


'''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 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					str=13	,				_lowerCamelCase				:					int=7	,				_lowerCamelCase				:					Dict=True	,				_lowerCamelCase				:					Dict=True	,				_lowerCamelCase				:					Union[str, Any]=True	,				_lowerCamelCase				:					str=True	,				_lowerCamelCase				:					List[str]=99	,				_lowerCamelCase				:					Optional[int]=24	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					List[str]=5	,				_lowerCamelCase				:					Dict=4	,				_lowerCamelCase				:					int=37	,				_lowerCamelCase				:					str="gelu"	,				_lowerCamelCase				:					Tuple=0.1	,				_lowerCamelCase				:					Optional[int]=0.1	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					Dict=16	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					str=0.02	,				_lowerCamelCase				:					List[Any]=4	,				)							->					str:
										__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 					__A					( self				:					str  )							->					List[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 					__A					( self				:					Any  )							->					List[Any]:
										__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 		UpperCamelCase_		(					lowerCAmelCase__   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       int         =     True

						UpperCAmelCase__	:       str         =     (
						    (
						        FlaxElectraModel,
						        FlaxElectraForCausalLM,
						        FlaxElectraForMaskedLM,
						        FlaxElectraForPreTraining,
						        FlaxElectraForTokenClassification,
						        FlaxElectraForQuestionAnswering,
						        FlaxElectraForMultipleChoice,
						        FlaxElectraForSequenceClassification,
						    )
						    if is_flax_available()
						    else ()
						)
						def 					__A					( self				:					Optional[int]  )							->					List[Any]:
										__magic_name__	  = FlaxElectraModelTester(self  )

						@slow
						def 					__A					( self				:					Dict  )							->					Optional[Any]:






										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'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



