





'''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							:     Optional[List[str]]		     =None
__lowerCAmelCase							:     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
__lowerCAmelCase							:     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        UpperCAmelCase     :
 __lowercase       		=    True
 __lowercase       		=    None
 # Automatically constructed
 __lowercase       		=    "PIL.Image.Image"
 __lowercase       		=    pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}    )
 __lowercase       		=    field(default="""Image"""      ,	init=UpperCamelCase__      ,	repr=UpperCamelCase__    )
 def __call__(    self				:str							)->							Tuple:
     return self.pa_type
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]							)->							dict:
     if config.PIL_AVAILABLE:
         import PIL.Image
     else:
         raise ImportError("To support encoding images, please install 'Pillow'."							)

     if isinstance(lowercase_						,	lowercase_							):
         A__    =      np.array(lowercase_							)

     if isinstance(lowercase_						,	lowercase_							):
         return {"path": value, "bytes": None}
     elif isinstance(lowercase_						,	lowercase_							):
         return {"path": None, "bytes": value}
     elif isinstance(lowercase_						,	np.ndarray							):
         # convert the image array to PNG/TIFF bytes
         return encode_np_array(lowercase_							)
     elif isinstance(lowercase_						,	PIL.Image.Image							):
         # convert the PIL image to bytes (default format is PNG/TIFF)
         return encode_pil_image(lowercase_							)
     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				:Any						,	lowercase_				:dict						,	lowercase_				:str=None							)->							"PIL.Image.Image":
     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__,     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(lowercase_							):
                 A__    =      PIL.Image.open(lowercase_							)
             else:
                 A__    =      path.split("::"							)[-1]
                 try:
                     A__    =      string_to_dict(lowercase_						,	config.HUB_DATASETS_URL							)["repo_id"]
                     A__    =      token_per_repo_id.get(lowercase_							)
                 except ValueError:
                     A__    =      None
                 with xopen(lowercase_						,	"rb"						,	use_auth_token=lowercase_							) 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 					UpperCAmelCase_							(    self				:List[Any]							)->							Union["FeatureType", Dict[str, "FeatureType"]]:
     from .features import Value

     return (
         self
         if self.decode
         else {
             "bytes": Value("binary"							),
             "path": Value("string"							),
         }
     )
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Union[pa.StringArray, pa.StructArray, pa.ListArray]							)->							pa.StructArray:
     if pa.types.is_string(storage.type							):
         A__    =      pa.array([None] * len(lowercase_							)						,	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(lowercase_							)						,	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(lowercase_							)						,	type=pa.binary()							)
         if storage.type.get_field_index("path"							) >= 0:
             A__    =      storage.field("path"							)
         else:
             A__    =      pa.array([None] * len(lowercase_							)						,	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(lowercase_							)							)["bytes"] if arr is not None else None for arr in storage.to_pylist()]						,	type=pa.binary()						,	)
         A__    =      pa.array([None] * len(lowercase_							)						,	type=pa.string()							)
         A__    =      pa.StructArray.from_arrays(
             [bytes_array, path_array]						,	["bytes", "path"]						,	mask=bytes_array.is_null()							)
     return array_cast(lowercase_						,	self.pa_type							)



 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:pa.StructArray							)->							pa.StructArray:

     @no_op_if_value_is_null
     def path_to_bytes(lowercase_				:int							):
         with xopen(lowercase_						,	"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(lowercase_							) 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(lowercase_						,	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()
        A__    =      list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
    return _IMAGE_COMPRESSION_FORMATS





def 						UpperCamelCase    (							_lowerCamelCase : "PIL.Image.Image"	):
    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    (							_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'."	)

    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    (							_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:
        A__,     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 PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

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

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

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

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

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

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     scheduler.set_timesteps(10							)
     assert torch.equal(
         scheduler.timesteps						,	torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

     for scheduler_class in self.scheduler_classes:
         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



