




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


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

lowerCamelCase			:			Optional[Any]								=       """
    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 		__lowercase						(snake_case__				):




      """simple docstring"""





      _snake_case					=		42




class 		__lowercase						(snake_case__				):




      """simple docstring"""





      def __init__( self							, A							, A							, A							, A							, A							, )						->							List[str]:
         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      )						->							Optional[Any]:
         if latents is None:
            snake_case :							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}"""      )
            snake_case :							Tuple           =			latents.to(A      )

         snake_case :							Optional[Any]           =			latents * scheduler.init_noise_sigma
         return latents

      def       UpperCAmelCase ( self							, A=0      )						->							Any:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""      )

         snake_case :							int           =			torch.device(f"""cuda:{gpu_id}"""      )

         snake_case :							Union[str, 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      )						->							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       UpperCAmelCase ( self							, A							, A							, A							, A							, )						->							Union[str, Any]:
         if isinstance(A							, A      ) and isinstance(image[0]							, torch.Tensor      ):
            snake_case :							Any           =			torch.cat(A							, axis=0      ) if image[0].ndim == 4 else torch.stack(A							, axis=0      )

         if not isinstance(A							, torch.Tensor      ):
            snake_case :							str           =			self.image_processor(A							, return_tensors="""pt"""      ).pixel_values[0].unsqueeze(0      )

         snake_case :							str           =			image.to(dtype=self.image_encoder.dtype							, device=A      )

         snake_case :							List[Any]           =			self.image_encoder(A      )['last_hidden_state']
         snake_case :							Tuple           =			image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

         snake_case :							Dict           =			image_embeds.repeat_interleave(A							, dim=0      )

         if do_classifier_free_guidance:
            snake_case :							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
            snake_case :							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 = 2_5							, A = None							, A = None							, A = 4.0							, A = 6_4							, A = "pil"							, A = True							, )						->							List[Any]:

         if isinstance(A							, PIL.Image.Image      ):
            snake_case :							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 :							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      )}"""      )

         snake_case :							int           =			self._execution_device

         snake_case :							List[Any]           =			batch_size * num_images_per_prompt

         snake_case :							Dict           =			guidance_scale > 1.0
         snake_case :							Any           =			self._encode_image(A							, A							, A							, A      )

         # prior

         self.scheduler.set_timesteps(A							, device=A      )
         snake_case :							List[Any]           =			self.scheduler.timesteps

         snake_case :							str           =			self.prior.config.num_embeddings
         snake_case :							Union[str, Any]           =			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 :							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
            snake_case :							Optional[int]           =			torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
            snake_case :							Optional[int]           =			self.scheduler.scale_model_input(A							, A      )

            snake_case :							Optional[Any]           =			self.prior(
                A							, timestep=A							, proj_embedding=A							, ).predicted_image_embedding

            # remove the variance
            snake_case :							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 :							Tuple           =			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

            snake_case :							List[Any]           =			self.scheduler.step(
                A							, timestep=A							, sample=A							, ).prev_sample

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

         snake_case :							List[Any]           =			[]
         for i, latent in enumerate(A      ):
            print()
            snake_case :							Optional[int]           =			self.renderer.decode(
                latent[None, :]							, A							, size=A							, ray_batch_size=4_0_9_6							, n_coarse_samples=6_4							, n_fine_samples=1_2_8							, )

            images.append(A      )

         snake_case :							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}"""      )

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

         if output_type == "pil":
            snake_case :							List[str]           =			[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      )












import argparse
import os
import re


_lowercase		:  List[str]			       ="""src/diffusers"""

# Pattern that looks at the indentation in a line.
_lowercase		:  str			       =re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase		:  Dict			       =re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowercase		:  Union[str, Any]			       =re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
_lowercase		:  Dict			       =re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowercase		:  Tuple			       =re.compile(r"""\[([^\]]+)\]""")


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      _re_indent.search(lowerCAmelCase__						)
		return "" if search is None else search.groups()[0]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=""							,lowerCAmelCase__=None							,lowerCAmelCase__=None						):
		lowerCamelCase_	:     Optional[Any] 						=      0
		lowerCamelCase_	:     Union[str, Any] 						=      code.split('\n'						)
		if start_prompt is not None:
				while not lines[index].startswith(lowerCAmelCase__						):
						index += 1
				lowerCamelCase_	:     Any 						=      ['\n'.join(lines[:index]						)]
		else:
				lowerCamelCase_	:     str 						=      []

