






"""simple docstring"""

import unittest

import numpy as np

from transformers import ElectraConfig, 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():
			from transformers.models.electra.modeling_flax_electra import (
			    FlaxElectraForCausalLM,
			    FlaxElectraForMaskedLM,
			    FlaxElectraForMultipleChoice,
			    FlaxElectraForPreTraining,
			    FlaxElectraForQuestionAnswering,
			    FlaxElectraForSequenceClassification,
			    FlaxElectraForTokenClassification,
			    FlaxElectraModel,
			)

class     A_				(   unittest.TestCase	):







				def __init__(				self				:    Tuple    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Tuple=1_3    ,     __lowerCamelCase				:    str=7    ,     __lowerCamelCase				:    Dict=True    ,     __lowerCamelCase				:    int=True    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    int=9_9    ,     __lowerCamelCase				:    Union[str, Any]=2_4    ,     __lowerCamelCase				:    Optional[Any]=3_2    ,     __lowerCamelCase				:    str=5    ,     __lowerCamelCase				:    Dict=4    ,     __lowerCamelCase				:    Dict=3_7    ,     __lowerCamelCase				:    str="gelu"    ,     __lowerCamelCase				:    Union[str, Any]=0.1    ,     __lowerCamelCase				:    List[str]=0.1    ,     __lowerCamelCase				:    Union[str, Any]=5_1_2    ,     __lowerCamelCase				:    Optional[int]=1_6    ,     __lowerCamelCase				:    str=2    ,     __lowerCamelCase				:    int=0.02    ,     __lowerCamelCase				:    Any=4    ,     )					->       Optional[Any]:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  seq_length
										__magic_name__      =  is_training
										__magic_name__      =  use_attention_mask
										__magic_name__      =  use_token_type_ids
										__magic_name__      =  use_labels
										__magic_name__      =  vocab_size
										__magic_name__      =  hidden_size
										__magic_name__      =  embedding_size
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  hidden_dropout_prob
										__magic_name__      =  attention_probs_dropout_prob
										__magic_name__      =  max_position_embeddings
										__magic_name__      =  type_vocab_size
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  initializer_range
										__magic_name__      =  num_choices







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[Any]:
										__magic_name__      =  ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size						)

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

										__magic_name__      =  None
										if self.use_token_type_ids:
																__magic_name__      =  ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size						)

										__magic_name__      =  ElectraConfig(
										    vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     embedding_size=self.embedding_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     initializer_range=self.initializer_range    ,     )

										return config, input_ids, token_type_ids, attention_mask







				def      _snake_case       (				self				:    Dict						)					->       str:
										__magic_name__      =  self.prepare_config_and_inputs()
										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  config_and_inputs
										__magic_name__      =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict





@require_flax
class     A_				(   snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					True

				UpperCAmelCase__										=					(
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)







				def      _snake_case       (				self				:    Tuple						)					->       int:
										__magic_name__      =  FlaxElectraModelTester(self						)







				@slow
				def      _snake_case       (				self				:    int						)					->       int:
										for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																						__magic_name__      =  model_class_name.from_pretrained("google/electra-small-generator"						)
																else:
																						__magic_name__      =  model_class_name.from_pretrained("google/electra-small-discriminator"						)
																__magic_name__      =  model(np.ones((1, 1)						)						)
																self.assertIsNotNone(__lowerCamelCase						)











"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



