





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = False				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)       ->   List[Any]:






        '''simple docstring'''






        super().__init__(
            UpperCAmelCase				,						split=UpperCAmelCase				,						features=UpperCAmelCase				,						cache_dir=UpperCAmelCase				,						keep_in_memory=UpperCAmelCase				,						streaming=UpperCAmelCase				,						num_proc=UpperCAmelCase				,						**UpperCAmelCase				,						)
        __snake_case   :				Any        =       path_or_paths if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else {self.split: path_or_paths}
        __snake_case   :				Optional[Any]        =       Csv(
            cache_dir=UpperCAmelCase				,						data_files=UpperCAmelCase				,						features=UpperCAmelCase				,						**UpperCAmelCase				,						)







      def 						UpperCAmelCase		( self   )       ->   str:






        '''simple docstring'''






        if self.streaming:
          __snake_case   :				str        =       self.builder.as_streaming_dataset(split=self.split   )
        # Build regular (map-style) dataset
        else:
          __snake_case   :				int        =       None
          __snake_case   :				Dict        =       None
          __snake_case   :				Dict        =       None
          __snake_case   :				Union[str, Any]        =       None

          self.builder.download_and_prepare(
              download_config=UpperCAmelCase				,						download_mode=UpperCAmelCase				,						verification_mode=UpperCAmelCase				,						base_path=UpperCAmelCase				,						num_proc=self.num_proc				,						)
          __snake_case   :				Dict        =       self.builder.as_dataset(
              split=self.split				,						verification_mode=UpperCAmelCase				,						in_memory=self.keep_in_memory   )
        return dataset




class 				_lowerCamelCase :



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)       ->   List[str]:






        '''simple docstring'''






        if num_proc is not None and num_proc <= 0:
          raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""   )

        __snake_case   :				Dict        =       dataset
        __snake_case   :				Optional[Any]        =       path_or_buf
        __snake_case   :				List[str]        =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        __snake_case   :				Optional[Any]        =       num_proc
        __snake_case   :				int        =       "utf-8"
        __snake_case   :				List[Any]        =       to_csv_kwargs







      def 						UpperCAmelCase		( self   )       ->   int:






        '''simple docstring'''






        __snake_case   :				int        =       self.to_csv_kwargs.pop("path_or_buf"				,						UpperCAmelCase   )
        __snake_case   :				Optional[Any]        =       self.to_csv_kwargs.pop("header"				,						UpperCAmelCase   )
        __snake_case   :				Union[str, Any]        =       self.to_csv_kwargs.pop("index"				,						UpperCAmelCase   )

        if isinstance(self.path_or_buf				,						(str, bytes, os.PathLike)   ):
          with open(self.path_or_buf				,						"wb+"   ) as buffer:
            __snake_case   :				str        =       self._write(file_obj=UpperCAmelCase				,						header=UpperCAmelCase				,						index=UpperCAmelCase				,						**self.to_csv_kwargs   )
        else:
          __snake_case   :				Union[str, Any]        =       self._write(file_obj=self.path_or_buf				,						header=UpperCAmelCase				,						index=UpperCAmelCase				,						**self.to_csv_kwargs   )
        return written







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Union[str, Any]:






        '''simple docstring'''






        __snake_case    ,   __snake_case    ,   __snake_case    ,   __snake_case   :				Optional[int]        =       args

        __snake_case   :				Optional[Any]        =       query_table(
            table=self.dataset.data				,						key=slice(UpperCAmelCase				,						offset + self.batch_size   )				,						indices=self.dataset._indices				,						)
        __snake_case   :				Dict        =       batch.to_pandas().to_csv(
            path_or_buf=UpperCAmelCase				,						header=header if (offset == 0) else False				,						index=UpperCAmelCase				,						**UpperCAmelCase   )
        return csv_str.encode(self.encoding   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   int:






        '''simple docstring'''






