

'''simple docstring'''



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


UpperCAmelCase					:			Optional[List[str]]       =	None
UpperCAmelCase					:			Union[str, Any]       =	'<' 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
UpperCAmelCase					:			Union[str, Any]       =	[
    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 		lowerCAmelCase__  :



 """simple docstring"""



 lowerCAmelCase__       = True
 lowerCAmelCase__       = None
 # Automatically constructed
 lowerCAmelCase__       = "PIL.Image.Image"
 lowerCAmelCase__       = pa.struct({"bytes": pa.binary(), "path": pa.string()}   )
 lowerCAmelCase__       = field(default="Image"					,   init=a					,   repr=a   )
 def __call__(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      return self.pa_type
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = np.array(__SCREAMING_SNAKE_CASE     )

      if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           return {"path": value, "bytes": None}
      elif isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           return {"path": None, "bytes": value}
      elif isinstance(__SCREAMING_SNAKE_CASE   ,				np.ndarray     ):
           # convert the image array to PNG/TIFF bytes
           return encode_np_array(__SCREAMING_SNAKE_CASE     )
      elif isinstance(__SCREAMING_SNAKE_CASE   ,				PIL.Image.Image     ):
           # convert the PIL image to bytes (default format is PNG/TIFF)
           return encode_pil_image(__SCREAMING_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  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			dict   ,				__SCREAMING_SNAKE_CASE   :			str=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:
           __SCREAMING_SNAKE_CASE      = {}

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = 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(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = PIL.Image.open(__SCREAMING_SNAKE_CASE     )
                else:
                     __SCREAMING_SNAKE_CASE      = path.split("""::"""     )[-1]
                     try:
                          __SCREAMING_SNAKE_CASE      = string_to_dict(__SCREAMING_SNAKE_CASE   ,				config.HUB_DATASETS_URL     )["""repo_id"""]
                          __SCREAMING_SNAKE_CASE      = token_per_repo_id.get(__SCREAMING_SNAKE_CASE     )
                     except ValueError:
                          __SCREAMING_SNAKE_CASE      = None
                     with xopen(__SCREAMING_SNAKE_CASE   ,				"""rb"""   ,				use_auth_token=__SCREAMING_SNAKE_CASE     ) as f:
                          __SCREAMING_SNAKE_CASE      = BytesIO(f.read()     )
                     __SCREAMING_SNAKE_CASE      = PIL.Image.open(bytes_     )
      else:
           __SCREAMING_SNAKE_CASE      = PIL.Image.open(BytesIO(bytes_     )     )
      image.load()  # to avoid "Too many open files" errors
      return image
 def  UpperCAmelCase__						(						self   :			int     )    ->					Union["FeatureType", Dict[str, "FeatureType"]]:


      """simple docstring"""




      from .features import Value

      return (
          self
          if self.decode
          else {
              "bytes": Value("""binary"""     ),
              "path": Value("""string"""     ),
          }
      )
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Union[pa.StringArray, pa.StructArray, pa.ListArray]     )    ->					pa.StructArray:


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			pa.StructArray     )    ->					pa.StructArray:


      """simple docstring"""




      @no_op_if_value_is_null
      def path_to_bytes(__SCREAMING_SNAKE_CASE   :			List[Any]     ):
           with xopen(__SCREAMING_SNAKE_CASE   ,				"""rb"""     ) as f:
                __SCREAMING_SNAKE_CASE      = f.read()
           return bytes_

      __SCREAMING_SNAKE_CASE      = 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()   ,				)
      __SCREAMING_SNAKE_CASE      = pa.array(
          [os.path.basename(__SCREAMING_SNAKE_CASE     ) if path is not None else None for path in storage.field("""path"""     ).to_pylist()]   ,				type=pa.string()   ,				)
      __SCREAMING_SNAKE_CASE      = pa.StructArray.from_arrays([bytes_array, path_array]   ,				["""bytes""", """path"""]   ,				mask=bytes_array.is_null()     )
      return array_cast(__SCREAMING_SNAKE_CASE   ,				self.pa_type     )







def   a__			(    ):




     """simple docstring"""


     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()
          __SCREAMING_SNAKE_CASE      = list(set(PIL.Image.OPEN.keys()		) & set(PIL.Image.SAVE.keys()		)		)
     return _IMAGE_COMPRESSION_FORMATS







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = BytesIO()
     if image.format in list_image_compression_formats():
          __SCREAMING_SNAKE_CASE      = image.format
     else:
          __SCREAMING_SNAKE_CASE      = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
     image.save(a__ ,       format=a__		)
     return buffer.getvalue()







def   a__			(    a__		):




     """simple docstring"""


     if hasattr(a__ ,       """filename"""		) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(a__		)}







def   a__			(    a__		):




     """simple docstring"""


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

     __SCREAMING_SNAKE_CASE      = array.dtype
     __SCREAMING_SNAKE_CASE      = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
     __SCREAMING_SNAKE_CASE      = dtype.kind
     __SCREAMING_SNAKE_CASE      = dtype.itemsize

     __SCREAMING_SNAKE_CASE      = None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          __SCREAMING_SNAKE_CASE      = 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:
          __SCREAMING_SNAKE_CASE      = 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:
               __SCREAMING_SNAKE_CASE      = dtype_byteorder + dtype_kind + str(a__		)
               __SCREAMING_SNAKE_CASE      = np.dtype(a__		)
               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}'		)
     __SCREAMING_SNAKE_CASE      = PIL.Image.fromarray(array.astype(a__		)		)
     return {"path": None, "bytes": image_to_bytes(a__		)}







def   a__			(    a__		):




     """simple docstring"""


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

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









'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = IFInpaintingPipeline
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__       = PipelineTesterMixin.required_optional_params - {"latents"}
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      return self._get_dummy_components()
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					Any:


      """simple docstring"""




      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs
 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""   ,				reason="""float16 requires CUDA"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      super().test_save_load_floataa(expected_max_diff=1E-1     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      self._test_save_load_local()


 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2   ,				)






