


'''simple docstring'''

import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.distilbert.modeling_flax_distilbert import (
	    FlaxDistilBertForMaskedLM,
	    FlaxDistilBertForMultipleChoice,
	    FlaxDistilBertForQuestionAnswering,
	    FlaxDistilBertForSequenceClassification,
	    FlaxDistilBertForTokenClassification,
	    FlaxDistilBertModel,
	)


class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase=13       ,			__lowercase=7       ,			__lowercase=True       ,			__lowercase=True       ,			__lowercase=True       ,			__lowercase=True       ,			__lowercase=99       ,			__lowercase=32       ,			__lowercase=5       ,			__lowercase=4       ,			__lowercase=37       ,			__lowercase="gelu"       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=5_12       ,			__lowercase=16       ,			__lowercase=2       ,			__lowercase=0.0_2       ,			__lowercase=4       ,			):
										__lowerCAmelCase							       =				parent
										__lowerCAmelCase							       =				batch_size
										__lowerCAmelCase							       =				seq_length
										__lowerCAmelCase							       =				is_training
										__lowerCAmelCase							       =				use_attention_mask
										__lowerCAmelCase							       =				use_token_type_ids
										__lowerCAmelCase							       =				use_labels
										__lowerCAmelCase							       =				vocab_size
										__lowerCAmelCase							       =				hidden_size
										__lowerCAmelCase							       =				num_hidden_layers
										__lowerCAmelCase							       =				num_attention_heads
										__lowerCAmelCase							       =				intermediate_size
										__lowerCAmelCase							       =				hidden_act
										__lowerCAmelCase							       =				hidden_dropout_prob
										__lowerCAmelCase							       =				attention_probs_dropout_prob
										__lowerCAmelCase							       =				max_position_embeddings
										__lowerCAmelCase							       =				type_vocab_size
										__lowerCAmelCase							       =				type_sequence_label_size
										__lowerCAmelCase							       =				initializer_range
										__lowerCAmelCase							       =				num_choices




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size		)

										__lowerCAmelCase							       =				None
										if self.use_attention_mask:
																__lowerCAmelCase							       =				random_attention_mask([self.batch_size, self.seq_length]		)

										__lowerCAmelCase							       =				DistilBertConfig(
										    vocab_size=self.vocab_size       ,			dim=self.hidden_size       ,			n_layers=self.num_hidden_layers       ,			n_heads=self.num_attention_heads       ,			hidden_dim=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			dropout=self.hidden_dropout_prob       ,			attention_dropout=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			initializer_range=self.initializer_range       ,			tie_weights_=__lowercase       ,			)

										return config, input_ids, attention_mask




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.prepare_config_and_inputs()
										__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				config_and_inputs
										__lowerCAmelCase							       =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict


@require_flax
class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Union[str, Any]	=	(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				FlaxDistilBertModelTester(self		)




				@slow
				def 					_snake_case  (self		):
										for model_class_name in self.all_model_classes:
																__lowerCAmelCase							       =				model_class_name.from_pretrained('''distilbert-base-uncased'''		)
																__lowerCAmelCase							       =				model(np.ones((1, 1)		)		)
																self.assertIsNotNone(__lowercase		)


@require_flax
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
										__lowerCAmelCase							       =				np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]		)
										__lowerCAmelCase							       =				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
										__lowerCAmelCase							       =				model(__lowercase       ,			attention_mask=__lowercase		)[0]
										__lowerCAmelCase							       =				(1, 11, 7_68)
										self.assertEqual(output.shape       ,			__lowercase		)
										__lowerCAmelCase							       =				np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]		)

										self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,			__lowercase       ,			atol=1e-4		)		)





'''simple docstring'''

from PIL import Image

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):

						def brightness(lowerCamelCase) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -2_55.0 <= level <= 2_55.0:
												raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''')
						return img.point(lowerCamelCase)


if __name__ == "__main__":
	# Load image
	with Image.open("""image_data/lena.jpg""") as img:
		# Change brightness to 100
		_UpperCAmelCase				: str							    =				change_brightness(img, 1_0_0)
		brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

