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

A__   :					List[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 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		42


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def __init__(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__()

         self.register_modules(
             prior=A_ ,				image_encoder=A_ ,				image_processor=A_ ,				scheduler=A_ ,				renderer=A_ ,				)
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         if latents is None:
           _lowercase:			Tuple       =     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}'''       )
           _lowercase:			Union[str, Any]       =     latents.to(A_       )

         _lowercase:			Optional[int]       =     latents * scheduler.init_noise_sigma
         return latents
       def 							lowercase_							(     self ,				A_=0       )     ->	Optional[Any]:





         """simple docstring"""




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

         _lowercase:			Tuple       =     torch.device(f'''cuda:{gpu_id}'''       )

         _lowercase:			List[str]       =     [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       )     ->	Any:





         """simple docstring"""




         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 ,				A_ ,				A_ ,				A_ ,				A_ ,				)     ->	Optional[Any]:





         """simple docstring"""




         if isinstance(A_ ,				A_       ) and isinstance(image[0] ,				torch.Tensor       ):
           _lowercase:			Tuple       =     torch.cat(A_ ,				axis=0       ) if image[0].ndim == 4 else torch.stack(A_ ,				axis=0       )

         if not isinstance(A_ ,				torch.Tensor       ):
           _lowercase:			Union[str, Any]       =     self.image_processor(A_ ,				return_tensors='''pt'''       ).pixel_values[0].unsqueeze(0       )

         _lowercase:			Optional[Any]       =     image.to(dtype=self.image_encoder.dtype ,				device=A_       )

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

         _lowercase:			List[str]       =     image_embeds.repeat_interleave(A_ ,				dim=0       )

         if do_classifier_free_guidance:
           _lowercase:			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
           _lowercase:			int       =     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 ,				)     ->	str:





         """simple docstring"""




         if isinstance(A_ ,				PIL.Image.Image       ):
           _lowercase:			int       =     1
         elif isinstance(A_ ,				torch.Tensor       ):
           _lowercase:			Tuple       =     image.shape[0]
         elif isinstance(A_ ,				A_       ) and isinstance(image[0] ,				(torch.Tensor, PIL.Image.Image)       ):
           _lowercase:			Optional[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_       )}'''       )

         _lowercase:			Tuple       =     self._execution_device

         _lowercase:			Tuple       =     batch_size * num_images_per_prompt

         _lowercase:			Optional[Any]       =     guidance_scale > 1.0
         _lowercase:			Optional[Any]       =     self._encode_image(A_ ,				A_ ,				A_ ,				A_       )

         # prior

         self.scheduler.set_timesteps(A_ ,				device=A_       )
         _lowercase:			Union[str, Any]       =     self.scheduler.timesteps

         _lowercase:			Tuple       =     self.prior.config.num_embeddings
         _lowercase:			Optional[int]       =     self.prior.config.embedding_dim

         _lowercase:			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
         _lowercase:			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
           _lowercase:			Union[str, Any]       =     torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
           _lowercase:			List[Any]       =     self.scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     self.prior(
               A_ ,				timestep=A_ ,				proj_embedding=A_ ,				).predicted_image_embedding

           # remove the variance
           _lowercase ,				_lowercase:			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:
             _lowercase ,				_lowercase:			Any       =     noise_pred.chunk(2       )
             _lowercase:			str       =     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           _lowercase:			int       =     self.scheduler.step(
               A_ ,				timestep=A_ ,				sample=A_ ,				).prev_sample

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

         _lowercase:			Optional[int]       =     []
         for i, latent in enumerate(A_       ):
           print()
           _lowercase:			int       =     self.renderer.decode(
               latent[None, :] ,				A_ ,				size=A_ ,				ray_batch_size=4096 ,				n_coarse_samples=64 ,				n_fine_samples=128 ,				)

           images.append(A_       )

         _lowercase:			Union[str, 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}'''       )

         _lowercase:			str       =     images.cpu().numpy()

         if output_type == "pil":
           _lowercase:			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 json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


