"""simple docstring"""







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

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

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


_a					:					List[Any]=			logging.get_logger(__name__)  # pylint: disable=invalid-name

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

@dataclass
class 		UpperCamelCase      (   _a						):
				UpperCAmelCase    :				Any   =		42







class 		UpperCamelCase      (   _a						):



				def __init__(self    :				Optional[Any]     ,  _A    :				PriorTransformer     ,  _A    :				CLIPVisionModel     ,  _A    :				CLIPImageProcessor     ,  _A    :				HeunDiscreteScheduler     ,  _A    :				ShapERenderer     ,  )   ->     Tuple:
					super().__init__()

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



				def  _lowercase					(self    :				Tuple     ,  _A    :				Tuple     ,  _A    :				Tuple     ,  _A    :				Any     ,  _A    :				Any     ,  _A    :				List[str]     ,  _A    :				Any)   ->     List[Any]:
					if latents is None:
						__snake_case      :							Any						=			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}")
						__snake_case      :							List[str]						=			latents.to(_a)

					__snake_case      :							int						=			latents * scheduler.init_noise_sigma
					return latents



				def  _lowercase					(self    :				Dict     ,  _A    :				str=0)   ->     List[str]:
					if is_accelerate_available():
						from accelerate import cpu_offload
					else:
						raise ImportError('Please install accelerate via `pip install accelerate`')

					__snake_case      :							List[Any]						=			torch.device(f"cuda:{gpu_id}")

					__snake_case      :							List[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  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					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  _lowercase					(self    :				Any     ,  _A    :				List[str]     ,  _A    :				Optional[int]     ,  _A    :				Union[str, Any]     ,  _A    :				List[str]     ,  )   ->     Optional[Any]:
					if isinstance(_a     ,  _a) and isinstance(image[0]     ,  torch.Tensor):
						__snake_case      :							Optional[int]						=			torch.cat(_a     ,  axis=0) if image[0].ndim == 4 else torch.stack(_a     ,  axis=0)

					if not isinstance(_a     ,  torch.Tensor):
						__snake_case      :							Optional[Any]						=			self.image_processor(_a     ,  return_tensors='pt').pixel_values[0].unsqueeze(0)

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

					__snake_case      :							str						=			self.image_encoder(_a)["last_hidden_state"]
					__snake_case      :							Tuple						=			image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					__snake_case      :							Optional[Any]						=			image_embeds.repeat_interleave(_a     ,  dim=0)

					if do_classifier_free_guidance:
						__snake_case      :							Union[str, Any]						=			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
						__snake_case      :							int						=			torch.cat([negative_image_embeds, image_embeds])

					return image_embeds






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

					if isinstance(_a     ,  PIL.Image.Image):
						__snake_case      :							List[str]						=			1
					elif isinstance(_a     ,  torch.Tensor):
						__snake_case      :							List[Any]						=			image.shape[0]
					elif isinstance(_a     ,  _a) and isinstance(image[0]     ,  (torch.Tensor, PIL.Image.Image)):
						__snake_case      :							int						=			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)}")

					__snake_case      :							Optional[int]						=			self._execution_device

					__snake_case      :							Optional[int]						=			batch_size * num_images_per_prompt

					__snake_case      :							Any						=			guidance_scale > 1.0
					__snake_case      :							int						=			self._encode_image(_a     ,  _a     ,  _a     ,  _a)

					# prior

					self.scheduler.set_timesteps(_a     ,  device=_a)
					__snake_case      :							int						=			self.scheduler.timesteps

					__snake_case      :							str						=			self.prior.config.num_embeddings
					__snake_case      :							Dict						=			self.prior.config.embedding_dim

					__snake_case      :							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
					__snake_case      :							int						=			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
						__snake_case      :							Union[str, Any]						=			torch.cat([latents] * 2) if do_classifier_free_guidance else latents
						__snake_case      :							Tuple						=			self.scheduler.scale_model_input(_a     ,  _a)

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

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

						__snake_case      :							Any						=			self.scheduler.step(
						    _a     ,  timestep=_a     ,  sample=_a     ,  ).prev_sample

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

					__snake_case      :							List[str]						=			[]
					for i, latent in enumerate(_a):
						print()
						__snake_case      :							Any						=			self.renderer.decode(
						    latent[None, :]     ,  _a     ,  size=_a     ,  ray_batch_size=40_96     ,  n_coarse_samples=64     ,  n_fine_samples=1_28     ,  )

						images.append(_a)

