




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_snake_case										=       datasets.utils.logging.get_logger(__name__)







@dataclass
class 						lowercase							(   datasets.BuilderConfig		):
     _a							 =						None
     _a							 =						"utf-8"
     _a							 =						None
     _a							 =						None
     _a							 =						True  # deprecated
     _a							 =						None  # deprecated
     _a							 =						1_0 << 2_0  # 10MB
     _a							 =						None







class 						lowercase							(   datasets.ArrowBasedBuilder		):
     _a							 =						JsonConfig

     def 					a__	(  self				)						->							Union[str, Any]:
           if self.config.block_size is not None:
                 logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""				)
                 _A							:     Optional[Any]			=						self.config.block_size
           if self.config.use_threads is not True:
                 logger.warning(
                     """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""				)
           if self.config.newlines_in_values is not None:
                 raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""				)
           return datasets.DatasetInfo(features=self.config.features				)

     def 					a__	(  self		,     _a				)						->							Union[str, Any]:
           if not self.config.data_files:
                 raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
           _A							:     Optional[Any]			=						dl_manager.download_and_extract(self.config.data_files				)
           if isinstance(_a		,     (str, list, tuple)				):
                 _A							:     List[str]			=						data_files
                 if isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						[files]
                 _A							:     str			=						[dl_manager.iter_files(_a				) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={"""files""": files}				)]
           _A							:     Tuple			=						[]
           for split_name, files in data_files.items():
                 if isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						[files]
                 _A							:     Tuple			=						[dl_manager.iter_files(_a				) for file in files]
                 splits.append(datasets.SplitGenerator(name=_a		,     gen_kwargs={"""files""": files}				)				)
           return splits

     def 					a__	(  self		,     _a				)						->							pa.Table:
           if self.config.features is not None:
                 # adding missing columns
                 for column_name in set(self.config.features				) - set(pa_table.column_names				):
                       _A							:     List[Any]			=						self.config.features.arrow_schema.field(_a				).type
                       _A							:     str			=						pa_table.append_column(_a		,     pa.array([None] * len(_a				)		,     type=_a				)				)
                 # more expensive cast to support nested structures with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 _A							:     Optional[Any]			=						table_cast(_a		,     self.config.features.arrow_schema				)
           return pa_table



     def 					a__	(  self		,     _a				)						->							Optional[int]:
           for file_idx, file in enumerate(itertools.chain.from_iterable(_a				)				):
                 # If the file is one json object and if we need to look at the list of items in one specific field
                 if self.config.field is not None:
                       with open(_a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                             _A							:     Any			=						json.load(_a				)

                       # We keep only the field we are interested in
                       _A							:     Optional[int]			=						dataset[self.config.field]

                       # We accept two format: a list of dicts or a dict of lists
                       if isinstance(_a		,     (list, tuple)				):
                             _A							:     Union[str, Any]			=						set().union(*[row.keys() for row in dataset]				)
                             _A							:     Union[str, Any]			=						{col: [row.get(_a				) for row in dataset] for col in keys}
                       else:
                             _A							:     List[str]			=						dataset
                       _A							:     Optional[Any]			=						pa.Table.from_pydict(_a				)
                       yield file_idx, self._cast_table(_a				)

