from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[tf.Tensor, np.ndarray]	)							->					List[int]:







  """simple docstring"""





  if isinstance(__UpperCamelCase ,     np.ndarray	):
    return list(tensor.shape	)

  SCREAMING_SNAKE_CASE__										=      tf.shape(__UpperCamelCase	)

  if tensor.shape == tf.TensorShape(__UpperCamelCase	):
    return dynamic

  SCREAMING_SNAKE_CASE__										=      tensor.shape.as_list()

  return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase	)]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       tf.Tensor ,     __UpperCamelCase    :       Optional[int] = None ,     __UpperCamelCase    :       Optional[str] = None	)							->					tf.Tensor:







  """simple docstring"""





  return tf.nn.softmax(logits=logits + 1E-9 ,     axis=__UpperCamelCase ,     name=__UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Tuple=1E-5 ,     __UpperCamelCase    :       str=-1	)							->					Union[str, Any]:







  """simple docstring"""





  if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCamelCase ,     __UpperCamelCase	):
    raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""	)

  # Get mean and variance on the axis to be normalized
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      tf.nn.moments(__UpperCamelCase ,     axes=[axis] ,     keepdims=__UpperCamelCase	)

  if axis != -1:
    # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
    # on every dimension except axis
    SCREAMING_SNAKE_CASE__										=      [1] * inputs.shape.rank
    SCREAMING_SNAKE_CASE__										=      shape_list(__UpperCamelCase	)[axis]
    SCREAMING_SNAKE_CASE__										=      tf.reshape(__UpperCamelCase ,     __UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      tf.reshape(__UpperCamelCase ,     __UpperCamelCase	)

  # Compute layer normalization using the batch_normalization
  # function.
  SCREAMING_SNAKE_CASE__										=      tf.nn.batch_normalization(
      __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     offset=__UpperCamelCase ,     scale=__UpperCamelCase ,     variance_epsilon=__UpperCamelCase ,     )
  return outputs



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Tuple=0 ,     __UpperCamelCase    :       Union[str, Any]=-1	)							->					str:







  """simple docstring"""





  if end_dim < 0:
    end_dim += input.shape.rank
  if start_dim < 0:
    start_dim += input.shape.rank

  if start_dim == end_dim:
    return input

  SCREAMING_SNAKE_CASE__										=      tf.shape(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
  SCREAMING_SNAKE_CASE__										=      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,     axis=0	)
  return tf.reshape(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       tf.Tensor	)							->					tf.Tensor:







  """simple docstring"""





  if not isinstance(__UpperCamelCase ,     tf.Tensor	):
    SCREAMING_SNAKE_CASE__										=      tf.convert_to_tensor(__UpperCamelCase	)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    SCREAMING_SNAKE_CASE__										=      encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    SCREAMING_SNAKE_CASE__										=      encoder_attention_mask[:, None, None, :]
  # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
  # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
  # /transformer/transformer_layers.py#L270
  # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
  # encoder_extended_attention_mask.transpose(-1, -2))
  SCREAMING_SNAKE_CASE__										=      (
      tf.cast(1 ,     encoder_attention_mask.dtype	) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       tf.Tensor ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       str = "input_ids"	)							->					None:







  """simple docstring"""





  tf.debugging.assert_less(
      __UpperCamelCase ,     tf.cast(__UpperCamelCase ,     dtype=tensor.dtype	) ,     message=(
          f"""The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase	)}) must be smaller than the embedding """
          f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
      ) ,     )



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Tuple	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      6_45_12
  # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
  # because in that case even chunking the array would not make the saving
  # possible.
  SCREAMING_SNAKE_CASE__										=      [x for x in data if len(__UpperCamelCase	) > HDF5_OBJECT_HEADER_LIMIT]

  # Expecting this to never be true.
  if bad_attributes:
    raise RuntimeError(
        """The following attributes cannot be saved to HDF5 file because """
        f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
        f"""bytes: {bad_attributes}"""	)

  SCREAMING_SNAKE_CASE__										=      np.asarray(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      np.array_split(__UpperCamelCase ,     __UpperCamelCase	)

  # This will never loop forever thanks to the test above.
  while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data	):
    num_chunks += 1
    SCREAMING_SNAKE_CASE__										=      np.array_split(__UpperCamelCase ,     __UpperCamelCase	)

  if num_chunks > 1:
    for chunk_id, chunk_data in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      chunk_data
  else:
    SCREAMING_SNAKE_CASE__										=      data



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Union[str, Any]	)							->					str:







  """simple docstring"""





  if name in group.attrs:
    SCREAMING_SNAKE_CASE__										=      [n.decode("""utf8"""	) if hasattr(__UpperCamelCase ,     """decode"""	) else n for n in group.attrs[name]]
  else:
    SCREAMING_SNAKE_CASE__										=      []
    SCREAMING_SNAKE_CASE__										=      0
    while "%s%d" % (name, chunk_id) in group.attrs:
      data.extend(
          [n.decode("""utf8"""	) if hasattr(__UpperCamelCase ,     """decode"""	) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]	)
      chunk_id += 1
  return data






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					int:







