'''simple docstring'''




import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_UpperCamelCase				:		Tuple       			=		logging.get_logger(__name__)

if is_tf_available():
     import tensorflow as tf

@dataclass
class   _lowercase(     _lowerCamelCase				):
   __lowerCamelCase    =  '''tf'''
   __lowerCamelCase    =  field(
       default=_lowerCamelCase   ,metadata={'''help''': '''Name of TPU'''}   ,)

   __lowerCamelCase    =  field(
       default=_lowerCamelCase   ,metadata={'''help''': '''Zone of TPU'''}   ,)

   __lowerCamelCase    =  field(
       default=_lowerCamelCase   ,metadata={'''help''': '''Name of Cloud TPU-enabled project'''}   ,)

   __lowerCamelCase    =  field(
       default=1.0   ,metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}   ,)

   __lowerCamelCase    =  field(default=_lowerCamelCase   ,metadata={'''help''': '''Whether to activate the XLA compilation or not'''}				)





   @cached_property
   def 			snake_case	(							self:    int	):
    requires_backends(self			,['tf']	)
    logger.info('Tensorflow: setting up strategy'	)

    __UpperCAmelCase         =					tf.config.list_physical_devices('GPU'	)

    # Set to float16 at first
    if self.fpaa:
     tf.keras.mixed_precision.set_global_policy('mixed_float16'	)

    if self.no_cuda:
     __UpperCAmelCase         =					tf.distribute.OneDeviceStrategy(device='/cpu:0'	)
    else:
     try:
      if self.tpu_name:
       __UpperCAmelCase         =					tf.distribute.cluster_resolver.TPUClusterResolver(
           self.tpu_name			,zone=self.tpu_zone			,project=self.gcp_project	)
      else:
       __UpperCAmelCase         =					tf.distribute.cluster_resolver.TPUClusterResolver()
     except ValueError:
      if self.tpu_name:
       raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""	)
      else:
       __UpperCAmelCase         =					None

     if tpu:
      # Set to bfloat16 in case of TPU
      if self.fpaa:
       tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'	)

      tf.config.experimental_connect_to_cluster(a	)
      tf.tpu.experimental.initialize_tpu_system(a	)

      __UpperCAmelCase         =					tf.distribute.TPUStrategy(a	)

     elif len(a	) == 0:
      __UpperCAmelCase         =					tf.distribute.OneDeviceStrategy(device='/cpu:0'	)
     elif len(a	) == 1:
      __UpperCAmelCase         =					tf.distribute.OneDeviceStrategy(device='/gpu:0'	)
     elif len(a	) > 1:
      # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
      __UpperCAmelCase         =					tf.distribute.MirroredStrategy()
     else:
      raise ValueError('Cannot find the proper strategy, please check your environment properties.'	)

    return strategy





   @property
   def 			snake_case	(							self:    Union[str, Any]	):
    requires_backends(self			,['tf']	)
    return self._setup_strategy





   @property
   def 			snake_case	(							self:    List[Any]	):
    requires_backends(self			,['tf']	)
    return self._setup_strategy.num_replicas_in_sync





   @property
   def 			snake_case	(							self:    Union[str, Any]	):
    return False  # TF Logging is handled by Keras not the Trainer





   @property
   def 			snake_case	(							self:    Optional[Any]	):
    if self.per_gpu_train_batch_size:
     logger.warning(
         'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
         'version. Using `--per_device_train_batch_size` is preferred.'	)
    __UpperCAmelCase         =					self.per_gpu_train_batch_size or self.per_device_train_batch_size
    return per_device_batch_size * self.n_replicas





   @property
   def 			snake_case	(							self:    Dict	):
    if self.per_gpu_eval_batch_size:
     logger.warning(
         'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
         'version. Using `--per_device_eval_batch_size` is preferred.'	)
    __UpperCAmelCase         =					self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
    return per_device_batch_size * self.n_replicas





   @property
   def 			snake_case	(							self:    Dict	):
    requires_backends(self			,['tf']	)
    warnings.warn(
        'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'			,a			,)
    return self._setup_strategy.num_replicas_in_sync




'''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	)



