
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   import PIL.Image

   from .features import FeatureType


__lowerCamelCase							=       None
__lowerCamelCase							=       """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCamelCase							=       [
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]






@dataclass
class   UpperCAmelCase		:
   A__					:		bool						     =     True
   A__					:		Optional[str]						     =     None
   # Automatically constructed
   A__					:		ClassVar[str]						     =     "PIL.Image.Image"
   A__					:		ClassVar[Any]						     =     pa.struct({"bytes": pa.binary(), "path": pa.string()} )
   A__					:		str						     =     field(default="Image"       ,init=A_       ,repr=A_ )



   def __call__(self		:					Any )		->	List[str]:


          '''simple docstring'''


          return self.pa_type



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] )		->	dict:


          '''simple docstring'''


          if config.PIL_AVAILABLE:
                 import PIL.Image
          else:
                 raise ImportError("To support encoding images, please install 'Pillow'." )

          if isinstance(snake_case__    ,						snake_case__ ):
                 snake_case       :   Optional[Any]							=      np.array(snake_case__ )

          if isinstance(snake_case__    ,						snake_case__ ):
                 return {"path": value, "bytes": None}
          elif isinstance(snake_case__    ,						snake_case__ ):
                 return {"path": None, "bytes": value}
          elif isinstance(snake_case__    ,						np.ndarray ):
                 # convert the image array to PNG/TIFF bytes
                 return encode_np_array(snake_case__ )
          elif isinstance(snake_case__    ,						PIL.Image.Image ):
                 # convert the PIL image to bytes (default format is PNG/TIFF)
                 return encode_pil_image(snake_case__ )
          elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
                 # we set "bytes": None to not duplicate the data if they're already available locally
                 return {"bytes": None, "path": value.get("path" )}
          elif value.get("bytes" ) is not None or value.get("path" ) is not None:
                 # store the image bytes, and path is used to infer the image format using the file extension
                 return {"bytes": value.get("bytes" ), "path": value.get("path" )}
          else:
                 raise ValueError(
                     f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					dict    ,						snake_case__		:					Any=None )		->	"PIL.Image.Image":


          '''simple docstring'''


          if not self.decode:
                 raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )

          if config.PIL_AVAILABLE:
                 import PIL.Image
          else:
                 raise ImportError("To support decoding images, please install 'Pillow'." )

          if token_per_repo_id is None:
                 snake_case       :   List[Any]							=      {}

          snake_case				,					snake_case       :   List[Any]							=      value["path"], value["bytes"]
          if bytes_ is None:
                 if path is None:
                        raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
                 else:
                        if is_local_path(snake_case__ ):
                               snake_case       :   Optional[Any]							=      PIL.Image.open(snake_case__ )
                        else:
                               snake_case       :   Any							=      path.split("::" )[-1]
                               try:
                                      snake_case       :   int							=      string_to_dict(snake_case__    ,						config.HUB_DATASETS_URL )["repo_id"]
                                      snake_case       :   Optional[int]							=      token_per_repo_id.get(snake_case__ )
                               except ValueError:
                                      snake_case       :   Dict							=      None
                               with xopen(snake_case__    ,						"rb"    ,						use_auth_token=snake_case__ ) as f:
                                      snake_case       :   Union[str, Any]							=      BytesIO(f.read() )
                               snake_case       :   Tuple							=      PIL.Image.open(bytes_ )
          else:
                 snake_case       :   int							=      PIL.Image.open(BytesIO(bytes_ ) )
          image.load()  # to avoid "Too many open files" errors
          return image



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union["FeatureType", Dict[str, "FeatureType"]]:


          '''simple docstring'''


          from .features import Value

          return (
              self
              if self.decode
              else {
                  "bytes": Value("binary" ),
                  "path": Value("string" ),
              }
          )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					Union[pa.StringArray, pa.StructArray, pa.ListArray] )		->	pa.StructArray:


          '''simple docstring'''