		# We split into blocks until we get to the `end_prompt` (or the end of the block).
		lowerCamelCase_	:     List[str] 						=      [lines[index]]
		index += 1
		while index < len(lowerCAmelCase__						) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__						)):
				if len(lines[index]						) > 0 and get_indent(lines[index]						) == indent_level:
						if len(lowerCAmelCase__						) > 0 and get_indent(current_block[-1]						).startswith(indent_level + ' '						):
								current_block.append(lines[index]						)
								blocks.append('\n'.join(lowerCAmelCase__						)						)
								if index < len(lowerCAmelCase__						) - 1:
										lowerCamelCase_	:     int 						=      [lines[index + 1]]
										index += 1
								else:
										lowerCamelCase_	:     List[str] 						=      []
						else:
								blocks.append('\n'.join(lowerCAmelCase__						)						)
								lowerCamelCase_	:     str 						=      [lines[index]]
				else:
						current_block.append(lines[index]						)
				index += 1

		# Adds current block if it's nonempty.
		if len(lowerCAmelCase__						) > 0:
				blocks.append('\n'.join(lowerCAmelCase__						)						)

		# Add final block after end_prompt if provided.
		if end_prompt is not None and index < len(lowerCAmelCase__						):
				blocks.append('\n'.join(lines[index:]						)						)

		return blocks


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		def _inner(lowerCAmelCase__						):
				return key(lowerCAmelCase__						).lower().replace('_'							,''						)

		return _inner


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=None						):

		# If no key is provided, we use a noop.
		def noop(lowerCAmelCase__						):
				return x

		if key is None:
				lowerCamelCase_	:     int 						=      noop
		# Constants are all uppercase, they go first.
		lowerCamelCase_	:     Any 						=      [obj for obj in objects if key(lowerCAmelCase__						).isupper()]
		# Classes are not all uppercase but start with a capital, they go second.
		lowerCamelCase_	:     Dict 						=      [obj for obj in objects if key(lowerCAmelCase__						)[0].isupper() and not key(lowerCAmelCase__						).isupper()]
		# Functions begin with a lowercase, they go last.
		lowerCamelCase_	:     Any 						=      [obj for obj in objects if not key(lowerCAmelCase__						)[0].isupper()]

		lowerCamelCase_	:     Optional[Any] 						=      ignore_underscore(lowerCAmelCase__						)
		return sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		# This inner function sort imports between [ ].
		def _replace(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      match.groups()[0]
				if "," not in imports:
						return F"[{imports}]"
				lowerCamelCase_	:     Optional[int] 						=      [part.strip().replace('"'							,''						) for part in imports.split(','						)]
				# We will have a final empty element if the line finished with a comma.
				if len(keys[-1]						) == 0:
						lowerCamelCase_	:     str 						=      keys[:-1]
				return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						) + "]"

		lowerCamelCase_	:     Tuple 						=      import_statement.split('\n'						)
		if len(lowerCAmelCase__						) > 3:
				# Here we have to sort internal imports that are on several lines (one per name):
				# key: [
				#     "object1",
				#     "object2",
				#     ...
				# ]

				# We may have to ignore one or two lines on each side.
				lowerCamelCase_	:     int 						=      2 if lines[1].strip() == '[' else 1
				lowerCamelCase_	:     Any 						=      [(i, _re_strip_line.search(lowerCAmelCase__						).groups()[0]) for i, line in enumerate(lines[idx:-idx]						)]
				lowerCamelCase_	:     str 						=      sort_objects(lowerCAmelCase__							,key=lambda lowerCAmelCase__						: x[1]						)
				lowerCamelCase_	:     Any 						=      [lines[x[0] + idx] for x in sorted_indices]
				return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]						)
		elif len(lowerCAmelCase__						) == 3:
				# Here we have to sort internal imports that are on one separate line:
				# key: [
				#     "object1", "object2", ...
				# ]
				if _re_bracket_content.search(lines[1]						) is not None:
						lowerCamelCase_	:     Optional[int] 						=      _re_bracket_content.sub(_replace							,lines[1]						)
				else:
						lowerCamelCase_	:     Any 						=      [part.strip().replace('"'							,''						) for part in lines[1].split(','						)]
						# We will have a final empty element if the line finished with a comma.
						if len(keys[-1]						) == 0:
								lowerCamelCase_	:     List[Any] 						=      keys[:-1]
						lowerCamelCase_	:     Optional[Any] 						=      get_indent(lines[1]						) + ', '.join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						)
				return "\n".join(lowerCAmelCase__						)
		else:
				# Finally we have to deal with imports fitting on one line
				lowerCamelCase_	:     Any 						=      _re_bracket_content.sub(_replace							,lowerCAmelCase__						)
				return import_statement


