase ,       max_length=UpperCamelCase  )
																																	tokenizer_r.encode_plus(UpperCamelCase ,       max_length=UpperCamelCase  )

																																	tokenizer_r.batch_encode_plus(UpperCamelCase ,       max_length=UpperCamelCase  )
																																	tokenizer_r.encode(UpperCamelCase ,       max_length=UpperCamelCase  )
																																	tokenizer_r.batch_encode_plus(UpperCamelCase ,       max_length=UpperCamelCase  )
																										except ValueError:
																																	self.fail("""Bloom Tokenizer should be able to deal with padding"""  )

																										__UpperCAmelCase   :						Union[str, Any]      							=					None  # Hotfixing padding = None
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Simple input
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Simple input
																										self.assertRaises(
																										    UpperCamelCase ,       tokenizer_r.batch_encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length""" ,       )

																										# Pair input
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Pair input
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Pair input
																										self.assertRaises(
																										    UpperCamelCase ,       tokenizer_r.batch_encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length""" ,       )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					self.get_rust_tokenizer()
												__UpperCAmelCase   :						Optional[Any]      							=					load_dataset("""xnli""" ,       """all_languages""" ,       split="""test""" ,       streaming=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					next(iter(UpperCamelCase  )  )["""premise"""]  # pick up one data
												__UpperCAmelCase   :						Any      							=					list(sample_data.values()  )

												__UpperCAmelCase   :						Optional[Any]      							=					list(map(tokenizer.encode ,       UpperCamelCase  )  )
												__UpperCAmelCase   :						List[Any]      							=					[tokenizer.decode(UpperCamelCase ,       clean_up_tokenization_spaces=UpperCamelCase  ) for x in output_tokens]
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map  ) ,       1  )
												self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()  )[0]  ) ,       1  )