                 # If the file has one json object per line
                 else:
                       with open(_a		,     """rb"""				) as f:
                             _A							:     Dict			=						0
                             # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                             # Set a default minimum value of 16kB if the chunk size is really small
                             _A							:     List[str]			=						max(self.config.chunksize // 32		,     16 << 10				)
                             _A							:     Any			=						(
                                 self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                             )
                             while True:
                                   _A							:     List[Any]			=						f.read(self.config.chunksize				)
                                   if not batch:
                                         break
                                   # Finish current line
                                   try:
                                         batch += f.readline()
                                   except (AttributeError, io.UnsupportedOperation):
                                         batch += readline(_a				)
                                   # PyArrow only accepts utf-8 encoded bytes
                                   if self.config.encoding != "utf-8":
                                         _A							:     List[str]			=						batch.decode(self.config.encoding		,     errors=_a				).encode("""utf-8"""				)
                                   try:
                                         while True:
                                               try:
                                                     _A							:     Union[str, Any]			=						paj.read_json(
                                                         io.BytesIO(_a				)		,     read_options=paj.ReadOptions(block_size=_a				)				)
                                                     break
                                               except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                     if (
                                                         isinstance(_a		,     pa.ArrowInvalid				)
                                                         and "straddling" not in str(_a				)
                                                         or block_size > len(_a				)
                                                     ):
                                                           raise
                                                     else:
                                                           # Increase the block size in case it was too small.
                                                           # The block size will be reset for the next file.
                                                           logger.debug(
                                                               F'''Batch of {len(_a				)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''				)
                                                           block_size *= 2
                                   except pa.ArrowInvalid as e:
                                         try:
                                               with open(
                                                   _a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                                                     _A							:     List[str]			=						json.load(_a				)
                                         except json.JSONDecodeError:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                               raise e
                                         # If possible, parse the file as a list of json objects and exit the loop
                                         if isinstance(_a		,     _a				):  # list is the only sequence type supported in JSON
                                               try:
                                                     _A							:     str			=						set().union(*[row.keys() for row in dataset]				)
                                                     _A							:     Dict			=						{col: [row.get(_a				) for row in dataset] for col in keys}
                                                     _A							:     List[Any]			=						pa.Table.from_pydict(_a				)
                                               except (pa.ArrowInvalid, AttributeError) as e:
                                                     logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                                     raise ValueError(F'''Not able to read records in the JSON file at {file}.'''				) from None
                                               yield file_idx, self._cast_table(_a				)
                                               break
                                         else:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                               raise ValueError(
                                                   F'''Not able to read records in the JSON file at {file}. '''
                                                   F'''You should probably indicate the field of the JSON file containing your records. '''
                                                   F'''This JSON file contain the following fields: {str(list(dataset.keys()				)				)}. '''
                                                   F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''				) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                   yield (file_idx, batch_idx), self._cast_table(_a				)
                                   batch_idx += 1







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


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

_snake_case										=       "\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 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     )						->							List[Any]:
           super().__init__()

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

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							str:
           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							:     Union[str, Any]			=						latents.to(_a				)

           _A							:     int			=						latents * scheduler.init_noise_sigma
           return latents

     def 					a__	(  self		,     _a=0				)						->							Optional[Any]:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

           _A							:     str			=						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 					a__	(  self				)						->							List[Any]:
           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 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     )						->							Tuple:
           if isinstance(_a		,     _a				) and isinstance(image[0]		,     torch.Tensor				):
                 _A							:     int			=						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							:     int			=						image.to(dtype=self.image_encoder.dtype		,     device=_a				)

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

           _A							:     Dict			=						image_embeds.repeat_interleave(_a		,     dim=0				)

           if do_classifier_free_guidance:
                 _A							:     str			=						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							:     List[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 = 25		,     _a = None		,     _a = None		,     _a = 4.0		,     _a = 64		,     _a = "pil"		,     _a = True		,     )						->							Union[str, Any]:

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

           _A							:     Tuple			=						batch_size * num_images_per_prompt

           _A							:     List[Any]			=						guidance_scale > 1.0
           _A							:     Optional[Any]			=						self._encode_image(_a		,     _a		,     _a		,     _a				)

           # prior

           self.scheduler.set_timesteps(_a		,     device=_a				)
           _A							:     Optional[int]			=						self.scheduler.timesteps

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

           _A							:     Optional[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
           _A							:     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
                 _A							:     List[str]			=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                 _A							:     int			=						self.scheduler.scale_model_input(_a		,     _a				)

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

                 # remove the variance
                 _A			,   _A							:     Optional[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			,   _A							:     Dict			=						noise_pred.chunk(2				)
                       _A							:     Tuple			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

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

                 images.append(_a				)

           _A							:     List[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}'''				)

           _A							:     List[str]			=						images.cpu().numpy()

           if output_type == "pil":
                 _A							:     List[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				)