  """simple docstring"""





  def _expand_single_ad_tensor(__UpperCamelCase    :       List[str]	):
    if isinstance(__UpperCamelCase ,     tf.Tensor	) and t.shape.rank == 1:
      return tf.expand_dims(__UpperCamelCase ,     axis=-1	)
    return t

  return tf.nest.map_structure(_expand_single_ad_tensor ,     __UpperCamelCase	)

from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowerCamelCase			: str							   =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Dict       ,	_lowercase			:				CLIPSegForImageSegmentation       ,	_lowercase			:				CLIPSegProcessor       ,	_lowercase			:				AutoencoderKL       ,	_lowercase			:				CLIPTextModel       ,	_lowercase			:				CLIPTokenizer       ,	_lowercase			:				UNetaDConditionModel       ,	_lowercase			:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,	_lowercase			:				StableDiffusionSafetyChecker       ,	_lowercase			:				CLIPImageProcessor       ,	):



     """simple docstring"""






     super().__init__()

     if hasattr(scheduler.config       ,	"""steps_offset"""      ) and scheduler.config.steps_offset != 1:
       SCREAMING_SNAKE_CASE__										=      (
           f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
           f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
           """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
           """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
           """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
           """ file"""
       )
       deprecate("""steps_offset!=1"""       ,	"""1.0.0"""       ,	_lowercase       ,	standard_warn=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      1
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

     if hasattr(scheduler.config       ,	"""skip_prk_steps"""      ) and scheduler.config.skip_prk_steps is False:
       SCREAMING_SNAKE_CASE__										=      (
           f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
           """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
           """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
           """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
           """ Hub, it would be very nice if you could open a Pull request for the"""
           """ `scheduler/scheduler_config.json` file"""
       )
       deprecate("""skip_prk_steps not set"""       ,	"""1.0.0"""       ,	_lowercase       ,	standard_warn=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      True
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

     if safety_checker is None:
       logger.warning(
           f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
           """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
           """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
           """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
           """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
           """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""      )

     self.register_modules(
         segmentation_model=_lowercase       ,	segmentation_processor=_lowercase       ,	vae=_lowercase       ,	text_encoder=_lowercase       ,	tokenizer=_lowercase       ,	unet=_lowercase       ,	scheduler=_lowercase       ,	safety_checker=_lowercase       ,	feature_extractor=_lowercase       ,	)




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Union[str, int]] = "auto"      ):



     """simple docstring"""






     if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       SCREAMING_SNAKE_CASE__										=      self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(_lowercase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     self.enable_attention_slicing(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     if is_accelerate_available():
       from accelerate import cpu_offload
     else:
       raise ImportError("""Please install accelerate via `pip install accelerate`"""      )

     SCREAMING_SNAKE_CASE__										=      torch.device("""cuda"""      )

     for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
       if cpu_offloaded_model is not None:
         cpu_offload(_lowercase       ,	_lowercase      )




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     if self.device != torch.device("""meta"""      ) or not hasattr(self.unet       ,	"""_hf_hook"""      ):
       return self.device
     for module in self.unet.modules():
       if (
           hasattr(_lowercase       ,	"""_hf_hook"""      )
           and hasattr(module._hf_hook       ,	"""execution_device"""      )
           and module._hf_hook.execution_device is not None
       ):
         return torch.device(module._hf_hook.execution_device      )
     return self.device




   @torch.no_grad()
   def __call__(    self			:				Optional[Any]       ,	_lowercase			:				Union[str, List[str]]       ,	_lowercase			:				Union[torch.FloatTensor, PIL.Image.Image]       ,	_lowercase			:				str       ,	_lowercase			:				int = 5_12       ,	_lowercase			:				int = 5_12       ,	_lowercase			:				int = 50       ,	_lowercase			:				float = 7.5       ,	_lowercase			:				Optional[Union[str, List[str]]] = None       ,	_lowercase			:				Optional[int] = 1       ,	_lowercase			:				float = 0.0       ,	_lowercase			:				Optional[torch.Generator] = None       ,	_lowercase			:				Optional[torch.FloatTensor] = None       ,	_lowercase			:				Optional[str] = "pil"       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	_lowercase			:				int = 1       ,	**_lowercase			:				str       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.segmentation_processor(
         text=[text]       ,	images=[image]       ,	padding="""max_length"""       ,	return_tensors="""pt"""      ).to(self.device      )
     SCREAMING_SNAKE_CASE__										=      self.segmentation_model(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
     SCREAMING_SNAKE_CASE__										=      self.numpy_to_pil(_lowercase      )[0].resize(image.size      )

     # Run inpainting pipeline with the generated mask
     SCREAMING_SNAKE_CASE__										=      StableDiffusionInpaintPipeline(
         vae=self.vae       ,	text_encoder=self.text_encoder       ,	tokenizer=self.tokenizer       ,	unet=self.unet       ,	scheduler=self.scheduler       ,	safety_checker=self.safety_checker       ,	feature_extractor=self.feature_extractor       ,	)
     return inpainting_pipeline(
         prompt=_lowercase       ,	image=_lowercase       ,	mask_image=_lowercase       ,	height=_lowercase       ,	width=_lowercase       ,	num_inference_steps=_lowercase       ,	guidance_scale=_lowercase       ,	negative_prompt=_lowercase       ,	num_images_per_prompt=_lowercase       ,	eta=_lowercase       ,	generator=_lowercase       ,	latents=_lowercase       ,	output_type=_lowercase       ,	return_dict=_lowercase       ,	callback=_lowercase       ,	callback_steps=_lowercase       ,	)
