'''simple docstring'''




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


_UpperCamelCase				:		List[Any]       			=		datasets.utils.logging.get_logger(__name__)

@dataclass
class   _lowercase(     datasets.BuilderConfig				):
   __lowerCamelCase    =  None
   __lowerCamelCase    =  None
   __lowerCamelCase    =  None
   __lowerCamelCase    =  True
   __lowerCamelCase    =  None
   __lowerCamelCase    =  None
   __lowerCamelCase    =  None
   __lowerCamelCase    =  10000
   __lowerCamelCase    =  None





   def 			snake_case	(							self:    str	):
    if self.sql is None:
     raise ValueError('sql must be specified'	)
    if self.con is None:
     raise ValueError('con must be specified'	)





   def 			snake_case	(							self:    Any			,a:    dict			,a:    Optional[datasets.Features] = None			,):
    __UpperCAmelCase         =					config_kwargs.copy()
    # We need to stringify the Selectable object to make its hash deterministic

    # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
    __UpperCAmelCase         =					config_kwargs['sql']
    if not isinstance(a			,a	):
     if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
      import sqlalchemy

      if isinstance(a			,sqlalchemy.sql.Selectable	):
       __UpperCAmelCase         =					sqlalchemy.create_engine(config_kwargs['con'].split('://'	)[0] + '://'	)
       __UpperCAmelCase         =					str(sql.compile(dialect=engine.dialect	)	)
       __UpperCAmelCase         =					sql_str
      else:
       raise TypeError(
           f"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(a	)}: {sql}"""	)
     else:
      raise TypeError(
          f"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(a	)}: {sql}"""	)
    __UpperCAmelCase         =					config_kwargs['con']
    if not isinstance(a			,a	):
     __UpperCAmelCase         =					id(a	)
     logger.info(
         f"""SQL connection 'con' of type {type(a	)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""	)

    return super().create_config_id(a			,custom_features=a	)





   @property
   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					{
        'index_col': self.index_col,
        'columns': self.columns,
        'params': self.params,
        'coerce_float': self.coerce_float,
        'parse_dates': self.parse_dates,
    }
    return pd_read_sql_kwargs




class   _lowercase(     datasets.ArrowBasedBuilder				):
   __lowerCamelCase    =  SqlConfig





   def 			snake_case	(							self:    List[str]	):
    return datasets.DatasetInfo(features=self.config.features	)





   def 			snake_case	(							self:    int			,a:    Optional[Any]	):
    return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,gen_kwargs={}	)]





   def 			snake_case	(							self:    Optional[Any]			,a:    pa.Table	):
    if self.config.features is not None:
     __UpperCAmelCase         =					self.config.features.arrow_schema
     if all(not require_storage_cast(a	) for feature in self.config.features.values()	):
      # cheaper cast
      __UpperCAmelCase         =					pa.Table.from_arrays([pa_table[field.name] for field in schema]			,schema=a	)
     else:
      # more expensive cast; allows str <-> int/float or str to Audio for example
      __UpperCAmelCase         =					table_cast(a			,a	)
    return pa_table





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					self.config.chunksize
    __UpperCAmelCase         =					pd.read_sql(
        self.config.sql			,self.config.con			,chunksize=a			,**self.config.pd_read_sql_kwargs	)
    __UpperCAmelCase         =					[sql_reader] if chunksize is None else sql_reader
    for chunk_idx, df in enumerate(a	):
     __UpperCAmelCase         =					pa.Table.from_pandas(a	)
     yield chunk_idx, self._cast_table(a	)




'''simple docstring'''




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   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    AutoencoderKL			,a:    CLIPTextModel			,a:    CLIPTokenizer			,a:    UNetaDConditionModel			,a:    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,):
    super().__init__()

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





   def 			snake_case	(							self:    Union[str, Any]			,a:    Any	):
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.encode(tfms.ToTensor()(a	).unsqueeze(0	).to(self.device	) * 2 - 1	)
     __UpperCAmelCase         =					0.18215 * latent.latent_dist.sample()
    return latent





   def 			snake_case	(							self:    Any			,a:    List[str]	):
    __UpperCAmelCase         =					(1 / 0.18215) * latent
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.decode(a	).sample
    __UpperCAmelCase         =					(img / 2 + 0.5).clamp(0			,1	)
    __UpperCAmelCase         =					img.detach().cpu().permute(0			,2			,3			,1	).numpy()
    __UpperCAmelCase         =					(img * 255).round().astype('uint8'	)
    return Image.fromarray(img[0]	)





   def 			snake_case	(							self:    Any			,a:    str	):
    __UpperCAmelCase         =					self.tokenizer(
        a			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

    __UpperCAmelCase         =					self.tokenizer(
        ''			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

    return torch.cat([uncond_embedding, text_embedding]	)





   def __call__(							self:    Optional[Any]			,a:    Image.Image			,a:    str			,a:    float = 0.3			,a:    float = 0.6			,a:    float = 0.5			,a:    int = 42			,a:    int = 50			,a:    float = 7.5			,):
    __UpperCAmelCase         =					steps - int(kmin * steps	)
    __UpperCAmelCase         =					steps - int(kmax * steps	)

    __UpperCAmelCase         =					self.prep_text(a	)

    self.scheduler.set_timesteps(a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					img.size
    __UpperCAmelCase         =					self.encode(a	)

    torch.manual_seed(a	)
    __UpperCAmelCase         =					torch.randn(
        (1, self.unet.config.in_channels, height // 8, width // 8)			,).to(self.device	)

    __UpperCAmelCase         =					self.scheduler.add_noise(
        a			,a			,timesteps=self.scheduler.timesteps[tmax]			,)

    __UpperCAmelCase         =					torch.cat([latents] * 2	)

    __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,self.scheduler.timesteps[tmax]	)

    with torch.no_grad():
     __UpperCAmelCase         =					self.unet(
         a			,self.scheduler.timesteps[tmax]			,encoder_hidden_states=a			,).sample

    __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
    __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

    __UpperCAmelCase         =					self.scheduler.step(a			,self.scheduler.timesteps[tmax]			,a	).prev_sample

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

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

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

      __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,a	)

      with torch.no_grad():
       __UpperCAmelCase         =					self.unet(
           a			,a			,encoder_hidden_states=a			,).sample

      __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
      __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __UpperCAmelCase         =					self.scheduler.step(a			,a			,a	).prev_sample

    return self.decode(a	)



