






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			  =    {
    "configuration_prophetnet": ["PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ProphetNetConfig"],
    "tokenization_prophetnet": ["ProphetNetTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_			  =    [
        "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ProphetNetDecoder",
        "ProphetNetEncoder",
        "ProphetNetForCausalLM",
        "ProphetNetForConditionalGeneration",
        "ProphetNetModel",
        "ProphetNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
    from .tokenization_prophetnet import ProphetNetTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_prophetnet import (
            PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            ProphetNetDecoder,
            ProphetNetEncoder,
            ProphetNetForCausalLM,
            ProphetNetForConditionalGeneration,
            ProphetNetModel,
            ProphetNetPreTrainedModel,
        )

else:
    import sys

    A_			  =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""






import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput
@dataclass
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  __lowerCamelCase       :    Union[List[PIL.Image.Image], np.ndarray]




class 						__lowerCAmelCase   (			unittest.TestCase       ):







  '''simple docstring'''


  def 					UpperCamelCase__   (      self:		Tuple					):
     UpperCamelCase_     =CustomOutput(images=np.random.rand(1				,			3				,			4				,			4					)					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			np.ndarray					)
     assert outputs.images.shape == (1, 3, 4, 4)
     assert isinstance(outputs["images"]				,			np.ndarray					)
     assert outputs["images"].shape == (1, 3, 4, 4)
     assert isinstance(outputs[0]				,			np.ndarray					)
     assert outputs[0].shape == (1, 3, 4, 4)

     # test with a non-tensor attribute
     UpperCamelCase_     =CustomOutput(images=[PIL.Image.new("RGB"				,			(4, 4)					)]					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			UpperCamelCase_					)
     assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
     assert isinstance(outputs["images"]				,			UpperCamelCase_					)
     assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
     assert isinstance(outputs[0]				,			UpperCamelCase_					)
     assert isinstance(outputs[0][0]				,			PIL.Image.Image					)





  def 					UpperCamelCase__   (      self:		Optional[int]					):
     # test output reinitialization with a `dict` for compatibility with `accelerate`
     UpperCamelCase_     =CustomOutput({"images": np.random.rand(1				,			3				,			4				,			4					)}					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			np.ndarray					)
     assert outputs.images.shape == (1, 3, 4, 4)
     assert isinstance(outputs["images"]				,			np.ndarray					)
     assert outputs["images"].shape == (1, 3, 4, 4)
     assert isinstance(outputs[0]				,			np.ndarray					)
     assert outputs[0].shape == (1, 3, 4, 4)

     # test with a non-tensor attribute
     UpperCamelCase_     =CustomOutput({"images": [PIL.Image.new("RGB"				,			(4, 4)					)]}					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			UpperCamelCase_					)
     assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
     assert isinstance(outputs["images"]				,			UpperCamelCase_					)
     assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
     assert isinstance(outputs[0]				,			UpperCamelCase_					)
     assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