A__   :					Any							=   'hf-internal-testing/tiny-random-bert'
A__   :					int							=   os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
A__   :					Dict							=   '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'


class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     cached_file(A_ ,				A_       )
         # Should have downloaded the file in here
         self.assertTrue(os.path.isdir(A_       )       )
         # Cache should contain at least those three subfolders:
         for subfolder in ["blobs", "refs", "snapshots"]:
           self.assertTrue(os.path.isdir(os.path.join(A_ ,				A_       )       )       )
         with open(os.path.join(A_ ,				'''refs''' ,				'''main'''       )       ) as f:
           _lowercase:			Tuple       =     f.read()
         self.assertEqual(A_ ,				os.path.join(A_ ,				'''snapshots''' ,				A_ ,				A_       )       )
         self.assertTrue(os.path.isfile(A_       )       )

         # File is cached at the same place the second time.
         _lowercase:			List[Any]       =     cached_file(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )

         # Using a specific revision to test the full commit hash.
         _lowercase:			Union[str, Any]       =     cached_file(A_ ,				A_ ,				revision='''9b8c223'''       )
         self.assertEqual(A_ ,				os.path.join(A_ ,				'''snapshots''' ,				A_ ,				A_       )       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         with self.assertRaisesRegex(A_ ,				'''is not a valid model identifier'''       ):
           _lowercase:			Union[str, Any]       =     cached_file('''tiny-random-bert''' ,				A_       )

         with self.assertRaisesRegex(A_ ,				'''is not a valid git identifier'''       ):
           _lowercase:			List[Any]       =     cached_file(A_ ,				A_ ,				revision='''aaaa'''       )

         with self.assertRaisesRegex(A_ ,				'''does not appear to have a file named'''       ):
           _lowercase:			int       =     cached_file(A_ ,				'''conf'''       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         with self.assertRaisesRegex(A_ ,				'''does not appear to have a file named'''       ):
           _lowercase:			Dict       =     cached_file(A_ ,				'''conf'''       )

         with open(os.path.join(A_ ,				'''refs''' ,				'''main'''       )       ) as f:
           _lowercase:			int       =     f.read()
         self.assertTrue(os.path.isfile(os.path.join(A_ ,				'''.no_exist''' ,				A_ ,				'''conf'''       )       )       )

         _lowercase:			Optional[int]       =     cached_file(A_ ,				'''conf''' ,				_raise_exceptions_for_missing_entries=A_       )
         self.assertIsNone(A_       )

         _lowercase:			Optional[int]       =     cached_file(A_ ,				'''conf''' ,				local_files_only=A_ ,				_raise_exceptions_for_missing_entries=A_       )
         self.assertIsNone(A_       )

         _lowercase:			Optional[Any]       =     mock.Mock()
         _lowercase:			Optional[Any]       =     500
         _lowercase:			Optional[Any]       =     {}
         _lowercase:			Dict       =     HTTPError
         _lowercase:			Optional[Any]       =     {}

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch('''requests.Session.request''' ,				return_value=A_       ) as mock_head:
           _lowercase:			Tuple       =     cached_file(A_ ,				'''conf''' ,				_raise_exceptions_for_connection_errors=A_       )
           self.assertIsNone(A_       )
           # This check we did call the fake head request
           mock_head.assert_called()
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         self.assertIsNone(get_file_from_repo('''bert-base-cased''' ,				'''ahah.txt'''       )       )

         # The function raises if the repository does not exist.
         with self.assertRaisesRegex(A_ ,				'''is not a valid model identifier'''       ):
           get_file_from_repo('''bert-base-case''' ,				A_       )

         # The function raises if the revision does not exist.
         with self.assertRaisesRegex(A_ ,				'''is not a valid git identifier'''       ):
           get_file_from_repo('''bert-base-cased''' ,				A_ ,				revision='''ahaha'''       )

         _lowercase:			Optional[int]       =     get_file_from_repo('''bert-base-cased''' ,				A_       )
         # The name is the cached name which is not very easy to test, so instead we load the content.
         _lowercase:			Union[str, Any]       =     json.loads(open(A_ ,				'''r'''       ).read()       )
         self.assertEqual(config['''hidden_size'''] ,				768       )



       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         with tempfile.TemporaryDirectory() as tmp_dir:
           _lowercase:			List[Any]       =     Path(A_       ) / '''a.txt'''
           filename.touch()
           self.assertEqual(get_file_from_repo(A_ ,				'''a.txt'''       ) ,				str(A_       )       )

           self.assertIsNone(get_file_from_repo(A_ ,				'''b.txt'''       )       )
