

'''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      (						__lowerCAmelCase							):
				snake_case_           =   42







class lowerCamelCase      (						unittest.TestCase							):

				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				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
					snake_case							 =				CustomOutput(images=[PIL.Image.new('RGB',	(4, 4)   )]   )

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






				def 				_lowerCamelCase						(					self   )						->       List[str]:
					# test output reinitialization with a `dict` for compatibility with `accelerate`
					snake_case							 =				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
					snake_case							 =				CustomOutput({'images': [PIL.Image.new('RGB',	(4, 4)   )]}   )

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








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''mega'''

				def __init__(					self,	lowercase_=30522,	lowercase_=128,	lowercase_=4,	lowercase_=256,	lowercase_=16,	lowercase_=True,	lowercase_=64,	lowercase_=False,	lowercase_=-1,	lowercase_=None,	lowercase_=True,	lowercase_="scalenorm",	lowercase_=True,	lowercase_="silu",	lowercase_="softmax",	lowercase_=0.1,	lowercase_=0.1,	lowercase_=0.1,	lowercase_=False,	lowercase_=True,	lowercase_=256,	lowercase_=True,	lowercase_=0.1,	lowercase_=2048,	lowercase_=False,	lowercase_=2,	lowercase_=0.02,	lowercase_=0.2,	lowercase_=0.02,	lowercase_=1.0,	lowercase_=1,	lowercase_=0,	lowercase_=2,	lowercase_="rotary",	lowercase_=None,	lowercase_=True,	lowercase_=True,	**lowercase_,	)						->       List[Any]:
					super().__init__(pad_token_id=lowercase_,	bos_token_id=lowercase_,	eos_token_id=lowercase_,	**lowercase_   )

					snake_case							 =				vocab_size
					snake_case							 =				hidden_size
					snake_case							 =				num_hidden_layers
					snake_case							 =				activation
					snake_case							 =				attention_activation
					snake_case							 =				intermediate_size
					snake_case							 =				ema_projection_size
					snake_case							 =				bidirectional
					snake_case							 =				shared_representation_size
					snake_case							 =				use_chunking
					snake_case							 =				chunk_size
					snake_case							 =				truncation
					snake_case							 =				normalize_before_mega
					snake_case							 =				normalization_type
					snake_case							 =				norm_affine
					snake_case							 =				dropout_prob
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				attention_probs_dropout_prob
					snake_case							 =				use_feature_dropout
					snake_case							 =				use_normalized_ffn
					snake_case							 =				nffn_hidden_size
					snake_case							 =				normalize_before_ffn
					snake_case							 =				nffn_activation_dropout_prob
					snake_case							 =				max_positions
					snake_case							 =				add_token_type_embeddings
					snake_case							 =				type_vocab_size
					snake_case							 =				initializer_range
					snake_case							 =				ema_delta_alpha_range
					snake_case							 =				ema_beta_range
					snake_case							 =				ema_gamma_omega_range
					snake_case							 =				relative_positional_bias
					snake_case							 =				use_cache
					snake_case							 =				classifier_dropout
					snake_case							 =				add_lm_hidden_dense_layer
					snake_case							 =				1  # not used but required by Hugging Face







class lowerCamelCase      (						__lowerCAmelCase							):

				@property
				def 				_lowerCamelCase						(					self   )						->       Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						snake_case							 =				{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						snake_case							 =				{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ]   )