          if pa.types.is_string(storage.type ):
                 snake_case       :   str							=      pa.array([None] * len(snake_case__ )    ,						type=pa.binary() )
                 snake_case       :   Tuple							=      pa.StructArray.from_arrays([bytes_array, storage]    ,						["bytes", "path"]    ,						mask=storage.is_null() )
          elif pa.types.is_binary(storage.type ):
                 snake_case       :   Dict							=      pa.array([None] * len(snake_case__ )    ,						type=pa.string() )
                 snake_case       :   Tuple							=      pa.StructArray.from_arrays([storage, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null() )
          elif pa.types.is_struct(storage.type ):
                 if storage.type.get_field_index("bytes" ) >= 0:
                        snake_case       :   str							=      storage.field("bytes" )
                 else:
                        snake_case       :   Union[str, Any]							=      pa.array([None] * len(snake_case__ )    ,						type=pa.binary() )
                 if storage.type.get_field_index("path" ) >= 0:
                        snake_case       :   str							=      storage.field("path" )
                 else:
                        snake_case       :   int							=      pa.array([None] * len(snake_case__ )    ,						type=pa.string() )
                 snake_case       :   int							=      pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null() )
          elif pa.types.is_list(storage.type ):
                 snake_case       :   Optional[Any]							=      pa.array(
                     [encode_np_array(np.array(snake_case__ ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()]    ,						type=pa.binary()    ,						)
                 snake_case       :   str							=      pa.array([None] * len(snake_case__ )    ,						type=pa.string() )
                 snake_case       :   Any							=      pa.StructArray.from_arrays(
                     [bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null() )
          return array_cast(snake_case__    ,						self.pa_type )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					pa.StructArray )		->	pa.StructArray:


          '''simple docstring'''


          @no_op_if_value_is_null
          def path_to_bytes(snake_case__		:					List[str] ):
                 with xopen(snake_case__    ,						"rb" ) as f:
                        snake_case       :   int							=      f.read()
                 return bytes_

          snake_case       :   Tuple							=      pa.array(
              [
                  (path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
                  for x in storage.to_pylist()
              ]    ,						type=pa.binary()    ,						)
          snake_case       :   Dict							=      pa.array(
              [os.path.basename(snake_case__ ) if path is not None else None for path in storage.field("path" ).to_pylist()]    ,						type=pa.string()    ,						)
          snake_case       :   int							=      pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null() )
          return array_cast(snake_case__    ,						self.pa_type )



def 			UpperCamelCase					(    ):
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("To support encoding images, please install 'Pillow'."    )

       global _IMAGE_COMPRESSION_FORMATS
       if _IMAGE_COMPRESSION_FORMATS is None:
              PIL.Image.init()
              snake_case       :   Tuple							=      list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
       return _IMAGE_COMPRESSION_FORMATS


def 			UpperCamelCase					(    __lowerCamelCase     : "PIL.Image.Image"    ):
       snake_case       :   Dict							=      BytesIO()
       if image.format in list_image_compression_formats():
              snake_case       :   Optional[Any]							=      image.format
       else:
              snake_case       :   List[Any]							=      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
       image.save(__lowerCamelCase				,					format=__lowerCamelCase    )
       return buffer.getvalue()


def 			UpperCamelCase					(    __lowerCamelCase     : "PIL.Image.Image"    ):
       if hasattr(__lowerCamelCase				,					"filename"    ) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray    ):
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("To support encoding images, please install 'Pillow'."    )

       snake_case       :   List[str]							=      array.dtype
       snake_case       :   Dict							=      dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
       snake_case       :   Dict							=      dtype.kind
       snake_case       :   int							=      dtype.itemsize

       snake_case       :   Tuple							=      None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              snake_case       :   Any							=      np.dtype("|u1"    )
              if dtype_kind not in ["u", "i"]:
                     raise TypeError(
                         f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""    )
              if dtype is not dest_dtype:
                     warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""    )
    # Exact match
       elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
              snake_case       :   Optional[int]							=      dtype
       else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
              while dtype_itemsize >= 1:
                     snake_case       :   List[str]							=      dtype_byteorder + dtype_kind + str(__lowerCamelCase    )
                     snake_case       :   Dict							=      np.dtype(__lowerCamelCase    )
                     if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                            warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""    )
                            break
                     else:
                            dtype_itemsize //= 2

       if dest_dtype is None:
              raise TypeError(
                  f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""    )
       snake_case       :   List[str]							=      PIL.Image.fromarray(array.astype(__lowerCamelCase    )    )
       return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}




def 			UpperCamelCase					(    __lowerCamelCase     : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    ):
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("To support encoding images, please install 'Pillow'."    )

       if objs:
              snake_case				,					snake_case       :   str							=      first_non_null_value(__lowerCamelCase    )
              if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
                     return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
              if isinstance(__lowerCamelCase				,					np.ndarray    ):
                     snake_case       :   Any							=      no_op_if_value_is_null(__lowerCamelCase    )
                     return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
              elif isinstance(__lowerCamelCase				,					PIL.Image.Image    ):
                     snake_case       :   Optional[int]							=      no_op_if_value_is_null(__lowerCamelCase    )
                     return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
              else:
                     return objs
       else:
              return objs






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       ,	)
