
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					: 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					: Tuple      =       [
    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 				lowercase					:
		__lowercase       :       bool	     =				True
		__lowercase       :       Optional[str]	     =				None
		# Automatically constructed
		__lowercase       :       ClassVar[str]	     =				"PIL.Image.Image"
		__lowercase       :       ClassVar[Any]	     =				pa.struct({"bytes": pa.binary(), "path": pa.string()}   )
		__lowercase       :       str	     =				field(default="Image"  ,   init=_SCREAMING_SNAKE_CASE  ,   repr=_SCREAMING_SNAKE_CASE   )



		def __call__( self       )  ->					Tuple:
				"""simple docstring"""
				return self.pa_type



		def __UpperCamelCase ( self				,      A_       )  ->					dict:
				"""simple docstring"""
				if config.PIL_AVAILABLE:
						import PIL.Image
				else:
						raise ImportError('To support encoding images, please install \'Pillow\'.'       )

				if isinstance(A_				,      A_       ):
						UpperCamelCase		=     np.array(A_       )

				if isinstance(A_				,      A_       ):
						return {"path": value, "bytes": None}
				elif isinstance(A_				,      A_       ):
						return {"path": None, "bytes": value}
				elif isinstance(A_				,      np.ndarray       ):
						# convert the image array to PNG/TIFF bytes
						return encode_np_array(A_       )
				elif isinstance(A_				,      PIL.Image.Image       ):
						# convert the PIL image to bytes (default format is PNG/TIFF)
						return encode_pil_image(A_       )
				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				,      A_				,      A_=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:
						UpperCamelCase		=     {}

				UpperCamelCase	,  UpperCamelCase		=     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(A_       ):
										UpperCamelCase		=     PIL.Image.open(A_       )
								else:
										UpperCamelCase		=     path.split('::'       )[-1]
										try:
												UpperCamelCase		=     string_to_dict(A_				,      config.HUB_DATASETS_URL       )['repo_id']
												UpperCamelCase		=     token_per_repo_id.get(A_       )
										except ValueError:
												UpperCamelCase		=     None
										with xopen(A_				,      'rb'				,      use_auth_token=A_       ) as f:
												UpperCamelCase		=     BytesIO(f.read()       )
										UpperCamelCase		=     PIL.Image.open(bytes_       )
				else:
						UpperCamelCase		=     PIL.Image.open(BytesIO(bytes_       )       )
				image.load()  # to avoid "Too many open files" errors
				return image



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



		def __UpperCamelCase ( self				,      A_       )  ->					pa.StructArray:
				"""simple docstring"""

				@no_op_if_value_is_null
				def path_to_bytes(A_       ):
						with xopen(A_				,      'rb'       ) as f:
								UpperCamelCase		=     f.read()
						return bytes_

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






def       A       (					)   ->  List[str]:
		'''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()
				UpperCamelCase		=     list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
		return _IMAGE_COMPRESSION_FORMATS


def       A       (					lowercase )   ->  bytes:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  dict:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  dict:
		'''simple docstring'''

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

		UpperCamelCase		=     array.dtype
		UpperCamelCase		=     dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
		UpperCamelCase		=     dtype.kind
		UpperCamelCase		=     dtype.itemsize

		UpperCamelCase		=     None

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


def       A       (					lowercase )   ->  List[dict]:
		'''simple docstring'''

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

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


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 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

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

				UpperCamelCase		=     {
				    '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:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
