


"""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


A	:  Optional[List[str]]            =     None
A	:  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
A	:  List[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     _UpperCamelCase    :
							'''simple docstring'''







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

							def __call__(	self      ):
										return self.pa_type

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

										if isinstance(__a       ,  __a      ):
													__lowerCAmelCase							       =     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      snake_case							(	self       ,  __a       ,  __a=None      ):
										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:
													__lowerCAmelCase							       =     {}

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

							def      snake_case							(	self      ):
										from .features import Value

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

							def      snake_case							(	self       ,  __a      ):
										if pa.types.is_string(storage.type      ):
													__lowerCAmelCase							       =     pa.array([None] * len(__a      )       ,  type=pa.binary()      )
													__lowerCAmelCase							       =     pa.StructArray.from_arrays([bytes_array, storage]       ,  ["bytes", "path"]       ,  mask=storage.is_null()      )
										elif pa.types.is_binary(storage.type      ):
													__lowerCAmelCase							       =     pa.array([None] * len(__a      )       ,  type=pa.string()      )
													__lowerCAmelCase							       =     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:
																__lowerCAmelCase							       =     storage.field("bytes"      )
													else:
																__lowerCAmelCase							       =     pa.array([None] * len(__a      )       ,  type=pa.binary()      )
													if storage.type.get_field_index("path"      ) >= 0:
																__lowerCAmelCase							       =     storage.field("path"      )
													else:
																__lowerCAmelCase							       =     pa.array([None] * len(__a      )       ,  type=pa.string()      )
													__lowerCAmelCase							       =     pa.StructArray.from_arrays([bytes_array, path_array]       ,  ["bytes", "path"]       ,  mask=storage.is_null()      )
										elif pa.types.is_list(storage.type      ):
													__lowerCAmelCase							       =     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()       ,  )
													__lowerCAmelCase							       =     pa.array([None] * len(__a      )       ,  type=pa.string()      )
													__lowerCAmelCase							       =     pa.StructArray.from_arrays(
													    [bytes_array, path_array]       ,  ["bytes", "path"]       ,  mask=bytes_array.is_null()      )
										return array_cast(__a       ,  self.pa_type      )



							def      snake_case							(	self       ,  __a      ):

										@no_op_if_value_is_null
										def path_to_bytes(__a      ):
													with xopen(__a       ,  "rb"      ) as f:
																__lowerCAmelCase							       =     f.read()
													return bytes_

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


def _lowerCamelCase      (						):

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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

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

			__lowerCAmelCase							       =     None

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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

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








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =(KDPMaDiscreteScheduler,)
							__UpperCAmelCase		:       Optional[Any]							 =1_0

							def      snake_case							(	self       ,  **__a      ):
										__lowerCAmelCase							       =     {
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__a      )
										return config

							def      snake_case							(	self      ):
										for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=__a      )

							def      snake_case							(	self      ):
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
													self.check_over_configs(beta_start=__a       ,  beta_end=__a      )

							def      snake_case							(	self      ):
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__a      )

							def      snake_case							(	self      ):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config(prediction_type="v_prediction"      )
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_9_3_4e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 6.1_1_1_2e-1_0      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3

							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3



							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps       ,  device=__a      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter.to(__a      ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if str(__a      ).startswith("cpu"      ):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




