

'''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


__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 					A__      :
    lowercase						=		True
    lowercase						=		None
    # Automatically constructed
    lowercase						=		"PIL.Image.Image"
    lowercase						=		pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
    lowercase						=		field(default="Image"						,						init=__lowerCamelCase						,						repr=__lowerCamelCase			)



    def __call__(					self       )						->      Any:





           '''simple docstring'''
           return self.pa_type



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[str]:





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

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

           if isinstance(UpperCAmelCase_		,				UpperCAmelCase_       ):
                  return {"path": value, "bytes": None}
           elif isinstance(UpperCAmelCase_		,				UpperCAmelCase_       ):
                  return {"path": None, "bytes": value}
           elif isinstance(UpperCAmelCase_		,				np.ndarray       ):
                  # convert the image array to PNG/TIFF bytes
                  return encode_np_array(UpperCAmelCase_       )
           elif isinstance(UpperCAmelCase_		,				PIL.Image.Image       ):
                  # convert the PIL image to bytes (default format is PNG/TIFF)
                  return encode_pil_image(UpperCAmelCase_       )
           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		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      List[str]:





           '''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:
                  A_						=      {}

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



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           from .features import Value

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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





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







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''

           @no_op_if_value_is_null
           def path_to_bytes(UpperCamelCase__       ):
                  with xopen(UpperCAmelCase_		,				"""rb"""       ) as f:
                         A_						=      f.read()
                  return bytes_

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

def       UpperCAmelCase__    (      )			->			List[str]:
       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()
              A_						=      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
       return _IMAGE_COMPRESSION_FORMATS
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bytes:
       A_						=      BytesIO()
       if image.format in list_image_compression_formats():
              A_						=      image.format
       else:
              A_						=      'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
       image.save(lowerCamelCase_,       format=lowerCamelCase_				)
       return buffer.getvalue()
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			dict:
       if hasattr(lowerCamelCase_,       """filename"""				) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(lowerCamelCase_				)}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			dict:
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("""To support encoding images, please install \'Pillow\'."""				)

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

       A_						=      None

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







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[dict]:
       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError("""To support encoding images, please install \'Pillow\'."""				)

       if objs:
              A_						=      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				):
                     A_						=      no_op_if_value_is_null(lowerCamelCase_				)
                     return [obj_to_image_dict_func(lowerCamelCase_				) for obj in objs]
              elif isinstance(lowerCamelCase_,       PIL.Image.Image				):
                     A_						=      no_op_if_value_is_null(lowerCamelCase_				)
                     return [obj_to_image_dict_func(lowerCamelCase_				) for obj in objs]
              else:
                     return objs
       else:
              return objs






'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 					A__      (      _snake_case			):
    lowercase						=		(IPNDMScheduler,)
    lowercase						=		(("num_inference_steps", 50),)



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      {"""num_train_timesteps""": 1000}
           config.update(**UpperCamelCase__       )
           return config



    def 					snake_case_			(					self		,				UpperCamelCase__=0		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )
           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )
           A_						=      self.dummy_sample
           A_						=      0.1 * sample
           A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config(**UpperCamelCase__       )
                  A_						=      scheduler_class(**UpperCamelCase__       )
                  scheduler.set_timesteps(UpperCamelCase__       )
                  # copy over dummy past residuals
                  A_						=      dummy_past_residuals[:]

                  if time_step is None:
                         A_						=      scheduler.timesteps[len(scheduler.timesteps       ) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__       )
                         A_						=      scheduler_class.from_pretrained(UpperCamelCase__       )
                         new_scheduler.set_timesteps(UpperCamelCase__       )
                         # copy over dummy past residuals
                         A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           pass



    def 					snake_case_			(					self		,				UpperCamelCase__=0		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )
           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )
           A_						=      self.dummy_sample
           A_						=      0.1 * sample
           A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config()
                  A_						=      scheduler_class(**UpperCamelCase__       )
                  scheduler.set_timesteps(UpperCamelCase__       )

                  # copy over dummy past residuals (must be after setting timesteps)
                  A_						=      dummy_past_residuals[:]

                  if time_step is None:
                         A_						=      scheduler.timesteps[len(scheduler.timesteps       ) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__       )
                         A_						=      scheduler_class.from_pretrained(UpperCamelCase__       )
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(UpperCamelCase__       )

                         # copy over dummy past residual (must be after setting timesteps)
                         A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      self.scheduler_classes[0]
           A_						=      self.get_scheduler_config(**UpperCamelCase__       )
           A_						=      scheduler_class(**UpperCamelCase__       )

           A_						=      10
           A_						=      self.dummy_model()
           A_						=      self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                  A_						=      model(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           for i, t in enumerate(scheduler.timesteps       ):
                  A_						=      model(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           return sample



    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )

           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config()
                  A_						=      scheduler_class(**UpperCamelCase__       )

                  A_						=      self.dummy_sample
                  A_						=      0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCamelCase__		,				"""set_timesteps"""       ):
                         scheduler.set_timesteps(UpperCamelCase__       )
                  elif num_inference_steps is not None and not hasattr(UpperCamelCase__		,				"""set_timesteps"""       ):
                         A_						=      num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                  A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.timesteps[5]
                  A_						=      scheduler.timesteps[6]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  self.assertEqual(output_a.shape		,				sample.shape       )
                  self.assertEqual(output_a.shape		,				output_a.shape       )

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  self.assertEqual(output_a.shape		,				sample.shape       )
                  self.assertEqual(output_a.shape		,				output_a.shape       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__		,				time_step=UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           for t, num_inference_steps in zip([1, 5, 10]		,				[10, 50, 100]       ):
                  self.check_over_forward(num_inference_steps=UpperCamelCase__		,				time_step=UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.full_loop()
           A_						=      torch.mean(torch.abs(UpperCamelCase__       )       )

           assert abs(result_mean.item() - 2540529       ) < 10
