




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 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






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







class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	str        =							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
								lowercase__  :	Optional[int]        =							CustomOutput(images=[PIL.Image.new("RGB"	,				(4, 4)    )]    )

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







	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							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
								lowercase__  :	Optional[Any]        =							CustomOutput({"images": [PIL.Image.new("RGB"	,				(4, 4)    )]}    )

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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(pad_token_id=lowerCamelCase	,				**lowerCamelCase    )

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	@property
	def    __a     (					self    ) ->      Mapping[str, Mapping[int, str]]:





								"""simple docstring"""


								if self.task == "multiple-choice":
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]    )