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=True						):
		with open(lowerCAmelCase__							,'r'						) as f:
				lowerCamelCase_	:     Any 						=      f.read()

		if "_import_structure" not in code:
				return

		# Blocks of indent level 0
		lowerCamelCase_	:     int 						=      split_code_in_indented_blocks(
		    lowerCAmelCase__							,start_prompt='_import_structure = {'							,end_prompt='if TYPE_CHECKING:'						)

		# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
		for block_idx in range(1							,len(lowerCAmelCase__						) - 1						):
				# Check if the block contains some `_import_structure`s thingy to sort.
				lowerCamelCase_	:     Any 						=      main_blocks[block_idx]
				lowerCamelCase_	:     Tuple 						=      block.split('\n'						)

				# Get to the start of the imports.
				lowerCamelCase_	:     Optional[int] 						=      0
				while line_idx < len(lowerCAmelCase__						) and "_import_structure" not in block_lines[line_idx]:
						# Skip dummy import blocks
						if "import dummy" in block_lines[line_idx]:
								lowerCamelCase_	:     List[Any] 						=      len(lowerCAmelCase__						)
						else:
								line_idx += 1
				if line_idx >= len(lowerCAmelCase__						):
						continue

				# Ignore beginning and last line: they don't contain anything.
				lowerCamelCase_	:     Tuple 						=      '\n'.join(block_lines[line_idx:-1]						)
				lowerCamelCase_	:     Dict 						=      get_indent(block_lines[1]						)
				# Slit the internal block into blocks of indent level 1.
				lowerCamelCase_	:     Dict 						=      split_code_in_indented_blocks(lowerCAmelCase__							,indent_level=lowerCAmelCase__						)
				# We have two categories of import key: list or _import_structure[key].append/extend
				lowerCamelCase_	:     List[str] 						=      _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
				# Grab the keys, but there is a trap: some lines are empty or just comments.
				lowerCamelCase_	:     Tuple 						=      [(pattern.search(lowerCAmelCase__						).groups()[0] if pattern.search(lowerCAmelCase__						) is not None else None) for b in internal_blocks]
				# We only sort the lines with a key.
				lowerCamelCase_	:     Any 						=      [(i, key) for i, key in enumerate(lowerCAmelCase__						) if key is not None]
				lowerCamelCase_	:     Optional[Any] 						=      [x[0] for x in sorted(lowerCAmelCase__							,key=lambda lowerCAmelCase__						: x[1]						)]

				# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
				lowerCamelCase_	:     int 						=      0
				lowerCamelCase_	:     Dict 						=      []
				for i in range(len(lowerCAmelCase__						)						):
						if keys[i] is None:
								reordered_blocks.append(internal_blocks[i]						)
						else:
								lowerCamelCase_	:     Tuple 						=      sort_objects_in_import(internal_blocks[sorted_indices[count]]						)
								reordered_blocks.append(lowerCAmelCase__						)
								count += 1

        # And we put our main block back together with its first and last line.
				lowerCamelCase_	:     Tuple 						=      '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]						)

		if code != "\n".join(lowerCAmelCase__						):
				if check_only:
						return True
				else:
						print(F"Overwriting {file}."						)
						with open(lowerCAmelCase__							,'w'						) as f:
								f.write('\n'.join(lowerCAmelCase__						)						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__=True						):
		lowerCamelCase_	:     Dict 						=      []
		for root, _, files in os.walk(lowerCAmelCase__						):
				if "__init__.py" in files:
						lowerCamelCase_	:     Optional[int] 						=      sort_imports(os.path.join(lowerCAmelCase__							,'__init__.py'						)							,check_only=lowerCAmelCase__						)
						if result:
								lowerCamelCase_	:     Dict 						=      [os.path.join(lowerCAmelCase__							,'__init__.py'						)]
		if len(lowerCAmelCase__						) > 0:
				raise ValueError(F"Would overwrite {len(lowerCAmelCase__						)} files, run `make style`."						)


if __name__ == "__main__":
		_lowercase		:  int			       =argparse.ArgumentParser()
		parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
		_lowercase		:  Union[str, Any]			       =parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)