					__snake_case      :							Tuple						=			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}")

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

					if output_type == "pil":
						__snake_case      :							Optional[int]						=			[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 subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 		UpperCamelCase      (   lowercase						):



				@require_torch
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
					# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
					# while running an external program

					# python one-liner segments

					# this must be loaded before socket.socket is monkey-patched
					__snake_case      :							Any						=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

					__snake_case      :							Tuple						=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

					__snake_case      :							int						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

					# Force fetching the files so that we can use the cache
					__snake_case      :							int						=			'hf-internal-testing/tiny-random-bert'
					BertConfig.from_pretrained(_A)
					BertModel.from_pretrained(_A)
					BertTokenizer.from_pretrained(_A)
					pipeline(task='fill-mask'     ,  model=_A)

					# baseline - just load from_pretrained with normal network
					__snake_case      :							Union[str, Any]						=			[sys.executable, '-c', '\n'.join([load, run, mock])]

					# should succeed
					__snake_case      :							Union[str, Any]						=			self.get_env()
					# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
					__snake_case      :							str						=			'1'
					__snake_case      :							Union[str, Any]						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())



				@require_torch
				def  _lowercase					(self    :				Union[str, Any])   ->     Union[str, Any]:
					# python one-liner segments
					# this must be loaded before socket.socket is monkey-patched
					__snake_case      :							Optional[Any]						=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

					__snake_case      :							Optional[Any]						=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

					__snake_case      :							Union[str, Any]						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

					# Force fetching the files so that we can use the cache
					__snake_case      :							str						=			'hf-internal-testing/tiny-random-bert'
					BertConfig.from_pretrained(_A)
					BertModel.from_pretrained(_A)
					BertTokenizer.from_pretrained(_A)
					pipeline(task='fill-mask'     ,  model=_A)

					# baseline - just load from_pretrained with normal network
					__snake_case      :							Any						=			[sys.executable, '-c', '\n'.join([load, run, mock])]

					# should succeed
					__snake_case      :							int						=			self.get_env()
					__snake_case      :							Tuple						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())



				@require_torch
				def  _lowercase					(self    :				int)   ->     Any:
					# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
					# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
					# while running an external program

					# python one-liner segments

					# this must be loaded before socket.socket is monkey-patched
					__snake_case      :							int						=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

					__snake_case      :							int						=			'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

					__snake_case      :							Optional[int]						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

					# baseline - just load from_pretrained with normal network
					__snake_case      :							Optional[Any]						=			[sys.executable, '-c', '\n'.join([load, run])]

					# should succeed
					__snake_case      :							Optional[int]						=			self.get_env()
					__snake_case      :							Dict						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())

					# next emulate no network
					__snake_case      :							Optional[Any]						=			[sys.executable, '-c', '\n'.join([load, mock, run])]

					# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
					# env["TRANSFORMERS_OFFLINE"] = "0"
					# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
					# self.assertEqual(result.returncode, 1, result.stderr)

					# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
					__snake_case      :							Union[str, Any]						=			'1'
					__snake_case      :							str						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())



				@require_torch
				def  _lowercase					(self    :				str)   ->     Dict:
					__snake_case      :							Dict						=			'\nfrom transformers import pipeline\n        '
					__snake_case      :							List[Any]						=			'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

					__snake_case      :							List[str]						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
					__snake_case      :							str						=			self.get_env()
					__snake_case      :							Tuple						=			'1'
					__snake_case      :							Tuple						=			[sys.executable, '-c', '\n'.join([load, mock, run])]
					__snake_case      :							Optional[Any]						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  1     ,  result.stderr)
					self.assertIn(
					    'You cannot infer task automatically within `pipeline` when using offline mode'     ,  result.stderr.decode().replace('\n'     ,  '')     ,  )






				@require_torch
				def  _lowercase					(self    :				int)   ->     Optional[Any]:
					__snake_case      :							int						=			'\nfrom transformers import AutoModel\n        '
					__snake_case      :							str						=			'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

					# baseline - just load from_pretrained with normal network
					__snake_case      :							str						=			[sys.executable, '-c', '\n'.join([load, run])]

					# should succeed
					__snake_case      :							str						=			self.get_env()
					__snake_case      :							Dict						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())

					# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
					__snake_case      :							List[str]						=			'1'
					__snake_case      :							Optional[int]						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())





