





"""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 			snake_case		(   __UpperCAmelCase   ):

      def __init__(			self   :int						,							_lowerCamelCase   :AutoencoderKL						,							_lowerCamelCase   :CLIPTextModel						,							_lowerCamelCase   :CLIPTokenizer						,							_lowerCamelCase   :UNetaDConditionModel						,							_lowerCamelCase   :Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]						,							):
       super().__init__()

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :List[str]						):
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[str]												=       self.vae.encode(tfms.ToTensor()(_lowerCamelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       0.1_8_2_1_5 * latent.latent_dist.sample()
       return latent

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Dict						):
       __SCREAMING_SNAKE_CASE :			str												=       (1 / 0.1_8_2_1_5) * latent
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Tuple												=       self.vae.decode(_lowerCamelCase						).sample
       __SCREAMING_SNAKE_CASE :			int												=       (img / 2 + 0.5).clamp(0						,							1						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       img.detach().cpu().permute(0						,							2						,							3						,							1						).numpy()
       __SCREAMING_SNAKE_CASE :			Tuple												=       (img * 2_5_5).round().astype('''uint8'''						)
       return Image.fromarray(img[0]						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :Tuple						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.tokenizer(
           _lowerCamelCase						,							padding='''max_length'''						,							max_length=self.tokenizer.model_max_length						,							truncation=_lowerCamelCase						,							return_tensors='''pt'''						,							)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

       __SCREAMING_SNAKE_CASE :			Dict												=       self.tokenizer(
           ''''''						,							padding='''max_length'''						,							max_length=self.tokenizer.model_max_length						,							truncation=_lowerCamelCase						,							return_tensors='''pt'''						,							)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

       return torch.cat([uncond_embedding, text_embedding]						)



      def __call__(			self   :Dict						,							_lowerCamelCase   :Image.Image						,							_lowerCamelCase   :str						,							_lowerCamelCase   :float = 0.3						,							_lowerCamelCase   :float = 0.6						,							_lowerCamelCase   :float = 0.5						,							_lowerCamelCase   :int = 4_2						,							_lowerCamelCase   :int = 5_0						,							_lowerCamelCase   :float = 7.5						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       steps - int(kmin * steps						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       steps - int(kmax * steps						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.prep_text(_lowerCamelCase						)

       self.scheduler.set_timesteps(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       img.size
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.encode(_lowerCamelCase						)

       torch.manual_seed(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)						,							).to(self.device						)

       __SCREAMING_SNAKE_CASE :			int												=       self.scheduler.add_noise(
           _lowerCamelCase						,							_lowerCamelCase						,							timesteps=self.scheduler.timesteps[tmax]						,							)

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

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.scheduler.scale_model_input(_lowerCamelCase						,							self.scheduler.timesteps[tmax]						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Any												=       self.unet(
            _lowerCamelCase						,							self.scheduler.timesteps[tmax]						,							encoder_hidden_states=_lowerCamelCase						,							).sample

       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       pred.chunk(2						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

       __SCREAMING_SNAKE_CASE :			int												=       self.scheduler.step(_lowerCamelCase						,							self.scheduler.timesteps[tmax]						,							_lowerCamelCase						).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
        if i > tmax:
         if i < tmin:  # layout generation phase
          __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.scheduler.add_noise(
              _lowerCamelCase						,							_lowerCamelCase						,							timesteps=_lowerCamelCase						,							)

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

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

         __SCREAMING_SNAKE_CASE :			Dict												=       self.scheduler.scale_model_input(_lowerCamelCase						,							_lowerCamelCase						)

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			List[Any]												=       self.unet(
              _lowerCamelCase						,							_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							).sample

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

         __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.scheduler.step(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						).prev_sample

       return self.decode(_lowerCamelCase						)







'''simple docstring'''







import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_lowerCamelCase							:						int								=  get_logger(__name__)


if TYPE_CHECKING:
						from .builder import DatasetBuilder






def 		_lowercase		(	lowercase_						,							lowercase_ = None  ):



		'''simple docstring'''







		_snake_case	       =       importlib.import_module(lowercase_  )

		# TODO(QL): always update the module to add subsequent new authentication
		if hasattr(lowercase_						,							"_patched_for_streaming"  ) and module._patched_for_streaming:
				return

		def wrap_auth(lowercase_  ):
				@wraps(lowercase_  )
				def wrapper(*lowercase_						,							**lowercase_  ):
						return function(*lowercase_						,							use_auth_token=lowercase_						,							**lowercase_  )

				_snake_case	       =       "wrap_auth"
				return wrapper

		# open files in a streaming fashion
		patch_submodule(lowercase_						,							"open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.listdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.walk"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"glob.glob"						,							wrap_auth(lowercase_  )  ).start()
		# allow to navigate in remote zip files
		patch_submodule(lowercase_						,							"os.path.join"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.dirname"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.basename"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.relpath"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.split"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.splitext"						,							lowercase_  ).start()
		# allow checks on paths
		patch_submodule(lowercase_						,							"os.path.exists"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isfile"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.getsize"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pathlib.Path"						,							lowercase_  ).start()
		# file readers
		patch_submodule(lowercase_						,							"gzip.open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"numpy.load"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pandas.read_csv"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"pandas.read_excel"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"scipy.io.loadmat"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"xml.etree.ElementTree.parse"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"xml.dom.minidom.parse"						,							wrap_auth(lowercase_  )  ).start()
		_snake_case	       =       True






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		extend_module_for_streaming(builder.__module__						,							use_auth_token=builder.use_auth_token  )
		# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
		if not builder.__module__.startswith("datasets."  ):  # check that it's not a packaged builder like csv
				for imports in get_imports(inspect.getfile(builder.__class__  )  ):
						if imports[0] == "internal":
								_snake_case	       =       imports[1]
								_snake_case	       =       ".".join(builder.__module__.split("."  )[:-1] + [internal_import_name]  )
								extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
		from .builder import DatasetBuilder

		_snake_case	       =       [
		    cls.__module__
		    for cls in type(lowercase_  ).__mro__[1:]  # make sure it's not the same module we've already patched
		    if issubclass(cls						,							lowercase_  ) and cls.__module__ != DatasetBuilder.__module__
		]  # check it's not a standard builder from datasets.builder
		for module in parent_builder_modules:
				extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )





