
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import tensorflow as tf





class   __UpperCamelCase				(    TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):






     """simple docstring"""







     def __init__(							self    :    Optional[Any]      ,		_A    :    List[str]=None      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       super().__init__(features=_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						tf_tensor_kwargs
       import tensorflow as tf  # noqa: import tf at initialization



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[str]				):







       """simple docstring"""




       import tensorflow as tf

       if isinstance(_A      ,		_A				) and column:
         if all(
             isinstance(_A      ,		tf.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
           return tf.stack(_A				)
         elif all(
             isinstance(_A      ,		(tf.Tensor, tf.RaggedTensor)				) and x.ndim == 1 and x.dtype == column[0].dtype
             for x in column				):
           # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
           return tf.ragged.stack(_A				)

       return column



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]				):







       """simple docstring"""




       import tensorflow as tf

       if value is None:
         return value

       __SCREAMING_SNAKE_CASE     :					str			=						{}

       if isinstance(_A      ,		(np.number, np.ndarray)				) and np.issubdtype(value.dtype      ,		np.integer				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						{'''dtype''': tf.intaa}
       elif isinstance(_A      ,		(np.number, np.ndarray)				) and np.issubdtype(value.dtype      ,		np.floating				):
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{'''dtype''': tf.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(_A      ,		PIL.Image.Image				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						np.asarray(_A				)

       return tf.convert_to_tensor(_A      ,		**{**default_dtype, **self.tf_tensor_kwargs}				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Tuple				):







       """simple docstring"""




       import tensorflow as tf

       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
         import torch

         if isinstance(_A      ,		torch.Tensor				):
           return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
       if hasattr(_A      ,		'''__array__'''				) and not isinstance(_A      ,		tf.Tensor				):
         __SCREAMING_SNAKE_CASE     :					List[str]			=						data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(_A      ,		np.ndarray				):
         if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
           return self._consolidate([self.recursive_tensorize(_A				) for substruct in data_struct]				)
       elif isinstance(_A      ,		(list, tuple)				):
         return self._consolidate([self.recursive_tensorize(_A				) for substruct in data_struct]				)
       return self._tensorize(_A				)



     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    dict				):







       """simple docstring"""




       return map_nested(self._recursive_tensorize      ,		_A      ,		map_list=_A				)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    pa.Table				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.numpy_arrow_extractor().extract_row(_A				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.python_features_decoder.decode_row(_A				)
       return self.recursive_tensorize(_A				)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    pa.Table				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.numpy_arrow_extractor().extract_column(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.python_features_decoder.decode_column(_A      ,		pa_table.column_names[0]				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.recursive_tensorize(_A				)
       __SCREAMING_SNAKE_CASE     :					str			=						self._consolidate(_A				)
       return column




     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    pa.Table				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.numpy_arrow_extractor().extract_batch(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.python_features_decoder.decode_batch(_A				)
       __SCREAMING_SNAKE_CASE     :					str			=						self.recursive_tensorize(_A				)
       for column_name in batch:
         __SCREAMING_SNAKE_CASE     :					str			=						self._consolidate(batch[column_name]				)
       return batch


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   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    AutoencoderKL      ,		_A    :    CLIPTextModel      ,		_A    :    CLIPTokenizer      ,		_A    :    UNetaDConditionModel      ,		_A    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		):







       """simple docstring"""




       super().__init__()

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



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Dict				):







       """simple docstring"""




       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.vae.encode(tfms.ToTensor()(_A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						0.1_82_15 * latent.latent_dist.sample()
       return latent



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						self.vae.decode(_A				).sample
       __SCREAMING_SNAKE_CASE     :					Any			=						(img / 2 + 0.5).clamp(0      ,		1				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						img.detach().cpu().permute(0      ,		2      ,		3      ,		1				).numpy()
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(img * 255).round().astype('''uint8'''				)
       return Image.fromarray(img[0]				)



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[Any]				):







       """simple docstring"""




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

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

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

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

       return torch.cat([uncond_embedding, text_embedding]				)




     def __call__(							self    :    Union[str, 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      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						steps - int(kmin * steps				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						steps - int(kmax * steps				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.prep_text(_A				)

       self.scheduler.set_timesteps(_A				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						img.size
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.encode(_A				)

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

       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.add_noise(
           _A      ,		_A      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

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

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.scheduler.scale_model_input(_A      ,		self.scheduler.timesteps[tmax]				)

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

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

       __SCREAMING_SNAKE_CASE     :					Any			=						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
             __SCREAMING_SNAKE_CASE     :					List[str]			=						self.scheduler.add_noise(
                 _A      ,		_A      ,		timesteps=_A      ,		)

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

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

           __SCREAMING_SNAKE_CASE     :					str			=						self.scheduler.scale_model_input(_A      ,		_A				)

           with torch.no_grad():
             __SCREAMING_SNAKE_CASE     :					int			=						self.unet(
                 _A      ,		_A      ,		encoder_hidden_states=_A      ,		).sample

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

           __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		_A      ,		_A				).prev_sample

       return self.decode(_A				)