        __snake_case   :				List[Any]        =       0

        if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0				,						len(self.dataset   )				,						self.batch_size   )				,						unit="ba"				,						disable=not logging.is_progress_bar_enabled()				,						desc="Creating CSV from Arrow format"				,						):
            __snake_case   :				int        =       self._batch_csv((offset, header, index, to_csv_kwargs)   )
            written += file_obj.write(UpperCAmelCase   )

        else:
          __snake_case    ,   __snake_case   :				Dict        =       len(self.dataset   ), self.batch_size
          with multiprocessing.Pool(self.num_proc   ) as pool:
            for csv_str in logging.tqdm(
                pool.imap(
                    self._batch_csv				,						[(offset, header, index, to_csv_kwargs) for offset in range(0				,						UpperCAmelCase				,						UpperCAmelCase   )]				,						)				,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,						unit="ba"				,						disable=not logging.is_progress_bar_enabled()				,						desc="Creating CSV from Arrow format"				,						):
              written += file_obj.write(UpperCAmelCase   )

        return written








from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:






        '''simple docstring'''






        super().__init__()

        self.register_modules(vae=UpperCAmelCase				,						text_encoder=UpperCAmelCase				,						tokenizer=UpperCAmelCase				,						unet=UpperCAmelCase				,						scheduler=UpperCAmelCase   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        with torch.no_grad():
          __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
          __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
        return latent







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
        with torch.no_grad():
          __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
        __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
        __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
        __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
        return Image.fromarray(img[0]   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:






        '''simple docstring'''






        __snake_case   :				int        =       self.tokenizer(
            UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

        __snake_case   :				List[Any]        =       self.text_encoder(text_input.input_ids.to(self.device   )   )[0]

        __snake_case   :				Union[str, Any]        =       self.tokenizer(
            ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

        __snake_case   :				int        =       self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

        return torch.cat([uncond_embedding, text_embedding]   )







      def __call__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 0.3				,						UpperCAmelCase = 0.6				,						UpperCAmelCase = 0.5				,						UpperCAmelCase = 42				,						UpperCAmelCase = 50				,						UpperCAmelCase = 7.5				,						)       ->   Image.Image:






        '''simple docstring'''






        __snake_case   :				Tuple        =       steps - int(kmin * steps   )
        __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )

        __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )

        self.scheduler.set_timesteps(UpperCAmelCase   )

        __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
        __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )

        torch.manual_seed(UpperCAmelCase   )
        __snake_case   :				str        =       torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )

        __snake_case   :				int        =       self.scheduler.add_noise(
            UpperCAmelCase				,						UpperCAmelCase				,						timesteps=self.scheduler.timesteps[tmax]				,						)

        __snake_case   :				Optional[Any]        =       torch.cat([latents] * 2   )

        __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )

        with torch.no_grad():
          __snake_case   :				int        =       self.unet(
              UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample

        __snake_case    ,   __snake_case   :				Union[str, Any]        =       pred.chunk(2   )
        __snake_case   :				Optional[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __snake_case   :				int        =       self.scheduler.step(UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						UpperCAmelCase   ).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
          if i > tmax:
            if i < tmin:  # layout generation phase
              __snake_case   :				int        =       self.scheduler.add_noise(
                  UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)

              __snake_case   :				str        =       (mix_factor * latents) + (
                  1 - mix_factor
              ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
              __snake_case   :				List[Any]        =       torch.cat([input] * 2   )

            else:  # content generation phase
              __snake_case   :				str        =       torch.cat([latents] * 2   )

            __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )

            with torch.no_grad():
              __snake_case   :				List[str]        =       self.unet(
                  UpperCAmelCase				,						UpperCAmelCase				,						encoder_hidden_states=UpperCAmelCase				,						).sample

            __snake_case    ,   __snake_case   :				str        =       pred.chunk(2   )
            __snake_case   :				List[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

            __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample

        return self.decode(UpperCAmelCase   )

